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7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Asha, P. Adithya, S. Aijith Prabhakar, U.T. Ankayarkanni, B. Anto Praveena, M.D. Padma Suresh, L. Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India Baselios Mathews II College of Engineering Computer Science and Engineering Kollam India

ISBN: (纸本)9798350372816

Music genre classification, the task of automatically assigning audio recordings to specific genres, offers exciting possibilities for music recommendation systems, personalized playlists, and music analysis. This abstract explores the potential of (K-NN), a versatile Artificial Intelligence (AI) algorithm, in tackling this challenge- NN excels in its simplicity and interpretability. It classifies new music by identifying its closest neighbors within a labeled dataset based on predefined features, such as tempo, timbre, or spectral content. These neighbors then collectively "vote"on the genre of the new piece. The abstract delves into the feature engineering process, highlighting the crucial role of selecting relevant musical characteristics for accurate classification. Furthermore, the abstract discusses the impact of the "K"hyper parameter, which determines the number of neighbors considered in the voting process. Choosing the optimal "K"value balances accuracy and sensitivity to genre nuances. While K-NN offers advantages in ease of implementation and understanding, the abstract acknowledges its potential drawbacks. The storage requirements can be high for large datasets, and computational costs may increase with data size. It concludes by acknowledging the need for further research to address computational challenges and explore advanced feature engineering techniques to unlock the full potential of K-NN in this exciting domain. © 2024 IEEE.

关键词： Audio recordings

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Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition

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SN computer science 2024年第5期5卷 1-10页

作者： Duhayyim, Mesfer Al Sharif, Mahir Mohammed Munjal, Muskaan Hilal, Anwer A. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj 16273 Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan

Sleep quality prediction in Internet of Things (IoT) involves leveraging a system of interrelated devices to gather as well as analyse related data. Smart devices like wearable devices or smart mattresses endlessly monitor many parameters such as body movement, heart rate, and environmental situations in bedroom. Real-time recommendations and feedback transported to consumers, allowing them to create informed lifestyle changes for superior sleep hygiene. As IoT ecosystem continues to develop, a combination of various data streams as well as classy analytics holds assurance for further refining sleep quality forecasts and improving overall well-being. The article designs a Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition (BESHDL-SQP) technique. The purpose of the BESHDL-SQP technique is to recognize and classify the quality of the sleep level in the IoT environment. In the presented BESHDL-SQP technique, the preliminary stage of data pre-processing using min–max normalization involved to normalize the input data. For predicting sleep quality, hierarchical long short-term memory (LSTM) technique exploited. Lastly, BES model utilized for optimal hyperparameter selection of LSTM method which helps in attaining enhanced results. To validate better performance of BESHDL-SQP technique, a huge range of simulation analysis is carried out. The extensive results stated that the BESHDL-SQP technique exhibits supreme performance over other models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bald eagle search Internet of things LSTM Sleep quality recognition Smart devices

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Depression detection via conversation turn classification

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Multimedia Tools and Applications 2023年第25期82卷 39393-39413页

作者： Lu, Kuan-Chieh Thamrin, Syauki Aulia Chen, Arbee L. P. Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering Asia University Taichung Taiwan

The fast pace of modern life caused people to experience more pressure from their surrounding environments. As a result, depression has emerged as one of the most common diseases. To detect depression, psychiatrists need to interview patients to understand their mental health condition. However, it may not fully reflect the condition of the patient since some people cannot adequately express their feelings. As an alternative, researchers work on automated depression detection. Our work aims to improve the detection capability by extracting knowledge from psychiatric consultation. Previous studies classified the questions and answers in the interview into categories unrelated directly to depression. In this paper, we implement a novel categorization based on the Patient Health Questionnaire-8 (PHQ-8). The PHQ-8 is commonly used as a screening tool to identify the severity of depression, making it more reliable for detecting depression. Transcribed data is processed using contextual word embedding, BERT, to learn semantic information. In the final step, a transformer encoder is used to get a higher-level representation of turns. Using categorization based on PHQ-8 and BERT as word embedding, we propose a novel deep learning framework with two-level encoding for detecting depression. In the training step, we use Distress Analysis Interview Corpus (DAIC) dataset, a transcript of the interview data. The model is evaluated with different adjustments to achieve the best performance. Various experiments are designed to show the effectiveness of our method for detecting depression from the psychiatric consultation dataset. As a result, our model performs better than others using the same dataset, achieving 0.76 F1-score. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Embeddings

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Rumor detection from online social media through feature selection and machine learning

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Multimedia Tools and Applications 2025年 1-25页

作者： Gurrapu, Neelima Baydeti, Nagaraju Department of Computer Science and Engineering National Institute of Technology Nagaland Nagaland Chumukedima797 103 India

Recently, Rumor Spreading over Online Social Media is found as one of the serious issue, which causes severe damage to society, organization and individuals. To control the rumor spread, rumor detection is found as one of the probable solution and gained huge interest in the research community. However, most of the past methods characterize the rumormonger behavior based on their own profiles and completely neglected the reputation features. A hybrid method for rumor identification by combining the user reputation feature with other features like user features, meta-content based features and linguistic features is proposed. The major novelty lies at the representation of users through their interaction with his/her followers in different communities. The own profile based features can be evaded easily but the reputation of follows cannot be evaded easily. Totally 44 features are used to characterize each tweet and trained through different classifiers. Four classifiers namely K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are used to train the system and the performance is analyzed through two standard datasets namely Zubiaga and Kwon. The obtained results show that the proposed method can recognize rumor with higher F1-score. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature Selection

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Statistical assessment of Indian classical music’s role in diabetes, thyroid, hypertension management

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International Journal of Information Technology (Singapore) 2025年第1期17卷 423-429页

作者： Pingle, Yogesh Prabhakar Ragha, Lakshmappa K. Research Scholar at Department of Computer Engineering Terna Engineering College Assistant Professor at Department of Computer Science and Engineering (Data Science) Vidyavardhinis College of Engineering and Technology University of Mumbai Maharashtra Mumbai India Department of Computer Engineering Terna Engineering College University of Mumbai Maharashtra Mumbai India

This multi-center randomized controlled trial explores the therapeutic benefits of Indian classical music, specifically “Raga Therapy,” for managing diabetes, thyroid disorders, and hypertension—prevalent global health issues. The study examines how specific Ragas can enhance traditional treatments: Bhimpalas and Chhanayat for hypertension, Malkauns and Bhupal Todi for diabetes, and Shyam Kalyan and Yaman for thyroid disorders. Results show significant health improvements in patients integrating Raga Therapy into their care, highlighting its holistic benefits. The study underscores the profound connection between music and healing, positioning Indian classical music as a valuable complementary therapy. By addressing a critical gap in the literature, it advocates for further research to understand the mechanisms involved and refine Raga selection for optimal therapeutic outcomes. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Paired t-test Patient satisfaction survey Raga Therapy Wilcoxon signed-rank test

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GWO-CNN: Trust and Intelligent Recommendation system based model to improve marketing strategies on e-commerce 2

GWO-CNN: Trust and Intelligent Recommendation system based m...

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2nd IEEE International Conference for Women in Innovation, Technology and Entrepreneurship, ICWITE 2024

作者： Sethi, Vikas Kumar, Rajneesh Mehla, Stuti Department of Computer Science and Engineering Mullana Ambala India Panipat Institute of Engineering and Technology Department of Computer Science and Engineering Panipat India

ISBN: (纸本)9798350383287

In 21st century, AI-based intelligent recommendation system uses rating predictions, which are frequently utilized and helps users swiftly filter down their options and make informed judgements from an abundance of material. Nevertheless, as recommender systems only contain a small number of explicit data and are subject to cold start and sparsity issues, the majority of existing recommendation models have poor accuracy. The majority of conventional algorithms used to make recommendations simply take into account the straight-forward connection among consumers and products, largely ignoring the interests, prospective employment factors, or other social aspects of the products being *** this study focus is on user preferences, as they are key to tailoring recommendations to individual needs. By combining trust values with user preferences, the system can recommend items that not only have a high trust value but also align with a user's individual tastes or needs. For this proposed model used feature matrix, Grey Wolf Optimizer (GWO) and Convolution Neural Network (CNN). GWO is an optimization algorithm used to find optimal weights or parameters for the recommendation system. Once the GWO has found optimal or near-optimal weights, they are integrated into the feature matrix. These weights can help prioritize or scale certain features to improve the recommendation quality. Then CNN will extract patterns and relationships from the feature matrix. Based on the processed feature matrix, the system predicts the top 'K' items to recommend to the user. © 2024 IEEE.

关键词： Recommender systems

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Voice Controlled Smart Home System 2

Voice Controlled Smart Home System

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Manjula, K. Dhananjaya, V. Computer Science & Engineering Nitte Meenakshi Institute of Technology Bangalore India Computer Science & Engineering Impact College of Engineering Bangalore India

ISBN: (纸本)9798350372892

The Internet of Things (IoT) is a significant technological advancement that uses the internet to enable seamless communication between various devices, allowing people and objects to connect anytime and from anywhere. In this research, we present an intelligent smart automation model that can remotely operate household appliances and water the garden plants from anywhere in the world. 'Home automation' is a quickly emerging technology that minimizes the work that humans must perform. Certain home automation systems are offered to people seeking luxurious, advanced home automation systems. This method is safe because it works best for the elderly and disabled people. However, certain people could find it more challenging to use certain systems because of their special demands. The system's primary features are voice recognition and intelligent interaction. Our main objective is to develop an advanced home automation system that has combined Alexa with the ESP 8266 module to improve security, reduce power consumption and minimize user effort. The model includes two essential sensors, a soil moisture sensor, a temperature and humidity sensor. All hardware components in this model are cost-effective, readily available, and easily replaceable. © 2024 IEEE.

关键词： Soil moisture

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3GPP Edge–Fog federation: Transparent 3rd-party authentication and application mobility

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computer Communications 2024年 222卷 220-229页

作者： Ali, Asad Islam, Minhajul Mallick, Tushin Islam, Mohammad Sakibul Sakib, Sadman Shohrab Hossain, Md. Lin, Ying-Dar National Institute of Cyber Security Ministry of Digital Affairs Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Bangladesh

3GPP edge and fog computing paradigms provide computational services to users at low latency. These paradigms alone are not enough to fulfill the users’ requirements completely. Therefore, a federation among these computing paradigms is necessary. To realize such federation, there is a need of an authentication mechanism where subscribers of a fog, can access 3GPP edge's services, or vice versa, without buying new subscription, and an application mobility mechanism for continuous service during handover from 3GPP edge to fog, or vice versa, without re-authentication. In this work, we propose: (1) a proxy-based state transfer and third-party authentication (PS3A), that uses a transparent proxy to transfer the authentication and application state information between 3GPP edge and fog, and (2) a token-based state transfer and proxy-based third-party authentication (TSP3A), that uses the proxy to transfer the authentication information and tokens to transfer application state information between 3GPP edge and fog. The proxy plays different roles, via virtual counterparts of entities involved in these protocols, to provide transparency. When the 3GPP edge, using EPS-AKA, receives an authentication request, the proxy relays and behaves as a virtual Home Subscriber Server (vHSS) for the 3GPP edge, and behaves as a virtual user for the fog, which is using OIDC. We applied PS3A and TSP3A to three federation scenarios among 3GPP edge and fog. Experimental results show that PS3A and TSP3A provide authentication within 0.345–2.858 s for a 0–100 Mbps proxy load. The results further show that TSP3A provides application mobility, while taking 40%–52% less time than PS3A, using state tokens. TSP3A and PS3A also reduce the service interruption latency by 82.4% and 84.6%, compared to the cloud-based service, via tokens and prefetching. © 2024 Elsevier B.V.

关键词： Authentication

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Automotive Visible-Light Communication: Alternative Devices and Systems

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Tsinghua science and Technology 2023年第4期28卷 719-728页

作者： Lin Cheng Yue Wu Chenren Xu Khadija Ashraf Ashwin Ashok Department of Engineering Trinity CollegeHartfordCT 06106USA Department of Computer Science Yale UniversityNew HavenCT 06520USA Department of Computer Science and Technology Peking UniversityBeijing 100084China Department of Computer Science Georgia State UniversityAtlantaGA 30302USA

Continuous advancements in visible-light communication(VLC)technology have paved the way for future high-capacity communication links that can simultaneously provide data transmission and *** is being accepted as a potential complementary technology in 5G networks,and standardization efforts through IEEE 802.15.7 are on their ***,vehicular networking applications have become increasingly complex with tight power and performance ***,devices and systems that can meet diverse vehicular networking applications are in great *** this article,we discuss three alternatives for vehicular networking applications in(1)LED–photodiode-based active VLC,(2)VLC with a multicamera array receiver,and(3)passive VLC based on decoding information from optical *** also present our recent experimental and modeling work using our camera-based and passive VLC prototype implementations.

关键词： visible-light communications automobile camera communications

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Streamlining Financial Predictions: A Web Application for Stock, Cryptocurrency, and Market Sentiment Analysis 4

Streamlining Financial Predictions: A Web Application for St...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Vivekanand, Chettiyar Vani Kumar, P. Amizhthini, A. Rajalakshmi Engineering College Computer Science and Engineering Chennai India

ISBN: (纸本)9798331523923

Among the high-degree lines of study in this area, one of the most interesting ones is stock price prediction, particularly in the context of machine learning and time series forecasting models. The traditional past ones include ARIMA, and SARIMAX, while deep learning models have been recently applied for predicting the stock prices based upon historical data. However, these models do hold much promise and also come with volatility and non-linearity in financial markets. Causes for such inaccuracies in predictions include sudden market shifts, external economic events, and investor sentiment. All these factors are difficult to model well. This study will propose a web application that uses advanced time series models to try and overcome such challenges. The system is implemented with Streamlit and Flask, so it gives users a smooth interface. It has five critical modules: Nifty 50, which deals with the analysis and forecasting of stocks using ARIMA and SARIMAX models;Global Market Data, which fetches real-time data from the Yahoo Finance API for granular analysis;Cryptocurrency, which relies heavily on Ethereum data with historical analysis and price forecasting;Twitter Sentiment Analysis, which focusses on real-time analysis of market sentiment through tweets;and the prediction module, which evaluates the accuracy of the models using Root Mean Square Error to measure prediction quality. This solution will provide a straightforward yet potent platform for analyzing stocks, cryptocurrency, and sentiment in real-time. The goal is to raise precision, reliability, and overall user experience concerning financial prediction, while more valuable insights become available to the investor and the researcher, having multiple sources integrated into the model. © 2025 IEEE.

关键词： Ethereum

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