AI BASED STROKE DISEASES PREDICTION

 Since stroke disease often causes death or serious disability, active primary prevention and early detection of prognostic symptoms are very important. Stroke diseases can be divided into ischemic stroke and hemorrhagic stroke, and they should be minimized by emergency treatment such as thrombolytic or coagulant administration by type. First, it is...

ROAD LANE DETECTION USING MACHINE LEARNING

 Road lane detection systems play a crucial role in the context of Advanced Driver Assistance Systems (ADASs) and autonomous driving. Such systems can lessen road accidents and increase driving safety by alerting the driver in risky traffic situations. Additionally, the detection of ego lanes with their left and right boundaries along with the recog...

STRESS DETECTION USING GALVANIC SKIN RESPONSE: AN ANDROID APPLICATION

 Stress is one of the factors that affect human health in many aspects. It is considered as one of the culprits in increasing the risk of getting sick that could probably lead to critical physical or mental illnesses. Stress can be experienced everywhere and in different circumstances. Hence, stress should be controlled and managed by monitoring its...

SENTIMENTAL ANALYSIS USING A CODE-MIX DATASET OF TAMIL-ENGLISH

 This paper describes the development of a multilingual, manually annotated dataset for three under-resourced Dravidian languages generated from social media comments. The dataset was annotated for sentiment analysis and offensive language identification for a total of more than 60,000 YouTube comments. The dataset consists of around 44,000 comments...

PCOS DETECTION AND PREDICTION USING CNN MACHINE LEARNING ALGORITHMS

 Polycystic Ovary Syndrome (PCOS) is a medical condition which causes hormonal disorder in women in their childbearing years. The hormonal imbalance leads to a delayed or even absent menstrual cycle. Women with PCOS majorly suffer from excessive weight gain, facial hair growth, acne, hair loss, skin darkening and irregular periods leading to inferti...

ONLINE PLANT SALES RECOMMENDER SYSTEM USING MACHINE LEARNING

 In today's technical era, every startup or a company attempt to establish a better sort of communication between their Plants and the users, and for that purpose, they require a type of mechanism which can promote their plant effectively, and here the recommender system serves this motive. It is basically a filtering system that tries to predict an...

MACHINE LEARNING-BASED AUTOMATIC CLASSIFICATION OF KNEE OSTEOARTHRITIS SEVERITY USING GAIT DATA AND RADIOGRAPHIC IMAGES

 Knee osteoarthritis (KOA) is a leading cause of disability among elderly adults, and it causes pain and discomfort and limits the functional independence of such adults. The aim of this study was the development of an automated classification model for KOA, based on the Kellgren–Lawrence (KL) grading system, using radiographic imaging and gait anal...

Fabric defects detection using convolution neural network and multispectral imaging

 Manual inspection of textiles is a long, tedious, and costly method. Technology has solved this problem by developing automatic systems for textile inspection. However, Jacquard fabrics present a challenge because patterns can be complex and seemingly random to systems. Only a few in-depth studies have been conducted on jacquard fabrics despite the...

EAD-NET A NOVEL LESION SEGMENTATION METHOD IN DIABETIC RETINOPATHY USING NEURAL NETWORKS

 Diabetic retinopathy (DR) is a common chronic fundus disease, which has four different kinds of microvessel structure and microvascular lesions: microaneurysms (MAs), hemorrhages (HEs), hard exudates, and soft exudates. Accurate detection and counting of them are a basic but important work. The manual annotation of these lesions is a labor-intensiv...

ABANDONED OBJECT DETECTION VIA TEMPORAL CONSISTENCY MODELING AND BACK TRACING VERIFICATION FOR VISUAL SURVEILLANCE THROUGH IOT

 This paper presents an effective approach for detecting abandoned luggage in surveillance videos. We combine short- and long-term background models to extract foreground objects, where each pixel in an input image is classified as a 2-bit code. Subsequently, we introduce a framework to identify static foreground regions based on the temporal transi...

EPILEPTIC SEIZURES PREDICTION USING IOT AND MACHINE LEARNING

 Epilepsy is a type of neurological disorder that causes abnormal brain activities and creates epileptic seizures.Traditionally epileptic seizure prediction is realized with a visual examination of Electroencephalogram (EEG) signals. But this technique needs a long time EEG monitoring. So, the automatic epileptic seizures prediction schemes become a...

CREATING DESKTOP SPEECH RECOGNISATION USING PYTHON PROGRAMMING

 As a personal assistant, this project is built with AI technologies, Artificial intelligence technologies are beginning to be actively used in human life, this is facilitated by the appearance and wide dissemination of the Internet of Things (IOT). Autonomous devices are becoming smarter in their way to interact with both a human and themselves. Th...

Feed forward-Cutset-Free Pipelined Multiply-Accumulate Unit for the Machine Learning Accelerator

 Multiply–accumulate (MAC) computations account for a large part of machine learning accelerator operations. The pipelined structure is usually adopted to improve the performance by reducing the length of critical paths. An increase in the number of flip-flops due to pipelining, however, generally results in significant area and power increase. A la...

WEAKLY SUPERVISED DEEP LEARNING FOR CUSTOMER REVIEW SENTIMENT ANALYSIS

 In recent times the concept of smart cities have gained grate popularity. The ever increasing population has led to chaotic city traffic. As a result of the process of searching a parking lot becomes tedious. It is time consuming task leading to discomfort. The fuel consumption is on an increasing side due to such scenarios. The increase in vehicul...

VEGETABLE DISEASES DETECTION USING CNN SEQUENTIAL NEURAL NETWORKS

 India is the cultivating country and our country is the biggest maker in agricultural products. So, we have to classify and exchange our agricultural products. Manual arranging is tedious and it requires works. The automatic grading system requires less time for grading of the agricultural products. Image processing technique is helpful in examinat...

Vegetable disease detection using k-means clustering and svm

  India is the cultivating country and our country is the biggest maker in agricultural products. So, we have to classify and exchange our agricultural products. Manual arranging is tedious and it requires works. The automatic grading system requires less time for grading of the agricultural products. Image processing technique is helpful in exam...

Traffic Prediction for Intelligent Transportation System using Machine Learning

  This paper aims to develop a tool for predicting accurate and timely traffic flow Information. Traffic Environment involves everything that can affect the traffic flowing on the road, whether it’s traffic signals, accidents, rallies, even repairing of roads that can cause a jam. If we have prior information which is very near approximate about a...

Traffic forcast prediction using android application

 We consider the problem of forecasting high frequency sampled mobile cellular traffic starting from a lower frequency sampled time series. We use a dataset of real downlink/uplink traffic traces obtained from a mobile cellular network and apply different methodologies for performing forecasts at different sampling frequencies. Through extensive eva...

Towards End-to-End Lane Detection: an Instance Segmentation approach

 Modern cars are incorporating an increasing number of driver assist features, among which automatic lane keeping. The latter allows the car to properly position itself within the road lanes, which is also crucial for any subsequent lane departure or trajectory planning decision in fully autonomous cars. Traditional lane detection methods rely on a ...

Seizure detection using wearable sensors and machine learning: Setting a benchmark

 Tracking seizures is crucial for epilepsy monitoring and treatment evaluation. Current epilepsy care relies on caretaker seizure diaries, but clinical seizure monitoring may miss seizures. Wearable devices may be better tolerated and more suitable for long-term ambulatory monitoring. This study evaluates the seizure detection performance of custom-...

Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensor flow and Comparison of Performance for Various Hidden Layers

  In recent times, with the increase of Artificial Neural Network (ANN), deep learning has brought a dramatic twist in the field of machine learning by making it more artificially intelligent. Deep learning is remarkably used in vast ranges of fields because of its diverse range of applications such as surveillance, health, medicine, sports, robo...

ONLINE E-COMMERCE PRODUCT RECOMMENDER SYSTEM

 Internet is speeding up and modifying the manner in which daily tasks such as online shopping, paying utility bills, watching new movies, communicating, etc., are accomplished. As an example, in older shopping methods, products were mass produced for a single market and audience but that approach is no longer viable. Markets based on long product a...

Object Detection System with Voice Output using Python

 As object recognition technology has developed recently, various technologies have been applied to autonomous vehicles, robots, and industrial facilities. However, the benefits of these technologies are not reaching the visually impaired, who need it the most. In this paper, we proposed an object detection system for the blind using deep learning t...

Non-invasive prediction of hemoglobin level using machine learning techniques with the PPG signals characteristics features

 Hemoglobin can be measured normally after the analysis of the blood sample taken from the body and this measurement is named as invasive. Hemoglobin must continuously be measured to control the disease and its progression in people who go through hemodialysis and have diseases such as oligocythemia and anemia. This gives a perpetual feeling of pain...

MEDICAL IMAGE ENHANCEMENT USING DEEP LEARNING, NEUTRAL NETWORKS, AUTO ENCODERS

 One common interest in radiography is producing radiographs with as low as possible radiation exposures to patients. In clinical practices, radiation exposure factors are preset for optimal image qualities to avoid underexposures which will lead to repeating examinations hence increasing radiation exposures to patients. Underexposed radiographs mai...

EEG Signal Based Seizures Detection using IOT

 Epilepsy is a chronic neurological disorder with several different types of seizures, some of them characterized by involuntary recurrent convulsions, which have a great impact on the everyday life of the patients. Several solutions have been proposed in the literature to detect this type of seizures and to monitor the patient; however, these appro...

EEG based Epileptic Seizures Detection using Intrinsic Time-Scale Decomposition

 Epilepsy is a type of neurological disorder that causes abnormal brain activities and creates epileptic seizures. Traditionally epileptic seizure prediction is realized with a visual examination of Electroencephalogram (EEG) signals. But this technique needs a long time EEG monitoring. So, the automatic epileptic seizures prediction schemes become ...

EARLY DETECTION OF MALPOSITIONED CATHETERS AND LINES ON CHEST X-RAYS USING DEEP LEARNING

 Hospital patients can have catheters and lines inserted during the course of their admission and serious complications can arise if they are positioned incorrectly. Early recognition of malpositioned tubes is the key to preventing risky complications (even death), even more so now that millions of COVID-19 patients are in the need of these tubes an...

PPSF A Privacy-Preserving and Secure Framework using Blockchain-based Machine-Learning for IoT-driven Smart Cities

 With the evolution of the Internet of Things (IoT), smart cities have become the mainstream of urbanization. IoT networks allow distributed smart devices to collect and process data within smart city infrastructure using an open channel, the Internet. Thus, challenges such as centralization, security, privacy (e.g., performing data poisoning and in...

Neuroscience and Network Dynamics Toward Brain-Inspired Intelligence

 This article surveys the interdisciplinary research of neuroscience, network science, and dynamic systems, with emphasis on the emergence of brain-inspired intelligence. To replicate brain intelligence, a practical way is to reconstruct cortical networks with dynamic activities that nourish the brain functions, instead of using only artificial comp...

Locally Weighted Principal Component Analysis-Based Multimode Modeling for Complex Distributed Parameter Systems

 Global principal component analysis (PCA) has been successfully introduced for modeling distributed parameter systems (DPSs). In spite of the merits, this method is not feasible due to parameter variations and multiple operating domains. A novel multimode spatiotemporal modeling method based on the locally weighted PCA (LW-PCA) method is developed ...

Integrating Resonant Recognition Model and Stockwell Transform for Localization of Hotspots in Tubulin

 Tubulin is a promising target for designing anti-cancer drugs. Identification of hotspots in multifunctional Tubulin protein provides insights for new drug discovery. Although machine learning techniques have shown significant results in prediction, they fail to identify the hotspots corresponding to a particular biological function. This paper pre...

A Multimodal Data Processing System for LiDAR-Based Human Activity Recognition

 Increasingly, the task of detecting and recognizing the actions of a human has been delegated to some form of neural network processing camera or wearable sensor data. Due to the degree to which the camera can be affected by lighting and wearable sensors scantiness, neither one modality can capture the required data to perform the task confidently....

When Does Diversity Help Generalization in Classification Ensembles

 Ensembles, as a widely used and effective technique in the machine learning community, succeed within a key element--``diversity.'' The relationship between diversity and generalization, unfortunately, is not entirely understood and remains an open research issue. To reveal the effect of diversity on the generalization of classification ensembles, ...

Saliency-Based Multilabel Linear Discriminant Analysis

 Linear discriminant analysis (LDA) is a classical statistical machine-learning method, which aims to find a linear data transformation increasing class discrimination in an optimal discriminant subspace. Traditional LDA sets assumptions related to the Gaussian class distributions and single-label data annotations. In this article, we propose a new ...

Progressive Self-Supervised Clustering With Novel Category Discovery

 These days, clustering is one of the most classical themes to analyze data structures in machine learning and pattern recognition. Recently, the anchor-based graph has been widely adopted to promote the clustering accuracy of plentiful graph-based clustering techniques. In order to achieve more satisfying clustering performance, we propose a novel ...

Multiple Flat Projections for Cross-Manifold Clustering

 Cross-manifold clustering is an extreme challenge learning problem. Since the low-density hypothesis is not satisfied in cross-manifold problems, many traditional clustering methods failed to discover the cross-manifold structures. In this article, we propose multiple flat projections clustering (MFPC) for cross-manifold clustering. In our MFPC, th...

Feature Selection Based on a Sparse Neural-Network Layer With Normalizing Constraints

 Feature selection (FS) is an important step in machine learning since it has been shown to improve prediction accuracy while suppressing the curse of dimensionality of high-dimensional data. Neural networks have experienced tremendous success in solving many nonlinear learning problems. Here, we propose a new neural-network-based FS approach that i...

A Data-Driven Modeling Method for Stochastic Nonlinear Degradation Process With Application to RUL Estimation

 This article proposes a novel modeling method for the stochastic nonlinear degradation process by using the relevance vector machine (RVM), which can describe the nonlinearity of degradation process more flexibly and accurately. Compared with the existing methods, where degradation processes are modeled as the Wiener process with a nonlinear drift ...

Transductive Multiview Modeling With Interpretable Rules, Matrix Factorization, and Cooperative Learning

 Multiview fuzzy systems aim to deal with fuzzy modeling in multiview scenarios effectively and to obtain the interpretable model through multiview learning. However, current studies of multiview fuzzy systems still face several challenges, one of which is how to achieve efficient collaboration between multiple views when there are few labeled data....

Precedence-Constrained Colored Traveling Salesman Problem An Augmented Variable Neighborhood Search Approach

 A traveling salesman problem (CTSP) as a generalization of the well-known multiple traveling salesman problem utilizes colors to distinguish the accessibility of individual cities to salesmen. This work formulates a precedence-constrained CTSP (PCTSP) over hypergraphs with asymmetric city distances. It is capable of modeling the problems with opera...

L1 Sparsity-Regularized Attention Multiple-Instance Network for Hyperspectral Target Detection

 Attention-based deep multiple-instance learning (MIL) has been applied to many machine-learning tasks with imprecise training labels. It is also appealing in hyperspectral target detection, which only requires the label of an area containing some targets, relaxing the effort of labeling the individual pixel in the scene. This article proposes an L1...

Joint Sparse Locality-Aware Regression for Robust Discriminative Learning

 With the dramatic increase of dimensions in the data representation, extracting latent low-dimensional features becomes of the utmost importance for efficient classification. Aiming at the problems of weakly discriminating marginal representation and difficulty in revealing the data manifold structure in most of the existing linear discriminant met...

Cold-Start Active Sampling via ?-Tube

 Active learning (AL) improves the generalization performance for the current classification hypothesis by querying labels from a pool of unlabeled data. The sampling process is typically assessed by an informative, representative, or diverse evaluation policy. However, the policy, which needs an initial labeled set to start, may degenerate its perf...

Automated Design of Multipass Heuristics for Resource-Constrained Job Scheduling With Self-Competitive Genetic Programming

 Resource constraint job scheduling is an important combinatorial optimization problem with many practical applications. This problem aims at determining a schedule for executing jobs on machines satisfying several constraints (e.g., precedence and resource constraints) given a shared central resource while minimizing the tardiness of the jobs. Due ...

Transfer Relation Network for Fault Diagnosis of Rotating Machinery With Small Data

 Many deep-learning methods have been developed for fault diagnosis. However, due to the difficulty of collecting and labeling machine fault data, the datasets in some practical applications are relatively much smaller than the other big data benchmarks. In addition, the fault data come from different machines. Therefore, on some occasions, fault di...

Hyperspectral Image Classification via Discriminant Gabor Ensemble Filter

 For a broad range of applications, hyperspectral image (HSI) classification is a hot topic in remote sensing, and convolutional neural network (CNN)-based methods are drawing increasing attention. However, to train millions of parameters in CNN requires a large number of labeled training samples, which are difficult to collect. A conventional Gabor...

Emotional Semantics-Preserved and Feature-Aligned Cycle GAN for Visual Emotion Adaptation

 Thanks to large-scale labeled training data, deep neural networks (DNNs) have obtained remarkable success in many vision and multimedia tasks. However, because of the presence of domain shift, the learned knowledge of the well-trained DNNs cannot be well generalized to new domains or datasets that have few labels. Unsupervised domain adaptation (UD...

Deep Ladder-Suppression Network for Unsupervised Domain Adaptation

 Unsupervised domain adaptation (UDA) aims at learning a classifier for an unlabeled target domain by transferring knowledge from a labeled source domain with a related but different distribution. Most existing approaches learn domain-invariant features by adapting the entire information of the images. However, forcing adaptation of domain-specific ...

Bridging the Micro and Macro Calibration of Agent-Based Model Using Mean-Field Dynamics

 Calibration of agent-based models (ABM) is an essential stage when they are applied to reproduce the actual behaviors of distributed systems. Unlike traditional methods that suffer from the repeated trial and error and slow convergence of iteration, this article proposes a new ABM calibration approach by establishing a link between agent microbehav...

Active Domain Adaptation With Application to Intelligent Logging Lithology Identification

 Lithology identification plays an essential role in formation characterization and reservoir exploration. As an emerging technology, intelligent logging lithology identification has received great attention recently, which aims to infer the lithology type through the well-logging curves using machine-learning methods. However, the model trained on ...

Weak Estimator-Based Stochastic Searching on the Line in Dynamic Dual Environments

 Stochastic point location deals with the problem of finding a target point on a real line through a learning mechanism (LM) with the stochastic environment (SE) offering directional information. The SE can be further categorized into an informative or deceptive one, according to whether p is above 0.5 or not, where p is the probability of providing...

Small Low-Contrast Target Detection Data-Driven Spatiotemporal Feature Fusion and Implementation

 Detecting small low-contrast targets in the airspace is an essential and challenging task. This article proposes a simple and effective data-driven support vector machine (SVM)-based spatiotemporal feature fusion detection method for small low-contrast targets. We design a novel pixel-level feature, called a spatiotemporal profile, to depict the di...

Resetting Weight Vectors in MOEAD for Multi objective Optimization Problems With Discontinuous Pareto Front

 When a multiobjective evolutionary algorithm based on decomposition (MOEA/D) is applied to solve problems with discontinuous Pareto front (PF), a set of evenly distributed weight vectors may lead to many solutions assembling in boundaries of the discontinuous PF. To overcome this limitation, this article proposes a mechanism of resetting weight vec...

Research Review for Broad Learning System Algorithms, Theory, and Applications

 In recent years, the appearance of the broad learning system (BLS) is poised to revolutionize conventional artificial intelligence methods. It represents a step toward building more efficient and effective machine-learning methods that can be extended to a broader range of necessary research fields. In this survey, we provide a comprehensive overvi...

Multiview Graph Restricted Boltzmann Machines

 Recently, the restricted Boltzmann machine (RBM) has aroused considerable interest in the multiview learning field. Although effectiveness is observed, like many existing multiview learning models, multiview RBM ignores the local manifold structure of multiview data. In this article, we first propose a novel graph RBM model, which preserves the dat...

Cross-Lingual Knowledge Transferring by Structural Correspondence and Space Transfer

 The cross-lingual sentiment analysis (CLSA) aims to leverage label-rich resources in the source language to improve the models of a resource-scarce domain in the target language, where monolingual approaches based on machine learning usually suffer from the unavailability of sentiment knowledge. Recently, the transfer learning paradigm that can tra...

A Drift Region-Based Data Sample Filtering Method

 Concept drift refers to changes in the underlying data distribution of data streams over time. A well-trained model will be outdated if concept drift occurs. Once concept drift is detected, it is necessary to understand where the drift occurs to support the drift adaptation strategy and effectively update the outdated models. This process, called d...

Type-2 Fuzzy Broad Learning System

 The broad learning system (BLS) has been identified as an important research topic in machine learning. However, the typical BLS suffers from poor robustness for uncertainties because of its characteristic of the deterministic representation. To overcome this problem, a type-2 fuzzy BLS (FBLS) is designed and analyzed in this article. First, a grou...

Robust Rank-Constrained Sparse Learning A Graph-Based Framework for Single View and Multiview Clustering

 Graph-based clustering aims to partition the data according to a similarity graph, which has shown impressive performance on various kinds of tasks. The quality of similarity graph largely determines the clustering results, but it is difficult to produce a high-quality one, especially when data contain noises and outliers. To solve this problem, we...

Performance Improvement of a Parsimonious Learning Machine Using Metaheuristic Approaches

 Autonomous learning algorithms operate in an online fashion in dealing with data stream mining, where minimum computational complexity is a desirable feature. For such applications, parsimonious learning machines (PALMs) are suitable candidates due to their structural simplicity. However, these parsimonious algorithms depend upon predefined thresho...

Federated Continuous Learning With Broad Network Architecture

 Federated learning (FL) is a machine-learning setting, where multiple clients collaboratively train a model under the coordination of a central server. The clients' raw data are locally stored, and each client only uploads the trained weight to the server, which can mitigate the privacy risks from the centralized machine learning. However, most of ...

Composite Kernel of Mutual Learning on Mid-Level Features for Hyperspectral Image Classification

 By training different models and averaging their predictions, the performance of the machine-learning algorithm can be improved. The performance optimization of multiple models is supposed to generalize further data well. This requires the knowledge transfer of generalization information between models. In this article, a multiple kernel mutual lea...

A Systematic Deep Learning Model Selection for P300-Based Brain-Computer Interfaces

 Predicting attention-modulated brain responses is a major area of investigation in brain-computer interface (BCI) research that aims to translate neural activities into useful control and communication commands. Such studies involve collecting electroencephalographic (EEG) data from subjects to train classifiers for decoding users' mental states. H...

Semi supervised Affinity Matrix Learning via Dual-Channel Information Recovery

 This article explores the problem of semisupervised affinity matrix learning, that is, learning an affinity matrix of data samples under the supervision of a small number of pairwise constraints (PCs). By observing that both the matrix encoding PCs, called pairwise constraint matrix (PCM) and the empirically constructed affinity matrix (EAM), expre...

Predictive Analytics for Default of Credit Card Clients

 Predictive analytics has a significant potential to support different decision processes. We aimed to compare various machine learning algorithms for the selected task, which predicts credit card clients' default based on the free available data. We chose Random Forest, AdaBoost, XGBoost, and Gradient Boosting algorithm and applied them to a prepar...

Neighborhood Preserving and Weighted Subspace Learning Method for Drift Compensation in Gas Sensor

 This article presents a novel discriminative subspace-learning-based unsupervised domain adaptation (DA) method for the gas sensor drift problem. Many existing subspace learning approaches assume that the gas sensor data follow a certain distribution such as Gaussian, which often does not exist in real-world applications. In this article, we addres...

Deep-LIFT Deep Label-Specific Feature Learning for Image Annotation

 Image annotation aims to jointly predict multiple tags for an image. Although significant progress has been achieved, existing approaches usually overlook aligning specific labels and their corresponding regions due to the weak supervised information (i.e., ``bag of labels'' for regions), thus failing to explicitly exploit the discrimination from d...

Deep-Learning-Based Automatic Selection of Fewest Channels for Brain-Machine Interfaces

 Due to the development of convenient brain-machine interfaces (BMIs), the automatic selection of a minimum channel (electrode) set has attracted increasing interest because the decrease in the number of channels increases the efficiency of BMIs. This study proposes a deep-learning-based technique to automatically search for the minimum number of ch...

An Input Weights Dependent Complex-Valued Learning Algorithm Based on Wirtinger Calculus

 Complex-valued neural network is a kind of learning model which can deal with problems in complex domain. Fully complex extreme learning machine (CELM) is a much faster training algorithm than the complex backpropagation (CBP) scheme. However, it is at the cost of using more hidden nodes to obtain the comparable performance. An upper-layer-solution...

Machine Learning for On-the-Fly Reliability-Aware Cell Library Characterization

  Aging-induced degradation imposes a major challenge to the designer when estimating timing guardbands. This problem increases as traditional worst-case corners bring over-pessimism to designers, exacerbating competitive and close-to-the-edge designs. In this work, we present an accurate machine learning approach for aging-aware cell library cha...

Asynchronous Parallel, Sparse Approximated SVRG for High-Dimensional Machine Learning

 With increasing of data size and development of multi-core computers, asynchronous parallel stochastic optimization algorithms such as KroMagnon have gained significant attention. In this paper, we propose a new Sparse approximation and asynchronous parallel Stochastic Variance Reduced Gradient (SSVRG) method for sparse and high-dimensional machine...

MLDEG A Machine Learning Approach to Identify Differentially Expressed Genes Using Network Property and Network Propagation

 Motivation: Identifying differentially expressed genes (DEGs) in transcriptome data is a very important task. However, performances of existing DEG methods vary significantly for data sets measured in different conditions and no single statistical or machine learning model for DEG detection perform consistently well for data sets of different trait...

An Adaptive Machine Learning Framework for Behind-the-Meter LoadPV Disaggregation

 A significant amount of distributed photovoltaic (PV) generation is invisible to distribution system operators since it is behind the meter on customer premises and not directly monitored by the utility. The generation essentially adds an unknown varying negative demand to the system, which causes additional uncertainty in determining the total loa...

Adaptive Knowledge Transfer by Continual Weighted Updating of Filter Kernels for Few-shot Fault Diagnosis of Machines

 Deep learning (DL) based diagnosis models have to be trained by large quantities of monitoring data of machines. However, in real-case scenarios, machines operate under the normal condition in most of their life time while faults seldom happen. Therefore, though massive data are accessible, most are data of the normal condition while fault data are...

Online Machine Learning for Energy-Aware Multicore Real-Time Embedded Systems

 In this paper, we present an Online Learning Artificial Neural Network (ANN) model that is able to predict the performance of tasks in lower frequency levels and safely optimize real-time embedded systems' power saving operations. The proposed ANN model is supported by feature selection, which provides the most relevant variables to describe shared...

Toward Automated Classroom Observation Multimodal Machine Learning to Estimate CLASS Positive Climate and Negative Climate

 In this work we present a multi-modal machine learning-based system, which we call ACORN, to analyze videos of school classrooms for the Positive Climate (PC) and Negative Climate (NC) dimensions of the CLASS [1] observation protocol that is widely used in educational research. ACORN uses convolutional neural networks to analyze spectral audio feat...

Online Machine Learning for Energy-Aware Multicore Real-Time Embedded Systems

 In this paper, we present an Online Learning Artificial Neural Network (ANN) model that is able to predict the performance of tasks in lower frequency levels and safely optimize real-time embedded systems' power saving operations. The proposed ANN model is supported by feature selection, which provides the most relevant variables to describe shared...

Novel L1 Regularized Extreme Learning Machine for Soft-sensing of an Industrial Process

 Extreme learning machine (ELM) is suitable for nonlinear soft sensor development. Yet it faces an over-fitting problem. To overcome it, this work integrates bound optimization theory with Variational Bayesian (VB) inference to derive novel L1 norm-based ELMs. An L1 term is attached to the squared sum cost of prediction errors to formulate an object...

Machine Learning-Enabled Joint Antenna Selection and Precoding Design From Offline Complexity to Online Performance

 We investigate the performance of multi-user multiple-antenna downlink systems in which a base station (BS) serves multiple users via a shared wireless medium. In order to fully exploit the spatial diversity while minimizing the passive energy consumed by radio frequency (RF) components, the BS is equipped with M RF chains and N antennas, where M <...

WiDE WiFi Distance based Group Profiling Via Machine Learning

 We develop WiDE, a WiFi-distance estimation based group profiling system using LightGBM. Given the uploaded WiFi information by users, WiDE can automatically learn powerful hidden features from the proposed features for between-person distance estimation, and infer group membership with the estimated distance. For each group, WiDE classifies the mo...

Health Index Prediction of Overhead Transmission Lines A Machine Learning Approach

 This paper presents an asset health index (HI) prediction methodology for high voltage transmission overhead lines (OHLs) using supervised machine learning and structured, unambiguous visual inspections. We propose a framework for asset HI predictions to determine the technical condition of individual OHL towers to improve grid reliability in a cos...

Analysis of Single Event Transients (SET) using Machine Learning (ML) and Ionizing Radiation Effects Spectroscopy (IRES)

 A methodology for automating the identification of single-event transients (SETs) through Ionizing Radiation Effects Spectroscopy (IRES) and machine learning (ML) is provided. IRES enhances the identification of SETs through statistical analysis of waveform behavior, allowing for the capture of subtle circuit dynamics changes.Automated identificati...

A Machine Learning-Assisted Model for GaN Ohmic Contacts Regarding the Fabrication Processes

 Gallium nitride (GaN) devices have been successfully commercialized due to their superior performance, especially their high-power transformation efficiency. To further reduce the power consumption of these devices, the optimization for the ohmic contacts is attracting more and more attention. In the light of the mature and powerful machine learnin...

A Cyber-Physical Anomaly Detection for Wide-Area Protection using Machine Learning

 Wide-area protection scheme (WAPS) provides system-wide protection by detecting and mitigating small and large-scale disturbances that are difficult to resolve using local protection schemes. As this protection scheme is evolving from a substation-based distributed remedial action scheme (DRAS) to the control center-based centralized RAS (CRAS), it...

Perspectives on Machine Learning-assisted Plasma Medicine Towards Automated Plasma Treatment

 Cold atmospheric plasmas (CAPs) have shown great promise for medical applications through their synergistic chemical, electrical, and thermal effects, which can induce therapeutic outcomes. However, safe and reproducible plasma treatment of complex biological surfaces poses a major hurdle to the widespread adoption of CAPs for medical applications....

Machine-Learning Beam Tracking and Weight Optimization for mmWave Multi-UAV Links

  Millimeter-wave (mmWave) hybrid analog-digital beamforming is a promising approach to satisfy the low-latency constraint in multiple unmanned aerial vehicles (UAVs) systems, which serve as network infrastructure for flexible deployment. However, in highly dynamic multi-UAV environments, analog beam tracking becomes a critical challenge. The ove...

IPvest Clustering the IP traffic of network entities hidden behind a single IP address using machine learning

 IP Networks serve a variety of connected network entities (NEs) such as personal computers, servers, mobile devices, virtual machines, hosted containers, etc. The growth in the number of NEs and technical considerations has led to a reality where a single IP address is used by multiple NEs. A typical example is a home router using Network Address T...

Applying a random projection algorithm to optimize machine learning model for breast lesion classification

 Machine learning is widely used in developing computer-aided diagnosis (CAD) schemes of medical images. However, CAD usually computes large number of image features from the targeted regions, which creates a challenge of how to identify a small and optimal feature vector to build robust machine learning models. In this study, we investigate feasibi...

A novel recursive gene selection method based on least square kernel extreme learning machine

 This paper presents a recursive feature elimination (RFE) mechanism to select the most informative genes with a least square kernel extreme learning machine (LSKELM) classifier.Describing the generalization ability of LSKELM in a way that is related to small norm of weights, we proposed a ranking criterion to evaluate the importance of genes by the...

A Machine Learning Resource Allocation Solution to Improve Video Quality in Remote Education

 The current global pandemic crisis has unquestionably disrupted the higher education sector, forcing educational institutions to rapidly embrace technology-enhanced learning. However, the COVID-19 containment measures that forced people to work or stay at home, have determined a significant increase in the Internet traffic that puts tremendous pres...

A Generative Machine Learning-Based Approach for Inverse Design of Multilayer Metasurfaces

 The synthesis of a metasurface exhibiting a specific set of desired scattering properties is a time-consuming and resource-demanding process, which conventionally relies on many cycles of full-wave simulations.It requires an experienced designer to choose the number of the metallic layers, the scatterer shapes and dimensions, and the type and the t...

VeriML Enabling Integrity Assurances and Fair Payments for Machine Learning as a Service

 Machine Learning as a Service (MLaaS) allows clients with limited resources to outsource their expensive ML tasks to powerful servers. Despite the huge benefits, current MLaaS solutions still lack strong assurances on: 1) service correctness (i.e., whether the MLaaS works as expected); 2) trustworthy accounting (i.e., whether the bill for the MLaaS...

Smart Portable Pen for Continuous Monitoring of Anaesthetics in Human Serum with Machine Learning

  Continuous monitoring of anaesthetics infusion is demanded by anaesthesiologists to help in defining personalized dose, hence reducing risks and side effects. We propose the first piece of technology tailored explicitly to close the loop between anaesthesiologist and patient with continuous drug monitoring. Direct detection of drugs is achieved...

Multilayer Machine Learning-Assisted Optimization-Based Robust Design and Its Applications to Antennas and Arrays

 An efficient multilayer machine learning-assisted optimization (ML-MLAO)-based robust design method is proposed for antenna and array applications. Machine learning methods are introduced into multiple layers of the robust design process, including worst-case analysis (WCA), maximum input tolerance hypervolume (MITH) searching, and robust optimizat...

A Confidence-Aware Machine Learning Framework for Dynamic Security Assessment

 Dynamic Security Assessment for the future power system is expected to be increasingly complicated with the higher level penetration of renewable energy sources and the widespread deployment of power electronic devices, which drive new dynamic phenomena. As a result, the increasing complexity and the severe computational bottleneck in real time ope...

Machine Learning for Automating the Design of Millimeter-Wave Baluns

 We propose a framework to analyze mm-wave baluns directly from physical parameters by adding a dimension of Machine Learning (ML) to existing electromagnetic (EM) methods.From a generalized physical model of mm-wave baluns, we train physical-electrical Machine Learning models that both accurately and quickly compute the electrical parameters of mm-...

Evaluating Performance of EEG Data-Driven Machine Learning for Traumatic Brain Injury Classification

 Objectives: Big data analytics can potentially benefit the assessment and management of complex neurological conditions by extracting information that is difficult to identify manually. In this study, we evaluated the performance of commonly used supervised machine learning algorithms in the classification of patients with traumatic brain injury (T...

Coordinates-based Resource Allocation Through Supervised Machine Learning

 Appropriate allocation of system resources is essential for meeting the increased user-traffic demands in the next generation wireless technologies.Traditionally, the system relies on channel state information (CSI) of the users for optimizing the resource allocation, which becomes costly for fast-varying channel conditions.Considering that future ...

Assessment of Machine Learning Models for Classification of Movement Patterns During a Weight-Shifting Exergame

 In exercise gaming (exergaming), reward systems are typically based on rules/templates from joint movement patterns. These rules or templates need broad ranges in definitions of correct movement patterns to accommodate varying body shapes and sizes.This can lead to inaccurate rewards and, thus, inefficient exercise, which can be detrimental to prog...

An Intrusion Detection Method Based on Machine Learning and State Observer for Train-Ground Communication Systems

 The communication-based train control (CBTC) system is a typical cyber physical system in urban rail transit.The train-ground communication system is a very important subsystem of the CBTC system and uses the wireless communication protocols to transmit control commands.However, it faces some potential information security risks.To ensure informati...

Adaptation Strategies for Automated Machine Learning on Evolving Data

  Automated Machine Learning (AutoML) systems have been shown to efficiently build good models for new datasets. However, it is often not clear how well they can adapt when the data evolves over time. The main goal of this study is to understand the effect of concept drift on the performance of AutoML methods, and which adaptation strategies can ...

IoT BASED SMART FARMING USING MACHINE LEARNING

 The deficiency of water all throughout the planet compel us to limit the use of water. Over 75% of new water assets were utilizing for water system reason so productive use of water in water system framework with cutting edge strategy is required. This paper presents a cutting-edge innovation based savvy framework to anticipate the water system nec...

Multi objective Automated Type-2 Parsimonious Learning Machine to Forecast Time-Varying Stock Indices Online

 Real-time forecasting of the financial time-series data is challenging for many machine learning (ML) algorithms. First, many ML models operate offline, where they need a batch of data, which may not be available during training. Besides, due to a fixed architecture of the majority of the offline-based ML models, they suffer to deal with the uncert...

Machine Learning Assisted Stochastic Unit Commitment during Hurricanes with Predictable Line Outages

 Stochastic unit commitment is an efficient method for grid operation in the presence of significant uncertainties.An example is an operation during a predicted hurricane with uncertain line out-ages.However, the solution quality comes at the cost of substantial computational burden, which makes its adoption challenging.This paper evaluates some pos...

Evolving Fully Automated Machine Learning via Life-Long Knowledge Anchors

 Automated Machine Learning (AutoML) has achieved remarkable progress on various tasks, which is attributed to its minimal involvement of manual feature and model designs. However, existing AutoML pipelines only touch parts of the full machine learning pipeline, e.g., Neural Architecture Search or optimizer selection.This leaves potentially importan...

An Adaptive Machine Learning Framework for Behind-the-Meter Load PV Disaggregation

 A significant amount of distributed photovoltaic (PV) generation is invisible to distribution system operators since it is behind the meter on customer premises and not directly monitored by the utility. The generation essentially adds an unknown varying negative demand to the system, which causes additional uncertainty in determining the total loa...

Machine Learning Exercises on One Dimensional Electromagnetic Inversion

 This work aims to enhance our fundamental understanding of how the measurement setup used to generate training and testing datasets affects the accuracy of the machine learning algorithms that attempt solving electromagnetic inversion problems solely from data. A systematic study is carried out on a one-dimensional semi-inverse electromagnetic prob...

Evaluating User and Machine Learning in Short-and Long-term Pattern Recognition-based Myoelectric Control

  ? Proper training is essential to achieve reliable pattern recognition (PR) based myoelectric control. ? The amount of training is commonly determined by experience. ? The purpose of this study is to provide an offline validation method that makes the offline performance transferable to online control and find the proper amount of training tha...

Dynamic Quaternion Extreme Learning Machine

 Quaternion random neural network trained by extreme learning machine (Q-ELM) becomes attractive for its good learning capability and generalization performance in 3 or 4-dimensional (3/4-D) hypercomplex data learning. But how to determine the optimal network architecture is always challenging in Q-ELM. To this end, a novel error-minimization based ...

Boosted Genetic Algorithm Using Machine Learning for Traffic Control Optimization

 Traffic control optimization is a challenging task for various traffic centers around the world and the majority of existing approaches focus only on developing adaptive methods under normal (recurrent) traffic conditions. Optimizing the control plans when severe incidents occur still remains an open problem, especially when a high number of lanes ...

A Survey on Curriculum Learning

 ? Curriculum learning (CL) is a training strategy that trains a machine learning model from easier data to harder data, which imitates the meaningful learning order in human curricula. ? As an easy-to-use plug-in, the CL strategy has demonstrated its power in improving the generalization capacity and convergence rate of various models in a wide r...

A Multiple Gradient Descent Design for Multi-task Learning on Edge Computing Multi-objective Machine Learning Approach

 Multi-task learning technique is widely utilized in machine learning modeling where commonalities and differences across multiple tasks are exploited. However, multiple conflicting objectives often occur in multi-task learning. Conventionally, a common compromise is to minimize the weighted sum of multiple objectives which may be invalid if the obj...

Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning

 ? Automated Machine Learning (AutoML) seeks to automatically find so-called machine learning pipelines that maximize the prediction performance when being used to train a model on a given dataset. One of the main and yet open challenges in AutoML is an effective use of computational resources: An AutoML process involves the evaluation of many candi...

On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images a Survey

 Stereo matching is one of the longest-standing problems in computer vision with close to 40 years of studies and research. Throughout the years the paradigm has shifted from local, pixel-level decision to various forms of discrete and continuous optimization to data-driven, learning-based methods. Recently, the rise of machine learning and the rapi...

Minimizing Training Time of Distributed Machine Learning by Reducing Data Communication

 ? Due to the additive property of most machine learning objective functions, the training can be distributed to multiple machines. ? Distributed machine learning is an efficient way to deal with the rapid growth of data volume at the cost of extra inter-machine communication. ? One common implementation is the parameter server system which cont...

Machine Learning Exercises on One Dimensional Electromagnetic Inversion

 ? This work aims to enhance our fundamental understanding of how the measurement setup used to generate training and testing datasets affects the accuracy of the machine learning algorithms that attempt solving electromagnetic inversion problems solely from data. ? A systematic study is carried out on a one-dimensional semi-inverse electromagneti...

Machine Learning Based Detection Method for Wafer Test Induced Defects

 ? Wafer test is carried out after integrated circuits (IC) fabrication to screen out bad dies. ? In addition, the results can be used to identify problems in the fabrication process and improve manufacturing yield. ? However, the wafer test itself may induce defects to otherwise good dies. ? Test-induced defects not only hurt overall manufact...

A NEW IMPULSE ON STROKE PATIENT HEALTH CARE USING DEEP LEARNING (STROKE PREDICTION)

 Many predictive techniques have been widely applied in clinical decision Making such as predicting occurrence of a disease or diagnosis, evaluating Prognosis or outcome of diseases and assisting clinicians to recommend Treatment of diseases. However, the conventional predictive models or techniques are still not effective enough in capturing the...

Blood Viscosity based Heart Disease Risk Prediction Model in EdgeFog

 In this paper, heart disease prediction modeled using partially observable Markova decision process (POMDP) is proposed. In emergency, the patient is alerted through the doctor by fog computing. Ambulance sent to the location of patient at critical situations. The doctor gets the data through fog computing iFogSim. Fog computing in healthcare is a ...

Malaria detection in blood Sample image using Python

 Malaria is one of the deadliest diseases ever exists in this planet. Automated evaluation process can notably decrease the time needed for diagnosis of the disease. This will result in early onset of treatment saving many lives. As it poses a serious global health problem, we approached to develop a model to detect malaria parasite accurately from ...

Disease Prediction in Health Care System

 To keep pace with the developments in medical informatics, health medical data is being collected continually. But, owing to the diversity of its categories and sources, medical data has become so complicated in many hospitals needs a clinical decision support (CDS) system for its management. To effectively utilize the accumulating health data, we ...

Classification of Kidney Images Using Cuckoo Search Algorithm and Artificial Neural Network

 Ultrasound (US) imaging is used to provide the structural abnormalities like stones, infections and cysts for kidney diagnosis and also produces information about kidney functions. The goal of this work is to classify the kidney images using US according to relevant features selection. In this work, images of a kidney are classified as abnormal i...

Evaluation of image processing technique in identifying rice blast disease in field conditions based on KNN algorithm improvement by K-means

 Nowadays, rice farming is affected by various diseases that are economically significant and worthy of attention. One of these diseases is blast. Rice blast is one of the most important limiting factors in rice yield. The purpose of this study is the timely and rapid diagnosis of rice blast based on the image processing technique in field condition...

Multi-level Attention Networks for Multi-step Citywide Passenger Demands Prediction

 For the emerging mobility-on-demand services, it is of great significance for predicting passenger demands based on historical mobility trips to achieve better vehicle distribution. Prior works have focused on predicting next-step passenger demands at selected locations or hotspots. However, we argue that multi-step citywide passenger demands encap...

BANKING CHATBOT

 This paper discusses the adoption of chatbots and virtual Assistants by different category of banks (private sector banks and public sector banks) in India. The research paper presents a brief introduction of banking industry in India, history, characteristics, and architecture of chatbots and virtual assistants. The research paper also included ba...

ATTENDANCE MANAGEMENT SYSTEM BASED ON THE CLOUD WEB APPLICATION WITH THE FUNCTION OF FACE RECOGNITION

 The problem about checking attendant is the main problem of teacher in nowadays. In order to solve this problem, Many systems have been completely changed due to this evolve to achieve more accurate results. However, in my study, these study still lack of the efficiency about correct the face and students cannot verify or pose to edit the data when...

EARTHQUAKE PREDICTION ANALYSIS USING NEURAL NETWORK

 The recent scientific advances in understanding the hierarchical nature of the lithosphere and its dynamics based on systematic monitoring and evidence of its space-energy similarity at global, regional, and local scales did result the design of reproducible inter mediate term middle-range earthquake prediction technique. The real-time experimental...

Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound

 Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultra sono graphy (L...

Electrical energy consumption prediction using machine learning for industry and smart buildings

 Machine learning (ML) methods has recently contributed very well in the advancement of the prediction models used for energy consumption. Such models highly improve the accuracy, robustness, and precision and the generalization ability of the conventional time series forecasting tools. This article reviews the state of the art of machine learning ...

Detection of Potato Disease Using Image Segmentation and Machine Learning

 Potato is one of the prominent food crops all over the world. In Bangladesh, potato cultivation has been getting remarkable popularity over the last decades. Many diseases affect the proper growth of potato plants. Noticeable diseases are seen in the leaf region of this plant. Two common and popular leaf diseases of the potato plants are Early Blig...

computerized classification of CT lung images using CNN with watershed segmentation

 Cancer is a major threat to the lives of human beings. Around 74% of the people who get affected by cancer lost their lives. But early detection of cancer cells can prevent death rates. CT(Computerized Tomography) is one of the major used for cancer cell identifications by the oncologist. Computer-aided cancer detection plays a major role in the de...

Fake Currency Detection Using Image Processing

 ? Fake currency is the money produced without the approval of the government, it is considered as a great offence. Most of them are doing it as a profession. Fake currency causes major issues in our economic growth and also it will decrease the value of original money. There are various methods available to fiused to detect these fake notes. We are...

Vegetable disease detection using k-means clustering and svm

 India is the cultivating country and our country is the biggest maker in agricultural products. So, we have to classify and exchange our agricultural products. Manual arranging is tedious and it requires works. The automatic grading system requires less time for grading of the agricultural products. Image processing technique is helpful in examinat...

Study of clinical staging and classification of retinal images for retinopathy of prematurity ROP screening

 Retinopathy of Prematurity (ROP) is a disease which requires immediate precautionary measures to prevent blindness in the infants, and this condition is prevalent in premature babies in all the underdeveloped, developing, and in the developed countries as well. This paper proposes a tool by which the stage and zones of Retinopathy of Prematurity in...

Fake News Detection with Generated Comments For News Articles

 Recently, fake news is shared via social networks and makes wrong rumors more diffusible. This problem is serious because the wrong rumor sometimes makes social damage by deceived people. Fact-checking is a solution to measure the credibility of news articles. However the process usually takes a long time and it is hard to make it before their diff...

Detection of Potato Disease Using Image Segmentation and Machine Learning

 Potato is one of the prominent food crops all over the world. In Bangladesh, potato cultivation has been getting remarkable popularity over the last decades. Many diseases affect the proper growth of potato plants. Noticeable diseases are seen in the leaf region of this plant. Two common and popular leaf diseases of the potato plants are Early Blig...

Fake News Detection with Generated Comments For News Articles

 Recently, fake news is shared via social networks and makes wrong rumors more diffusible. This problem is serious because the wrong rumor sometimes makes social damage by deceived people. Fact-checking is a solution to measure the credibility of news articles. However the process usually takes a long time and it is hard to make it before their diff...

Disease Detection in coffee plants using convolutional Neural Network

 Rust is a severe disease affecting many productive coffee regions. It is caused by pathogenic fungi that attack the underside of coffee leaves and it is characterized by the presence of yellow-orange and powdery points. If not treated, rust can cause a drop in coffee production of up to 45%. In this sense, this paper presents a contribution to the ...

Detection of Potato Disease Using Image Segmentation and Machine Learning

 Potato is one of the prominent food crops all over the world. In Bangladesh, potato cultivation has been getting remarkable popularity over the last decades. Many diseases affect the proper growth of potato plants. Noticeable diseases are seen in the leaf region of this plant. Two common and popular leaf diseases of the potato plants are Early Blig...

Advanced Skin Diseases Diagnosis leveraging image Processing

 Air pollution affects human skin in many ways. Skin diseases are common in densely populated regions. These diseases have a devastating impact on people's lives by creating a huge need for the disease diagnosis. The proposed work on skin disease determination system aims for an accurate diagnosis leveraging image processing. The methodology outline...

Traffic Forecasting and Decision making of investment and construction of Tourism Highway under the Background of Artificial intelligence

 Passenger traffic fore casting and decision-making of investment and construction of the tourism highway under the background of artificial intelligence is studied in this paper. As an important information asset, big data is expected to provide people with comprehensive, accurate, and real-time business insights a...

Road accident analysis and prediction of accident severity by using machine learning

 Injuries due to road accidents are one of the most prevalent causes of death apart from health related issues. The World Health Organization states that road traffic injuries caused an estimated 1.35 million deaths worldwide in the year 2016. That is, a person is killed every 25 seconds. This calls for the need to analyse road accidents and the fac...

Performance analysis of brain Tumor image classification using CNN and SVM

 Tumour is the undesired mass in the body. Brain tumour is the significant growth of brain cells. Manual method of classifying is time consuming and can be done at selective diagnostic centers only. Brain tumour classification is crucial task to do since treatment is based on different location and size of it. Magnetic Resonance Imaging (MRI) is mos...

Detection of leukemia in human blood sample based on microscopic images

 At the moment, identification of blood disorders is through visual inspection of microscopic images of blood cells. From the identification of blood disorders, it can lead to classification of certain diseases related to blood. This paper describes a preliminary study of developing a detection of leukemia types using microscopic blood sample images...

Banana ripeness analysis using reluctance and photoluminescence

 Bananas are favorite tropical fruit since bananas contain source of nutrition. In the industry, the banana ripeness is a mayor issue for harvesting. It is a challenge if we have a good simple instrument for determining ripeness of bananas. In this work, we utilized two optical measurement systems for detecting ripeness of bananas, i.e., reflectance...

Automatic detection of tuberculosis related abnormalities in Chest X-ray Images using hierarchical feature extraction scheme

 Machine learning techniques have been widely used for abnormality detection in medical images. Chest X-ray images (CXR) are among the non-invasive diagnostic tools used to detect various disease pathologies. The ambiguous anatomical structure of soft tissues is one of the major challenges for segregating normal and abnormal images. The main objecti...

Enabling Cost-Effective, SLO-Aware Machine Learning Inference Serving on Public Cloud

 The remarkable advances of Machine Learning (ML) have spurred an increasing demand for ML-as-a-Service on public cloud: developers train and publish ML models as online services to provide low-latency inference for dynamic queries. The primary challenge of ML model serving is to meet the response-time Service-Level Objectives (SLOs) of inference wo...

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