Online Payment Fraud Detection using Machine Learning in Python
As we are approaching modernity, the trend of paying online is increasing tremendously. It is very beneficial for the buyer to pay online as it saves time, and solves the problem of free money.
Also, we do not need to carry cash with us. But we all know that Good thing are accompanied by bad things.
The online payment method leads to frau...
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Tumor Detection using classification – Machine Learning and Python
Brain tumor detection is a critical application in the field of medical imaging, aimed at aiding healthcare professionals in the early and accurate diagnosis of brain tumors. This project leverages machine learning and deep learning techniques in Python to developa robust and reliable brain tumor detection system.
The system undergoes sensitivi...
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Fake News Detection using Machine Learning
In recent years, due to the booming development of online social networks, fake news for various commercial and political purposes has been appearing in large numbers and widespread in the online world.
With deceptive words, online social network users can get infected by these online fake news easily, which has brought about tremendous effects...
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The Quantum Path Kernel: A Generalized Neural Tangent Kernel for Deep Quantum Machine Learning
Building a quantum analog of classical deep neural networks represents a fundamental challenge in quantum computing.
A key issue is how to address the inherent non-linearity of classical deep learning, a problem in the quantum domain due to the fact that the composition of an arbitrary number of quantum gates, consisting of a series of sequenti...
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An Interpretable and Accurate Deep-Learning Diagnosis Framework Modeled With Fully and Semi-Supervised Reciprocal Learning
The deployment of automated deep-learning classifiers in clinical practice has the potential to streamline the diagnosis process and improve the diagnosis accuracy, but the acceptance of those classifiers relies on both their accuracy and interpretability.
In general, accurate deep-learning classifiers provide little model interpretability, whi...
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Public news fake identification using asp.net
Fake news, deceptive information, and conspiracy theories are part of our everyday life. It is really hard to distinguish between false and valid information.
As contemporary people receive the majority of information from electronic publications, in many cases fake information can seriously harm people’s health or economic status.
This article...
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Stock price movement prediction based on the historical data using machine learning
predicting stock market is one of the challenging tasks in the field of computation. Physical vs. physiological elements, rational vs. illogical conduct, investor emotions, market rumors, and other factors all play a role in the prediction.
All of these factors combine to make stock values very fluctuating and difficult to forecast accurately. ...
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Self-Supervised Learning for Electroencephalography
Decades of research have shown machine learning superiority in discovering highly nonlinear patterns embedded in electroencephalography (EEG) records compared with conventional statistical techniques.
However, even the most advanced machine learning techniques require relatively large, labeled EEG repositories.
EEG data collection and lab...
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Production-Level Artificial Intelligence Applications in Semiconductor Supply Chains
This is a panel paper which discusses the use of Artificial Intelligence (AI) techniques to address production level problems in semiconductor manufacturing.
We have gathered a group of expert semiconductor researchers and practitioners from around the world who have applied AI techniques to semiconductor problems and the paper provides their a...
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machine learning in innovations in stroke identification
Cerebrovascular diseases such as stroke are among the most common causes of death and disability worldwide and are preventable and treatable.
Early detection of strokes and their rapid intervention play an important role in reducing the burden of disease and improving clinical outcomes.
In recent years, machine learning methods have attracte...
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A Human-Machine Agent Based on Active Reinforcement Learning for Target Classification in Wargame
To meet the requirements of high accuracy and low cost of target classification in modern warfare, and lay the foundation for target threat assessment, the article proposes a human-machine agent for target classification based on active reinforcement learning (TCARL_H-M), inferring when to introduce human experience guidance for model and how to au...
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Evaluation of academic performance in web application
Managing the result sheets using traditional approach is a cumbersome process. The person must maintain the result records in registers and files using pen and paper.
The problem with this approach is, it requires lot of paperwork which is the part of our non-renewable natural resources. We are in the age, where we must think about sustain...
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e-Authentication system in cyber security
Authenticating the students’ identity and authenticity of their work is increasingly important to reduce academic malpractices and for quality assurance purposes in Education.
There is a growing body of research about technological innovations to combat cheating and plagiarism. However, the literature is very limited on the impact of e-authentica...
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Cursor movement on object motion
As modern technology of human-computer interactions become important in our everyday lives, varieties of mouse of all kinds of shapes and sizes were invented, from a casual office mouse to hard-core gaming mouse.
However, there are some limitations to this hardware as they are not as environmentally friendly as it seems. For example, the physical...
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Crime rate prediction using machine learning
In India, there are continuously more criminal cases filed, which results in an increase in the number of cases still outstanding. These ongoing increases in criminal cases make them challenging to categorise and resolve.
Therefore, it's crucial to identify a location's patterns of criminal activity in order to stop it from happening in order to ...
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Android battery saver system
This System is an innovative Application Allowing the System and the user to take the usage From Build-in classes and put a list in front of the User for him the full detailed review.
The List also Consists of the applications using the battery usage And also determines, analyze the battery level. If the Battery level is low or gets drop down or ...
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Application of Block Chaining Technology in Finance and Accounting Field
Block chaining technology is a distributed infrastructure and computing paradigm.
The latest version is represented by the super account book. The latest version is block chain 3.
From the perspective of large data, this paper systematically combs the essence and core technology of block chain technology, and expounds the application status...
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Ambulance - Emergency Ambulance Management System
About 33,000 people die because the ambulance cannot reach them in time. It is not necessary that when you call an ambulance, the nearest ambulance will reach you.
The paper describe a model to track the nearest free ambulance in the area using global positioning system and bring it to the person in distress.
The GPS device continuously mov...
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Vehicle Theft Detection Using Machine Learning
In the age of cloud computing, cloud users with limited storage can outsource their data to remote servers. These servers, in lieu of monetary benefits, offer retrievability of their clients’ data at any point of time.
Secure cloud storage protocols enable a client to check integrity of outsourced data. In this article, we explore the possibili...
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Secure cloud storage with data dynamic using secure network coding
In the age of cloud computing, cloud users with limited storage can outsource their data to remote servers. These servers, in lieu of monetary benefits, offer retrievability of their clients’ data at any point of time.
Secure cloud storage protocols enable a client to check integrity of outsourced data. In this article, we explore the possibili...
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RECOMMENDATION OF CROP AND FERTILIZER SYSTEM USING MACHINE LEARNING
Machine learning (ML) can make use of agricultural data related to crop yield under varying Crop based nutrient levels, and climatic fluctuations to suggest appropriate crops or supplementary nutrients to achieve the highest possible production.
The aim of this study was to evaluate the efficacy of five distinct ML models for a dataset sourced ...
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Railway Reservation Management System
The Railway Reservation System (RRS) is a comprehensive software solution designed to facilitate the booking of train tickets for passengers.
With the ever-growing demand for rail travel, an efficient and user-friendly reservation system becomes indispensable for both passengers and railway authorities.
The RRS aims to streamline the ticket boo...
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ONLINE E-COMMERCE WEBSITE FOR FARMER PRODUCT SALES USING MACHINE LEARNING
In our day to day life we consume food and our survival is based on mainly food. A considerable amount of our food is coming from farms and other means too. These farmers do their hard work for growing and serving many lives across the country, which pays for their source of income. But due to intermediates in the selling of their final product...
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Online book store E-Commerce web application
The Internet by far plays a major role in people’s life. It has drastically improved the quality of life and the standard of living of so many people. It has widened its branches into many different levels and areas. The e-commerce industry is one such branch which has come into spotlight in the recent years. The online bookstore system has eas...
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Minimize Social Network Rumors Based on Rumor Path Tree
Social networks have become a powerful information spreading platform. How to limit rumor spread on social networks is a challenging problem.
In this article, we combine information spreading mechanisms to simulate real-world social network user behavior. Based on this, we estimate the risk degree of each node during the hazard period and analy...
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Machine Learning Based Healthcare System for Investigating the Association Between Depression and Quality of Life
The present study aims to elucidate the main variables that increase the level of depression at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model.
Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the tr...
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LANDSLIDE MONITORING AND NOTIFICATION USING IOT
Hazards from landslides are everywhere. Landslides are more likely to occur on steeply sloped hillsides. For several case studies throughout the globe, researchers have performed landslide prediction, detection, and monitoring.
The major goal of researching landslide detection is to stop natural disasters by seeing their early movement. This wi...
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IOT Based E-Health care BAsed on Big Data
IoT-enabled smart healthcare systems has the characteristics of heterogeneous fusion, cross domain, collaborative autonomy, dynamic change and open interconnection, but they bring huge challenges in privacy issues. We proposed a scheme of forward privacy preserving for IoT-enabled healthcare systems, which mainly includes a searchable encryptio...
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Government Scheme using Chatbot Artificial Intelligent
In the ever-changing landscape of digital services and government initiatives, our project embarks on a mission to empower citizens with a revolutionary chatbot known as SchemeSetu.
This intelligent chatbot serves as a central information hub, consolidating crucial details on governmentsponsored loans and insurance schemes from various sources....
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Flight Reservation System
The Flight Reservation System (FRS) is a comprehensive software solution designed to facilitate the booking of Flight tickets for passengers.
With the ever-growing demand for Flight travel, an efficient and user-friendly reservation system becomes indispensable for both passengers and Flight authorities.
The FRS aims to streamline the tic...
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Fingerprint based attendance in python
Fingerprint authentication is one of the most popular and accurate technology. Our project is a fingerprint attendance system that records the attendance of students based on their fingerprint matches them against the database to mark their attendance.
Fingerprint-based attendance system used for ensures that there is a minimal fault in gatheri...
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facilitation for differently abled persons various government schemes
In the ever-changing landscape of digital services and government initiatives, our project embarks on a mission to empower citizens with a revolutionary chatbot known as SchemeSetu.
This intelligent chatbot serves as a central information hub, consolidating crucial details on governmentsponsored loans and insurance schemes from various sources....
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EPILEPSY PATIENT MONITORING SYSTEM
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 a...
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Enhancing Digital Image Forgery Detection Using Transfer Learning
Nowadays, digital images are a main source of shared information in social media. Meanwhile, malicious software can forge such images for fake information. So, it’s crucial to identify these forgeries.
This problem was tackled in the literature by various digital image forgery detection techniques. But most of these techniques are tied to detec...
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EFFICIENT DATA INTEGRITY AND AUDITING IN CLOUD BY USING SH256
The utilization of cloud storage is increasingly prevalent as the field of cloud computing continues to expand. Several cloud data auditing schemes have been proposed within the academic community to guarantee the availability and integrity of cloud data.
Nevertheless, several schemes rely on public key infrastructure and identity-based encrypt...
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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 th...
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Design & Implementation of High Dynamic Range Current Measurement System for IoT Applications
Internet-of-Things (IoT) devices have a strict necessity for power consumption to achieve the expected battery life. Therefore, measuring and optimizing the energy consumption of IoT nodes are essential.
IoT nodes feature extreme current consumption range over 100 dB between their operating modes. The main focus of this article is to design and...
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CONTROLLING WHEELCHAIR BY ANALYSING BRAIN NEURAL AND HEAD MOVEMENTS
Brain Computer Interface (BCI) sometimes called Brain-Machine Interface (BMI). It is a direct communication pathway between the Brain’s Electrical Activity and external device. Computer based system that acquires brainsignals, analyzes the man dtranslatesthemintocommandsthatarerelayed to an output device to carry out into desired action.
We can...
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Bus Pass Management System
Bus Pass Management System it is the web application that will manage all the records of pass which is issue by bus administrative.
Bus Pass Management System which is a automatic system which delivering data processing in a very high speed in the systematic manner. This system helps bus administrative to keep records of bus passes. Before this...
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Breast cancer detection using Adaboost classifier
Breast cancer is a deadly disease; an accurate and early diagnosis of breast cancer is the most efficient method to decrease the death rate.
But, in the early detection and diagnosis of breast cancer, differentiating abnormal tissues is a challenging task. In this paper, a weight-based AdaBoost algorithm is proposed for an effective detection a...
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AUTOMATIC WATER TANK CLEANER SYSTEM
Aim of this project is to develop a mechanical system for cleaning domestic cylindrical water tank. The mechanical system includes simple mechanism with centrifugal force.The principal involved in this mechanism is left hand fleming rule, were a simple electric energy is converted into rotational energy.
The motor shaft is connected to a long v...
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An Intelligent Patient Medicine Reminder
The aim of this project is to remind individuals who forget to take their medicines on time.
Elderly people because of their age typically forget about to take their drugs.
This project will support to remind the patient to take his/her medicinal drug at prescribed time.
The proposed system is best suited for elderly persons and those w...
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An efficient task scheduling and resource allocation in cloud computing by using particle swarm optimization
Cloud computing offers on-demand availability of computing resources over the Internet. To attract users, cloud providers offer their resources as services at reasonable prices and provide various price models to reflect higher level of quality of service (QoS), which are referred as pricing schemes. k-times anonymous authentication (k-TAA) is an a...
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A sensor data analytics for patient monitoring in connected healthcare application
As an emerging form of enabling technology, Web-based e-Health portals provide patients easier accesses to their healthcare information and services. We design and implement such an e-Health portal which can integrate many backend medical services effectively. A major challenge in designing such a system is to meet critical security requirement...
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A Robust Emergency communication in hills and forest using LORA Communication
The need for efficient fire detection systems is highlighted by the substantial threats that forest fires pose to both human lives and the ecosystem. Limited coverage, high false alarm rates, and delays in signal transmission are just a few of the problems traditional methods for detecting forest fires sometimes encounter.
This research stu...
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A POWER QUALITY IMPROVE EV CHANGE WITH BRIDGELESS LUO CONVERTER
An high non-linear diode bridge rectifier has been used in the electric vehicle charger for the use of DC voltage at only the DC-DC converter input but also to implement significant power quality problems only at existing AC input.
By eliminating their input bridges, such problems developed a tremendous a need boost the Performance of power quali...
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Agriculture Soil moisture and ph detection using IOT
In India economy is mainly based on agriculture still we are not able to make it profitable and make sustainable use of our land resources. Main reason is lack of knowledge regarding soil. There are many types of soil present and each type of soil has different characteristics. So it is necessary to test soil characteristics. There are different to...
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Covid-19 outbreak Prediction with the Base of Deep Learning Vgg16
In this paper, modifications in neoteric architectures such as VGG16, VGG19, ResNet50, and InceptionV3 are proposed for the classification of COVID-19 using chest X-rays.
The proposed architectures termed “COV-DLS” consist of two phases: heading model construction and classification.
The heading model construction phase utilizes four modified d...
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UNDERSTANDING DEPTH OF REFLECTIVE WRITING IN WORKPLACE LEARNINGASSESSMENTS USING MACHINE LEARNING CLASSIFICATION
The collapse of Dam I, owned by Vale S.A, in Brumadinho-MG (Brazil), among other serious socioenvironmental consequences, contaminated the waters of the Paraopeba River in a stretch of hundreds of kilometers. Considering the relevance of monitoring water quality, and knowing that field evaluation is a time-consuming and costly procedure, the use of...
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UNDERSTANDING DEPTH OF REFLECTIVE WRITING IN WORKPLACE LEARNINGASSESSMENTS USING MACHINE LEARNING CLASSIFICATION
The collapse of Dam I, owned by Vale S.A, in Brumadinho-MG (Brazil), among other serious socioenvironmental consequences, contaminated the waters of the Paraopeba River in a stretch of hundreds of kilometers. Considering the relevance of monitoring water quality, and knowing that field evaluation is a time-consuming and costly procedure, the use of...
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3-D DECONVOLUTIONAL NETWORKS FOR THE UNSUPERVISED REPRESENTATION LEARNING OF HUMAN MOTIONS
The major obstacle for learning-based RF sensing is to obtain a high-quality large-scale annotated dataset. However, unlike visual datasets that can be easily annotated by human workers, RF signal is non-intuitive and non-interpretable, which causes the annotation of RF signals time-consuming and laborious. To resolve the rapacious appetite of anno...
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MACHINE LEARNING FOR INTER-TURN SHORT-CIRCUIT FAULT DIAGNOSIS IN PERMANENT MAGNET SYNCHRONOUS MOTORS
Permanent Magnet Synchronous Motor (PMSM) is widely used due to its advantages of high power density, high efficiency and so on. In order to ensure the reliability of a PMSM system, it is extremely vital to accurately diagnose the incipient faults. In this paper, a variety of optimization algorithms are utilized to realize the diagnosis of the faul...
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I LET DEPRESSION AND ANXIETY DROWN ME…” IDENTIFYING FACTORS ASSOCIATEDWITH RESILIENCE BASED ON JOURNALING USING MACHINE LEARNING AND THEMATIC
Over the years, there has been a global increase in the use of technology to deliver interventions for health and wellness, such as improving people’s mental health and resilience. An example of such technology is the Q-Life app which aims to improve people’s resilience to stress and adverse life events through various coping mechanisms, including ...
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FUSING SELL-SIDE ANALYST BIDIRECTIONAL FORECASTS USING MACHINE LEARNING
Sell-side analysts’ recommendations are primarily targeted at institutional investors mandated to invest across many companies within client-mandated equity benchmarks, such as the FTSE/JSE All-Share index. Given the numerous sell-side recommendations for a single stock, making unbiased investment decisions is not often straightforward for portfoli...
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FAIRNESS IN SEMI-SUPERVISED LEARNING: UNLABELED DATA HELP TO REDUCE DISCRIMINATION
Machine learning is widely deployed in society, unleashing its power in a wide rangeof applications owing to the advent of big data.One emerging problem faced by machine learning is the discrimination from data, and such discrimination is reflected in the eventual decisions made by the algorithms. Recent study has proved that increasing the size of...
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ENRICHING THE TRANSFER LEARNING WITH PRE-TRAINED LEXICON EMBEDDINGFOR LOW-RESOURCE NEURAL MACHINE TRANSLATION
Most State-Of-The-Art (SOTA) Neural Machine Translation (NMT) systems today achieve outstanding results based only on large parallel corpora. The large-scale parallel corpora for high-resource languages is easily obtainable. However, the translation quality of NMT for morphologically rich languages is still unsatisfactory, mainly because of the dat...
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AUTOMATED SCREENING SYSTEM FOR ACUTE MYELOGENOUS LEUKEMIA DETECTION
Acute myelogenous leukemia (AML) is a subtype of acute leukemia, which is prevalent among adults.The average age of a person with AML is 65 years. The need for automation of leukemia detection arises since current methods involve manual examination of the blood smear as the first step toward diagnosis. This is time-consuming, and its accuracy depen...
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AN ONLINE TRANSFER LEARNING FRAMEWORK WITH EXTREME LEARNING MACHINFOR AUTOMATED CREDIT SCORINGE
Automated Credit Scoring (ACS) is the process of predicting user credit based on historical data. It involves analyzing and predicting the association between the data and particular credit values based on similar data. Recently, ACS has been handled as a machine learning problem, and numerous models were developed to address it. In this paper, we ...
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THE HIDDEN SEXUAL MINORITIES: MACHINE LEARNING APPROACHES TO ESTIMATE THE SEXUAL MINORITY ORIENTATION AMONG BEIJING COLLEGE STUDENTS
The availability of digital technology in the hands of every citizenry worldwide makes an availableunprecedented massive amount of data. The capability to process these gigantic amounts of data in real-time with Big Data Analytics (BDA) tools and Machine Learning (ML) algorithms carries many paybacks. However, the highnumber of free BDA tools, plat...
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ON THE SYNERGIES BETWEEN MACHINE LEARNING AND BINOCULAR STEREO FO 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 rapid...
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MACHINE LEARNING AND MARKETING A SYSTEMATIC LITERATURE REVIEW
Even though machine learning (ML) applications are not novel, they have gained popularity partly due to the advance in computing processing.This study explores the adoption of ML methods in marketing applications through a bibliographic review of the period 2008–2022. In this period, the adoption of ML in marketing has grown significantly. This gro...
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ICS: TOTAL FREEDOM IN MANUAL TEXT CLASSIFICATION SUPPORTED BY UNOBTRUSIVE MACHINE LEARNING
We present the Interactive Classification System (ICS), a web-based application that supports the activity of manual text classification.The application uses machine learning to continuously fit automatic classification models that are in turn used to actively support its users with classification suggestions. The key requirement we have establishe...
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MACHINE LEARNING FOR STRUCTURE DETERMINATION IN SINGLE-PARTICLE CRYO ELECTRON MICROSCOPY: A SYSTEMATIC REVIEW
Traditionally, X-ray crystallography and NMR spectroscopy represent major workhorses of structural biologists, with the lion share of protein structures reported in protein data bank (PDB) being generated by these powerful techniques.Despite their wide utilization in protein structure determination, these two techniques have logical limitations, wi...
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Data Privacy and key based security using SH256
Information security means protecting data, such as a database, from destructive forces and from the unwanted actions of unauthorized users. Information Security can be achieved by using cryptographic techniques. It is now very much demanding to develop a system to ensure better long lasting security services for message transaction over the Intern...
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ENHANCING REMEMBRANCE OF PASSWORD AS AN IMAGE
This paper centers around changing the process for having 'text' as secret word as individuals will generally frequently fail to remember their passwords and need to recuperate it. Proposing the procedure for having picture as a secret word which is kept encoded in a data set and unscrambled and matched to check for approval of client. It has been ...
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Covid-19 outbreak Prediction with the Base of Deep Learning Vgg16
In recent months, coronavirus disease 2019
(COVID-19) has infected millions of people worldwide. In
addition to the clinical tests like reverse transcription-
polymerase chain reaction (RT-PCR), medical imaging
techniques such as computed tomography (CT) can be used as a
rapid technique to detect and evaluate patients infected by
COVID...
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EMOJI TEXT BASED CHATBOT MUSIC RECOMMENDATION SYSTEM USING MACHINE LEARNING
Emojis are used in Computer Mediated Communication (CMC) as a way to express paralinguistics otherwise missing from text, such as facial expressions or gestures. However, finding an emoji on the ever expanding emoji list is a linear search problem and most users end up using a small subset of emojis that are near the top of the emoji list. Current ...
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A MULTIPLE GRADIENT DESCENT DESIGN FOR MULTI-TASK LEARNING ON EDGE COMPUTING MULTI-OBJECTIVE MACHINE LEARNING APPROACH
In multi-task learning, multiple tasks are solved jointly, sharing inductive bias between them. Multi-task learning is inherently a multi-objective problem because different tasks may conflict, necessitating a trade-off. A common compromise is to optimize a proxy objective that minimizes a weighted linear combination of pertask losses. However, thi...
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INTEGRATING MACHINE LEARNING ALGORITHMS WITH QUANTUM ANNEALING SOLVERS FOR ONLINE FRAUD DETECTION
Machine learning has been increasingly applied in identification of fraudulent transactions. However, most application systems detect duplicitous activities after they have already occurred, not at or near real time. Since spurious transactions are far fewer than the normal ones, the highly imbalanced data makes fraud detection very challenging and...
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LEARNING WITH SELECTED FEATURES
Feature selection is the task of choosing a small subset of features that is sufficient to predict the target labels well. Here, instead of trying to directly determine which features are better, we attempt to learn the properties of good features. For this purpose we assume that each feature is represented by a set of properties, referred to as me...
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MACHINE LEARNING TO IDENTIFY PSYCHOMOTOR BEHAVIORS OF DELIRIUM FOR PATIENTS IN LONG-TERM CARE FACILITY
This study aimed to develop accurate and explainable machine learning models for three psychomotor behaviors of delirium for hospitalized adult patients.A prospective pilot study was conducted with 33 participants admitted to a long-term care facility between August 10 and 25, 2020. During the pilot study, we collected 560 cases that included 33 cl...
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PREDICTING BRAIN AGE USING MACHINE LEARNING ALGORITHMS A COMPREHENSIVE EVALUATION
The rise of machine learning has unlocked new ways of analysing structural neuroimaging data, including brain age prediction. In this state-of-the-art review, we provide an introduction to the methods and potential clinical applications of brain age prediction. Studies on brain age typically involve the creation of a regression machine learning mod...
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QUANTUM-INSPIRED MACHINE LEARNING FOR 6G FUNDAMENTALS, SECURITY, RESOURCE ALLOCATIONS, CHALLENGES, AND FUTURE RESEARCH DIRECTIONS
Quantum computing is envisaged as an evolving paradigm for solving computationally complex optimization problems with a large-number factorization and exhaustive search.Recently, there has been a proliferating growth of the size of multi-dimensional datasets, the input-output space dimensionality, and data structures. Hence, the conventional machin...
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PREDICTION OF DIABETES EMPOWERED WITH FUSED MACHINE LEARNING
In the medical field, it is essential to predict diseases early to prevent them. Diabetes is one of the most dangerous diseases all over the world. In modern lifestyles, sugar and fat are typically present in our dietary habits, which have increased the risk of diabetes To predict the disease, it is extremely important to understand its symptoms. C...
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META-TRANSFER LEARNING THROUGH HARD TASKS
Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As deep neural networks (DNNs) tend to overfit using a few samples o...
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MACHINE LEARNING BASED HEALTHCARE SYSTEM FOR INVESTIGATING THE.ASSOCIATION BETWEEN DEPRESSION AND QUALITY OF LIFE
Machine learning (ML) algorithms are nowadays widely adopted in different contexts to perform autonomous decisions and predictions. Due to the high volume of data shared in the recent years, ML algorithms are more accurate and reliable since training and testing phases are more precise. An important concept to analyze when defining ML algorithms co...
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CREDIT CARD FRAUD DETECTION USING STATE-OF-THE-ART MACHINE LEARNING AND DEEP LEARNING ALGORITHMS
The usage of credit cards for online and regular purchases is exponentially increasing and so is the fraud related with it. A large number of fraud transactions are made every day. Various modern techniques like artificial neural network Different machine learning algorithms are compared, including Logistic Regression, Decision Trees, Random Forest...
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COGNITIVE WORKLOAD RECOGNITION USING EEG SIGNALS AND MACHINE LEARNING A REVIEW
Machine learning and its subfield deep learning techniques provide opportunities for the development of operator mental state monitoring, especially for cognitive workload recognition using electroencephalogram (EEG) signals. Although a variety of machine learning methods have been proposed for recognizing cognitive workload via EEG recently, there...
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ANOMALY DETECTION IN SELF-ORGANIZING NETWORKS CONVENTIONAL VERSUS.CONTEMPORARY MACHINE LEARNING
This paper presents a comparison of conventional and modern machine (deep) learning within the framework of anomaly detection in self-organizing networks. While deep learning has gained significant traction, especially in application scenarios where large volumes of data can be collected and processed, conventional methods may yet offer strong stat...
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A REVIEW ON MACHINE LEARNING STYLES IN COMPUTER VISION—TECHNIQUES AND FUTURE DIRECTIONS
Due to the advancement in the field of Artificial Intelligence (AI), the ability to tackle entire problems of machine intelligence. Nowadays, Machine learning (ML) is becoming a hot topic due to the direct training of machines with less interaction with a human.The scenario of manual feeding of the machine is changed in the modern era, it will lear...
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A NOVEL TWO-MODE INTEGRAL APPROACH FOR THERMAL ERROR MODELING IN CNC MILLING-TURNING MACHINING CENTER
Thermal errors have the largest contribution, as much as about 70%, to the machining inaccuracy of computer-numerical-controlled (CNC) machining centers. The error compensation method so far plays the most popular and effective way to minimize the thermal error. How to accurately and quickly build an applicable thermal error model (TEM) is the kern...
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A MINI-REVIEW OF MACHINE LEARNING IN BIG DATA ANALYTICS APPLICATIONS, CHALLENGES, AND PROSPECTS
The availability of digital technology in the hands of every citizenry worldwide makes an availableunprecedented massive amount of data. The capability to process these gigantic amounts of data in real-time with Big Data Analytics (BDA) tools and Machine Learning (ML) algorithms carries many paybacks. However, the highnumber of free BDA tools, plat...
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LAMINAR FLOW WATER TURBINE
The aim of our project is to design and fabricate a pneumatically operated tapping machine is called universal tapping machine. This device is operated by compressed air. It consists of the following main parts.
1. Barrel
2. Shaft
3. bearing
4. Couplings, etc.
A high pressure compressed air is forced on a fan and the fan is made...
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UNIVERSAL TAPPING MACHINE
The aim of our project is to design and fabricate a pneumatically operated tapping machine is called universal tapping machine. This device is operated by compressed air. It consists of the following main parts.
1. Barrel
2. Shaft
3. bearing
4. Couplings, etc.
A high pressure compressed air is forced on a fan and the f...
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WHEEL CHAIR CUM STRETCHER MODEL
The wheel chair cum stretcher model is a mechanism which is used for moving bed up and down. This is used for handicapped person for living like the normal persons do. For making the handicapped person’s job easy (i.e., they can move bed up and down). Two way switches is used to control the flow wheel chair. The Battery used to drive the D.C motor....
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SCALABLE AND PRACTICAL NATURAL GRADIENT FOR LARGE-SCALE DEEP LEARNING
large-scale distributed training of deep neural networks results in models with worse generalization performance as a result of the increase in the effective mini-batch size. previous approaches attempt to address this problem by varying the learning rate and batch size over epochs and layers, or ad hoc modifications of batch normalization we propo...
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ADVERSARIAL EVOLVING NEURAL NETWORK FOR LONGITUDINAL KNEE OSTEOARTHRITIS PREDICTION
Knee osteoarthritis (KOA) as a disabling joint disease has doubled in prevalence since the mid-20th century. Early diagnosis for the longitudinal KOA grades has been increasingly important for effective monitoring and intervention. Although recent studies have achieved promising performance for baseline KOA grading, longitudinal KOA grading has bee...
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UNSUPERVISED DEEP BACKGROUND MATTING USING DEEP MATTE PRIOR
Background matting is a recently developed image matting approach, with applications to image and video editing. It refers to estimating both the alpha matte and foreground from a pair of images with and without foreground objects. Recent work has applied deep learning to background matting, with very promising performance achieved. However, existi...
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UNSUPERVISED ALGORITHMS TO DETECT ZERO-DAYATTACKS STRATEGY AND APPLICATION
? In the last decade, researchers, practitioners and companies struggled for devising mechanisms to detect cyber-security threats. Among others, those efforts originated rule-based, signature-based or supervised Machine Learning (ML) algorithms that were proven effective for detecting those intrusions that have already been encountered and characte...
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TRAFFIC PREDICTION FOR INTELLIGENT TRANSPORTATION SYSTEM USING MACHINE LEARNING
The most vital information about the electrical activities of the brain can be obtained with the help of Electroencephalography (EEG) signals. It is quite a powerful tool to analyze the neural activities of the brain and various neurological disorders like epilepsy, schizophrenia, sleep related disorders, parkinson disease etc. can be investigated ...
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SASDL AND RBATQ: SPARSE AUTOENCODER WITH SWARM BASED DEEP LEARNING AND REINFORCEMENT BASED Q-LEARNING FOR EEG CLASSIFICATION
The most vital information about the electrical activities of the brain can be obtained with the help of Electroencephalography (EEG) signals. It is quite a powerful tool to analyze the neural activities of the brain and various neurological disorders like epilepsy, schizophrenia, sleep related disorders, parkinson disease etc. can be investigated ...
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QUANTIFYING THE ALIGNMENT OF GRAPH AND FEATURES IN DEEP LEARNING
We show that the classification performance of graph convolutional networks (GCNs) is related to the alignment between features, graph, and ground truth, which we quantify using a subspace alignment measure (SAM) corresponding to the Frobenius norm of the matrix of pairwise chordal distances between three subspaces associated with features, graph, ...
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PREDICTION OF 12 PHOTONIC CRYSTAL FIBER OPTICAL PROPERTIES USING MLP IN DEEP LEARNING
Photonic crystal fibers (PCFs) are the specialized optical waveguides that led to many interesting applications ranging from nonlinear optical signal processing to high-power fiber amplifiers. In this paper, machine learning techniques are used to compute various optical properties including effective index, effective mode area, dispersion and conf...
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POLLEN GRAINS CLASSIFICATION WITH A DEEP LEARNING SYSTEM GPU-TRAINED
Traditional approaches to automatic classification of pollen grains consisted of classifiers working with feature extractors designed by experts, which modeled pollen grains aspects of special importance for biologists. Recently, a Deep Learning (DL) algorithm called Convolutional Neural Network (CNN) has shown a great improvement in performance in...
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Multi-Agent Deep Reinforcement Learning-Empowered Channel Allocation in Vehicular Networks
With the rapid development of vehicular networks, vehicle-to-everything (V2X) communications have huge number of tasks to be calculated, which brings challenges to the scarce network resources. Cloud servers can alleviate the terrible situation regarding the lack of computing abilities of vehicular user equipment (VUE), but the limited resources, t...
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MODULARIZING DEEP LEARNING VIA PAIRWISE LEARNING WITH KERNELS
By redefining the conventional notions of layers, we present an alternative view on finitely wide, fully trainable deep neural networks as stacked linear models in feature spaces, leading to a kernel machine interpretation. Based on this construction, we then propose a provably optimal modular learning framework for classification that does not req...
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MACHINE LEARNING AND DEEP LEARNING APPROACHES FOR CYBERSECURITY A
The detection and prevention of a network intrusion is a major concern. Machine Learning and Deep Learning methods detect network intrusions by predicting the risk with the help of training the data. Various machine learning and deep learning methods have been proposed over the years which are shown to be more accurate when compared to other networ...
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LEARNING FROM NOISY DATA AN UNSUPERVISED RANDOM DENOISING METHOD FOR SEISMIC DATA USING MODEL-BASED DEEP LEARNING
For the noise removal problem of noisy seismic data, an improved noise reduction technique based on feedforward denoising neural network (DnCNN) is proposed. The previous DnCNN, which was designed to minimise noise in seismic data, had an issue with a large network depth, which hampered training efficiency. The revised DnCNN technique was previousl...
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IPHOSS(DEEP)-PSEAAC: IDENTIFICATION OF PHOSPHOSERINE SITES IN PROTEINS USING DEEP LEARNING ON GENERAL PSEUDO AMINO ACID COMPOSITIONS
Phosphoaspartate is one of the major components of eukaryotes and prokaryotic two-component signaling pathways, and it communicates the signal from the sensor of histidine kinase, through the response regulator, to the DNA alongside transcription features and initiates the transcription of correct response genes. Thus, the prediction of phosphoaspa...
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INVESTIGATING DEEP LEARNING BASED BREAST CANCER SUBTYPING USING PAN-CANCER AND MULTI-OMIC DATA
Breast Cancer comprises multiple subtypes implicated in prognosis. Existing stratification methods rely on the expression quantification of small gene sets. Next Generation Sequencing promises large amounts of omic data in the next years. In this scenario, we explore the potential of machine learning and, particularly, deep learning for breast canc...
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