Predicting Market Performance Using Machine and Deep Learning Techniques

 Today, forecasting the stock market has been one of the most challenging issues for the ‘‘artificial intelligence’’ AI research community. Stock market investment methods are sophisticated and rely on analyzing massive volumes of data. In recent years, machine-learning techniques have come under increasing scrutiny to assess and improve market pred...

Predicting Market Performance Deep Learning Stock Market Prediction Predicting Economic Trends Forecasting Stock Market Prices

Deep Learning for Credit Card Fraud Detection: A Review of Algorithms, Challenges, and Solutions

 Deep learning (DL), a branch of machine learning (ML), is the core technology in today’s technological advancements and innovations. Deep learning-based approaches are the state-of-the-art methods used to analyse and detect complex patterns in large datasets, such as credit card transactions. However, most credit card fraud models in the literatu...

Credit Card Fraud Detection Deep Learning for Credit Card Fraud Detection Deep Learning Artificial intelligent machine learning

Automatic detection of students’ engagement during online learning: A bagging ensemble deep learning approach

 The COVID-19 pandemic has reshaped education and shifted learning from in-person to online. While this shift offers advantages such as liberating the learning process from time and space constraints and enabling education to occur anywhere and anytime, a challenge lies in detecting student engagement during online learning due to limited interactio...

students engagement online learning deep learning Student online learning system online class training

Advancing Healthcare and Elderly Activity Recognition: Active Machine and Deep Learning for FineGrained Heterogeneity Activity Recognition

 This research explores the potential of technologies in human activity recognition among the elderly population. More precisely, using sensor data and implementing Active Learning (AL), Machine Learning (ML), and Deep learning (DL) techniques for elderly activity recognition. Moreover, the study leverages the HAR70+ dataset, providing insight int...

Healthcare Elderly Activity Recognition Deep Learning Heterogeneity Activity Recognition machine learning

cyberbullying detection on social media texts

 Cyberbullying is when someone is bullied using technology as an intermediary. Despite the fact that it has been a problem for many years, the impact on young people has just recently become more widely recognized. Bullies thrive on social media platforms, and teens and children who use them are vulnerable to attacks. A copious amount of usergener...

Automatic detection of cyberbullying in social media Cyberbullying Detection on Social Networks Accurate Cyberbullying Detection and Prevention Guidelines for Detecting Cyberbullying in Social Media Cyberbullying Detection and Classification

Development of AI-ML based models for predicting prices of agri-horticultural commodities such as pulses and vegetable (onion, potato, onion)

 The Department of Consumer Affairs monitors the daily prices of 22 essential food commodities through 550 price reporting centres across the country. The Department also maintains buffer stock of pulses, viz., gram, tur, urad, moon and masur, and onion for strategic market interventions to stabilize the volatility in prices. Decisions for market ...

AI-ML based models for predicting prices of agri-horticultural commodities Machine learning techniques for forecasting agricultural Machine Learning for Price Prediction for Agricultural AI & ML revolutionizing crop price prediction in agriculture Unlocking the Power of AI-ML for Agri-Commodity Prices

Analysis of Stock Market Prediction

 Stock marketplace is a complicated and demanding system in which people make more money or lose their entire savings. The stock market prediction having high accuracy yields more profit for stock investors. Stock market data is generated in a very large amount and it varies quickly every second. The decision making in stock marketplace is a very ...

Stock Market Stock Market Prediction Long Short Term Memory Convolution Neural Networks Mean Absolute Error

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. ...

LSTM Machine learning (ML) Long Short-Term Memory (LSTM) Random Forest Regressor model Recurrent Neural Network

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...

deep Q-network resource allocation algorithm DRL algorithms secure network coding Secure cloud storage

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...

Secure cloud storage Privacy Data Processing Data mining Cryptography SHA Algorithum

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...

MACHINE LEARNING THE HIDDEN SEXUAL MINORITIES

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...

MACHINE LEARNING PREDICTING BRAIN AGE

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...

MACHINE LEARNING SELF-ORGANIZING

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...

MACHINE LEARNING BIG DATA ANALYTICS APPLICATIONS

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 ...

DEEP LEARNING TRAFFIC PREDICTION

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 ...

DEEP LEARNING SASDL AND RBATQ

INFERENCE OF BRAIN STATES UNDER ANESTHESIA WITH META LEARNING BASED DEEP LEARNING MODELS

 Monitoringthe depth of unconsciousnessduring anesthesia is beneficial in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram(EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time. Different general anestheti...

INFERENCE OF BRAIN STATES UNDER ANESTHESIA WITH META LEARNING BASED DEEP LEARNING MODELS

 Monitoringthe depth of unconsciousnessduring anesthesia is beneficial in both clinical settings and neuroscience investigations to understand brain mechanisms. Electroencephalogram(EEG) has been used as an objective means of characterizing brain altered arousal and/or cognition states induced by anesthetics in real-time. Different general anestheti...

DEEP LEARNING META LEARNING BASED

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 ...

DEEP LEARNING REINFORCEMENT BASED Q-LEARNING

PREDICTION OF CHRONIC KIDNEY DISEASE USING DEEP NEURAL NETWORK

 Deep neural Network (DNN) is becoming a focal point in Machine Learning research. Its application is penetrating into different fields and solving intricate and complex problems. DNN is now been applied in health image processing to detect various ailment such as cancer and diabetes. Another disease that is causing threat to our health is the kidne...

ONLINE DEEP REINFORCEMENT LEARNING FOR COMPUTATION OFFLOADING IN BLOCKCHAIN-EMPOWERED MOBILE EDGE COMPUTING

 Offloading computation-intensive tasks (e.g., blockchain consensus processes and data processing tasks) to the edge/cloud is a promising solution for blockchain-empowered mobile edge computing. However, the traditional offloading approaches (e.g., auction-based and game-theory approaches) fail to adjust the policy according to the changing environ...

BLOCK CHAIN BASED SECURED DATA TRANSFER IN MOBILE CLOUD COMPUTING

 Offloading computation-intensive tasks (e.g., block chain agreement cycles and information handling undertakings) to the edge/cloud is a promising answer for block chain-enabled versatile edge figuring. Nonetheless, the conventional offloading approaches (e.g., sell off based and game-hypothesis draws near) neglect to change the strategy as per the...

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...

Low-Power ECG-Based Processor for Predicting Ventricular Arrhythmia

 This paper presents the design of a fully integrated electrocardiogram (ECG) signal processor (ESP) for the prediction of ventricular arrhythmia using a unique set of ECG features and a naive Bayes classifier. Real-time and adaptive techniques for the detection and the delineation of the P-QRS-T waves were investigated to extract the fiducial point...

Home Outlet and LED Array Lamp Controlled by a Smartphone with a Hand Gesture Recognition

  In this paper, we design a home outlet and a LED array lamp controlled by hand gesture recognition with a smart phone that has a system composed of two parts: a smart phone's application and a wireless remote control unit (WRCU). The application can read the accelerometer and gyroscope in a smart phone by means of hand gesture recognition and s...

A Wireless Sensor Interface for the Quantification of Tremor Using Off the Shelf Components

 Deep brain stimulation (DBS) surgery involves placing an electrode in the subthalamic nucleus to suppress the motor symptoms, such as tremor, of patients with Parkinson's disease (PD). Currently physicians use the standard Unified Parkinson's Disease Rating Scale (UPDRS) to describe the tremor intraoperatively and post operatively. This scale invol...

Edge LSTM Towards Deep and Sequential Edge Computing for IoT Applications

 The time series data generated by massive sensors in Internet of Things (IoT) is extremely dynamic, heterogeneous, large scale and time-dependent. It poses great challenges (e.g. accuracy, reliability, stability) on the real-time analysis and decision making for different IoT applications. In this paper, we design, implement and evaluate EdgeLSTM, ...

A Cooperative Defense Framework against Application-level DDoS Attacks on Mobile Edge Computing Services

 Mobile edge computing (MEC), extending computing services from cloud to edge, is recognized as one of key pillars to facilitate real-time services and tackle backhaul bottleneck. However, it is not economically efficient to attach intensive security appliances to every MEC node to defend application-level DDoS attacks and ensure the availability of...

A Novel Statistical and Neural Network Combined Approach for the Cloud Spot Market

 The instance price in the Amazon EC2 spot model is often much lower than in the on-demand counterpart. However, this price reduction comes with a decrease in the availability guarantees. To our knowledge, there is no work that accurately captures the short-term trade-off between spot price and availability, and does long-term analysis for spot pric...

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...

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...

Simplified Deep Forest Model Based Just-in-Time Defect Prediction for Android Mobile Apps

  The popularity of mobile devices has led to an explosive growth in the number of mobile apps in which Android mobile apps are the mainstream. Android mobile apps usually undergo frequent update due to new requirements proposed by users. Just-In-Time (JIT) defect prediction is appropriate for this scenario for quality assurance because it can pr...

A Hybrid Deep Network Framework for Android Malware Detection

 Andriod malware poses a serious threat to users privacy, money, equipment and file integrity. A series of data-driven malware detection methods were proposed. However, there exist two key challenges for these methods: (1) how to learn effective feature representation from raw data; (2) how to reduce the dependence on the prior knowledge or human la...

Leveraging Graph Convolutional-LSTM for Energy Efficient Caching in Blockchain-based Green IoT

 Nowadays, adopting blockchain technology to Internet of Things has become a trend and it is important to minimize energy consumption while providing a high quality of service (QoS) in Blockchain-based IoT networks. Pre-caching popular and fresh IoT content avoids activating sensors frequently, thus effectively reducing network energy consumption. H...

Context-aware Service Recommendation based on Knowledge Graph Embedding

 As a class of context-aware systems, context-aware service recommendation aims to bind high-quality services to users while taking into account their context requirements, including invocation time, location, social profiles, connectivity, and so on. However, current CASR approaches are not scalable with the huge amount of service data (QoS and con...

GCN2CDD A Commercial District Discovery Framework via Embedding Space Clustering on Graph Convolution Networks

 Modern enterprises attach much attention to the selection of commercial locations. With the rapid development of urban data and machine learning, we can discover the patterns of human mobility with these data and technology to guide commercial district discovery. In this paper, we propose an unsupervised commercial district discovery framework via ...

Fast Power System Event Identification using Enhanced LSTM Network with Renewable Energy Integration

  Accurate and fast event identification in power systems is critical for taking timely controls to avoid instability. In this paper, a synchrophasor measurementbased fast and robust event identification method is proposed considering different penetration levels of renewable energy. A difference Teager-Kaiser energy operator (dTKEO)-based algori...

Open Named Entity Modeling from Embedding Distribution

  In this paper, we report our discovery on named entity distribution in a general word embedding space, which helps an open definition on multilingual named entity definition rather than previous closed and constraint definition on named entities through a named entity dictionary, which is usually derived from human labor and replies on schedule...

Codee A Tensor Embedding Scheme for Binary Code Search

  Given a target binary function, the binary code search retrieves top-K similar functions in the repository, and similar functions represent that they are compiled from the same source codes. Searching binary code is particularly challenging due to large variations of compiler tool-chains and options and CPU architectures, as well as thousands o...

LYRIC Deadline and Budget Aware Spatio-Temporal Query Processing in Cloud

 With the enormous growth of wireless technology, and location acquisition techniques, a huge amount of spatio-temporal traces are being accumulated. This dataset facilitates varied location-aware services and helps to take real-life decisions. Efficiently handling and processing spatio-temporal queries are necessary to respond in real-time. Process...

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...

Utilizing Players’ Playtime Records for Churn Prediction Mining Playtime Regularity

 Churn prediction is an important topic in the freeonline game industry. Reducing the churn rate of a game signifi-cantly helps with the success of the game. Churn prediction helpsa game operator identify possible churning players and keep them engaged in the game via appropriate operational strategies, marketing strategies, and/or incentives. Play...

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...

A Dynamic Deep-learning-based Virtual Edge Node Placement Scheme for Edge Cloud Systems in Mobile Environment

 Edge node placement is a key topic to edge cloud systems for that it affects their service performances significantly. Previous solutions based on the existing information are not suitable for the mobile environment due to the mobility and random Internet access of end users. In this paper, we propose a dynamic virtual edge node placement scheme, i...