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88 articles for “long-term memory”
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A Comprehensive Review on Federated Learning in Disease Detection
Abstract: Healthcare data, which is frequently dispersed among various organisations, has enormous potential to improve predictive analytics and illness identification. However, there are substantial privacy & legal obstacles to sharing this private data for centralised model training. Federated Learning is a paradigm shift that allows several organisations to work together to build a global model without disclosing raw patient information. Federated Learning uses a larger dataset to provide more reliable insights …
Published in Emerging Trends in Personalized Medicines · Vol. 3, Issue 1, 2026 · pp. 1–21 Read article
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Multi-Parameter Biomedical Sensor-Based Mental State Classification Using EEG And Deep Learning Techniques
Abstract: With mental health concerns becoming increasingly widespread, there is a strong need for systems that can monitor conditions like stress, anxiety, and fatigue in a continuous and non- invasive manner. This research proposes a novel multi-parameter biomedical sensing framework for mental state classification by integrating electroencephalography (EEG) signals with physiological parameters, including body temperature acquired using LM35 sensors, heart rate from pulse sensors, and blood oxygen saturation (SpO₂) measurements. The …
Published in Current Trends in Signal Processing · Vol. 16, Issue 2, 2026 · pp. 1–8 Read article
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Tensor-Flow Based Approach to Identify Author of the Text
Abstract: Now-a-days a lot of content is available on internet, and people upload lot of information in form of opinion, review, description, recipe etc. online. In such scenario to trace the authenticity of the data, it is necessary to develop an author identification system. It has become a difficult problem in the scope of unnamed information has increased with fast growing Internet life. It is a process to identify author of …
Published in Current Trends in Information Technology · Vol. 8, Issue 3, 2018 · pp. 23–29 Read article
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AI Enabled Pharma Supply Chain Market
Abstract: Pharmaceutical supply chains are changing because of artificial intelligence (AI). The pharmaceutical industry has always had to deal with uncertainty. From the start of research to the final delivery of medicines to patients, it faces scientific risks, government rules, and supply chain problems. In recent years, these challenges have gotten worse because there is more drug shortage, political issues are messing up transportation, and regulators are asking for more openness …
Published in Research and Reviews: A Journal of Pharmaceutical Science · Vol. 17, Issue 2, 2026 Read article
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Enhancing Language Modelling with RNN and LSTMbased Next Word Prediction
Abstract: A growing field of study, natural language processing is utilised in many different contexts. We frequently text each other, and we have noticed that whenever we type a message, a recommendation pops up to try to guess what word we will write next. The task of anticipating the next word is known as next word prediction or language modelling. This makes them highly effective for tasks such as language modelling, …
Published in Journal of Advanced Database Management & Systems · Vol. 10, Issue 1, 2023 · pp. 20–24 Read article
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Advancements in Intrusion Detection: Tackling Imbalanced Network Traffic with Machine Learning and Deep Learning Techniques
Abstract: Malicious cyberattacks can frequently hide enormous amounts of typical data in unbalanced network traffic. It is very stealthy and obfuscating in cyberspace, which makes it challenging for Network Intrusion Detection Systems (NIDS) to guarantee the precision and promptness of detection. This essay investigates. Machine learning and deep learning are utilized for intrusion detection in imbalanced network traffic. It offers a novel method for addressing the problem of class imbalance termed …
Published in Recent Trends in Electronics Communication Systems · Vol. 11, Issue 2, 2024 · pp. 18–24 Read article
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Hoax News Detection Using Convolutional Neural Network
Abstract: In this paper our objective is to build a classifier that can predict whether a piece of news is Hoax or not based only its content, thereby approaching the problem from a purely deep learning perspective by technique models like LSTM(GRU), Naive Bayes and CNN. We will show the difference and analysis of results by applying them to the dataset Hoax_news which is available on kaggle.com. We found that the …
Published in Journal of Operating Systems Development & Trends · Vol. 7, Issue 2, 2020 · pp. 19–23 Read article
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Intelligent Paradigms in Subsea Connectivity: A Comprehensive Review of Artificial Intelligence in Underwater Communications
Abstract: Underwater wireless communication (UWC) plays a critical role in ocean exploration, environmental monitoring, offshore energy operations, disaster management, and naval defense. However, the underwater environment presents significant communication challenges, including severe signal attenuation, multipath propagation, Doppler effects, limited bandwidth, high latency, and energy constraints. Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) have emerged as promising solutions to address these limitations and enhance the efficiency, reliability, and adaptability …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
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Artificial Intelligence in Cerebellum Activation
Abstract: Neuroscience plays a significant function during the progression of artificial intelligence. It provided inspiration for the development of human-like AI. There are two ways that neuroscience encourages us to develop AI systems. Neural networks that replicate human cognition and those that match the structure of the brain are the two objectives. Neural networks, which draw inspiration from the architecture of the human brain, are the engine behind contemporary artificial intelligence …
Published in International Journal of Cheminformatics · Vol. 1, Issue 1, 2023 · pp. 14–26 Read article
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Study of Social Trends Prediction Using AI
Abstract: AI (Artificial Intelligence) has fundamentally changed the ability to analyze social trends by using large datasets to develop predictions about human behavior, public sentiment, and global events. Using methodologies such as Natural Language Processing (NLP), Time-Series Forecasting, and Graph-Based Social Network Analysis, AI is able to find hidden correlations in a variety of available datasets, from social media to economic indicators to public records, and fundamentally changes decision-making based on …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 3, 2025 · pp. 19–29 Read article
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AI Driven IoT Based Decision Making System for Brain wave study: KSK approach for Brain wave study
Abstract: The rapid convergence of Internet of Things (IoT) architectures and deep learning has unlocked unprecedented potential for real-time neuro-diagnostic monitoring. This paper presents a novel framework for an AI-driven IoT ecosystem designed to capture, transmit, and interpret human electroencephalography (EEG) signals with minimal latency. Traditional brain-computer interface (BCI) studies are often constrained by localized computing power and the high dimensionality of neural data. Our proposed architecture integrates low-power EEG sensors …
Published in International Journal of Brain Sciences · Vol. 3, Issue 2, 2026 Read article
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Bias Detection and Accuracy Enhancement in Voice-based Banking Authentication Using Deep Learning
Abstract: Biometric systems have become an integral part of how many people access banking services today, and voice verification systems can be a secure and easy-to-use source of banking authentication that does not require any physical contact with the bank or any other person. From the security perspective, these systems would normally provide an effective means of identifying an individual but frequently exhibit bias with respect to demographics such as the …
Published in International Journal of Information Security Engineering · Vol. 4, Issue 2, 2026 Read article
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Wind Forecasting Using Various Neural Networks in Machine Learning
Abstract: Wind Power Forecasting, as the name applies is a process in which data of past is used to tell what kind of output can be expected from a wind turbine in the foreseeable future. Machine learning, can be said to be a derivation of artificial intelligence that makes it possible for the system to learn automatically and improve upon itself from faults, without needing to tell the system to do …
Published in Journal of Telecommunication, Switching Systems and Networks · Vol. 8, Issue 2, 2021 · pp. 32–43 Read article
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Website Summarization Using Deep Learning
Abstract: They are a lot of websites, articles and blogs on the internet with a lot of textual information it is sometimes impossible to read all of them, without knowing the underlying summary of the whole text. The whole concept is to reduce or minimize the entire Textual information into a small summary of important information present in the documents so that we get an idea of what the blog or …
Published in Recent Trends in Electronics Communication Systems · Vol. 9, Issue 2, 2022 · pp. 7–12 Read article
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Melody Extraction from Polyphonic Music Using Deep Neural Network: A Literature Survey
Abstract: Abstract: Melody extraction plays an important role in the field of Music Information Retrieval (MIR). It has emerged as one of the active research problems in the MIR applications. Nowadays, the music providers have to facilitate searching of music based on their contents or recommend music based on user’s interest having similar contents. Melody extraction is necessary to fulfil these user-interest driven searching and recommendation. The main objective of melody …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 6, Issue 3, 2019 · pp. 16–21 Read article
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Cutting-edge Deep Learning Methods for Predicting and Detecting Cardiovascular Diseases
Abstract: Cardiovascular diseases (CVDs) remain a major global health issue, highlighting the need for improved early detection and risk assessment methods. This research investigates the efficacy of both deep learning and traditional machine learning methods in forecasting cardiovascular diseases (CVDs). We evaluate a variety of models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Multilayer Perceptrons (MLPs), Long Short-Term Memory (LSTM) networks, as well as Logistic Regression (LR), Decision Trees …
Published in Recent Trends in Parallel Computing · Vol. 11, Issue 2, 2024 · pp. 36–42 Read article
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Detecting Phishing Websites Using Hybrid Methodologies
Abstract: In the digital era, personal information theft has become a widespread and increasingly severe crime. Cybercriminals, often known as hackers, use deceptive strategies, with phishing websites being a major method for stealing confidential data. These fake websites imitate legitimate ones, tricking users into revealing sensitive personal and financial information, which has led to a rise in fraud cases. To address this escalating threat, a comprehensive research paper is proposed. This …
Published in Journal of Computer Technology & Applications · Vol. 15, Issue 3, 2024 · pp. 59–65 Read article
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Human Skin Abnormality Detection with Process Similarity Criteria Fit Machine Learning Method
Abstract: This method presents a machine learning method that satisfies the defined conditions for healthy waterside beach activities. The boundary conditions of the normal and abnormal radiation spaces were formulated. The objectives of using a Regression Polynomial with Process Similarity Criteria Fit for skin temperature prediction are justified by the analysis of the existing analytical and machine learning approaches. An algorithm for skin temperature prediction using the theories of similarity criteria …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 11, Issue 2, 2024 · pp. 17–24 Read article
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Data-Driven Life Prediction of Fiber-Reinforced Polymer Composites Using IoT Sensing and Machine Learning Algorithms
Abstract: The accurate prediction of fatigue life in fiber-reinforced polymer (FRP) composites remains a major challenge due to their nonlinear, multi-mechanism degradation behavior under variable loading conditions. This study presents a data-driven framework, H-LiProNet, which combines real-time IoT sensing with hybrid machine learning to estimate remaining useful life (RUL) in FRP composites. The proposed system integrates embedded Fiber Bragg Grating (FBG) and acoustic emission (AE) sensors to capture strain and damage …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 116–130 Read article
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A Comparative Study of Deep Learning Methods for Depression Detection in Social Media Data
Abstract: With the rise of social media platforms like Twitter, Reddit, and Facebook, individuals increasingly share personal information about their moods, behaviors, and mental states. This trend provides a unique opportunity to leverage large-scale textual data for understanding and monitoring mental health conditions, particularly depression, a prevalent and challenging mental health issue. Traditional depression assessments are often confined to clinical environments and lack the capacity for real-time monitoring. In contrast, social …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 3, 2025 · pp. 55–65 Read article