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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Datta, Arko Chakraborty, Joyita Majhi, Pabitra Mondal, Astom Roy, Satyaki Nandi, Subrata Saha, Sujoy National Institute of Technology Department of Computer Science and Engineering Durgapur India Dr. B. C. Roy Engineering College Department of Computer Science and Engineering Durgapur India University of Alabama in Huntsville Department of Mathematical Sciences AL United States

ISBN: (纸本)9798331531195

Air pollution is a significant contributor to respiratory diseases, posing severe health risks globally. Traditional air quality monitoring stations, while effective, are too expensive, particularly in developing nations like India, where numerous monitoring sites may be required to cover diverse urban and industrial regions. Existing cost-effective air quality monitoring systems are rendered inadequate by their reliance on sensors that are prone to drift, which necessitates frequent recalibration. This study addresses the gap by developing a cost-effective air quality monitoring device capable of detecting particulate matter, carbon monoxide (CO), temperature, and humidity, and incorporating self-diagnostic capabilities. It enhances reliability through a dual-sensing mechanism: it calculates the Air Quality Index (AQI) directly and uses a TinyML model to provide secondary-sensed AQI estimations. Deviations between these two AQI values signal sensor malfunction, which is tracked by a proposed algorithm, Interocept, that assigns fault scores to the sensors. Models were created for eight cities, showing accuracies from 0.69-0.89 for dust and 0.76-0.95 for CO. These models were optimized for real-time performance by converting them into TensorFlow Lite micro-models, allowing deployment on the device itself for continuous, autonomous sensor health diagnostics and improved long-term air quality management systems. © 2025 IEEE.

关键词： Fault detection

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Autonomous Robot Navigation in Unknown Terrains using Generative Adversarial Networks 4

Autonomous Robot Navigation in Unknown Terrains using Genera...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Chitra, P. Sridar, K. Govindan, Sudha Britto Raj, S. Akshya, J. Choudhry, Mani Deepak Sundarrajan, M. St Joseph's Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Veerammal Engineering College Department of Computer Science and Engineering Dindigul India Sona College of Technology Department of Computer Science and Engineering Salem India RRASE College of Engineering Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Computational Intelligence Chennai India SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798331515720

Autonomous Robot Navigation in unknown and dynamic environments is considered one of the grand challenges in robotics. These motions that guide it span from rescue operations to planetary exploration. Classical methods for path planning, including A*, Dijkstra, and RRT, were unable to balance the efficiency and adaptability of motion planning over complex terrain. This may have a powerful solution using Generative Adversarial Networks (GANs) with advanced machine learning techniques to improve the ability of robotic navigation. This study proposes a GAN-based approach for designing an effective self-adaptive path planning and obstacle avoidance system for autonomous robots. Despite performing well in structured environments, traditional techniques normally fail in analysing the unstructured terrains with real-time adjustments and dynamic changes due to the high computational costs and lack of flexibility involved. In this regard, the proposed GAN-based system resolves these issues by rating navigation paths concerning safety and efficiency. Quantitative analysis demonstrates that within a forest, desert, urban, or off-road environment, the computational times by using the developed strategy turn out to be 0.12 seconds, 0.10 seconds, 0.14 seconds, and 0.13 seconds, respectively, behaving much lower than what can be achieved with other algorithms. This approach proves much superior in terms of computational efficiency path smoothness and adaptability under different environmental conditions in applications related to autonomous robot navigation systems. © 2024 IEEE.

关键词： Generative adversarial networks

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Agricultural repercussions caused by the pandemic period

Agricultural repercussions caused by the pandemic period

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Sheeja Kumari, V. Seema Dev Aksatha, D. Michael, G. Vennira Selvi, G. Manikandan, T. Gururama Senthilvel, P. Department of Computational Intelligence – Institute of AI & ML SIMATS School of Engineering SIMATS University Chennai India Department of Computer Science Sri Krishna Arts and Science College Coimbatore India Department of Computer Science and Engineering SIMATS School of Engineering SIMATS University Chennai India

ISBN: (纸本)9781032669663

COVID-19 has affected India’s agriculture system. However, the latest quarterly GDP predictions post-COVID scenario show the resilience of the Indian agricultural sector, which is expected to be the sole sector to expand 4% for fiscal years. In this context, the major purpose is to assemble a summary of early evidence on COVID-19‘s influence on India’s agriculture industry, including production, marketing, and consumption, and then devise pragmatic measures to recover and thrive after the pandemic. The epidemic has disrupted production and marketing through labour and logistical restrictions, while the negative income shock has restricted access to markets and increased food commodity costs, changing consumption patterns, the poll found. India’s agriculture business was devastated by the outbreak. The crisis has allowed the state to launch many programmes and make important changes. After COVID-19, coordinated effort is needed to break the global stalemate. This review study shows how the COVID-19 epidemic has damaged global. © 2024, CRC Press/Balkema. All rights reserved.

关键词： COVID-19

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Analysis of Various Motion Detection and Facial Recognition Based Home Security Systems 2nd

Analysis of Various Motion Detection and Facial Recognition ...

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2nd International Conference on Deep sciences for Computing and Communications, IconDeepCom 2023

作者： Karthikeyan, P. Nidhi, Shashank Vishnu Kumar, S.R. Karunakaran, V. Velliangiri, S. Department of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan Department of Computer Science and Engineering-AI and ML Jain Deemed to be University Bangalore India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9783031689079

A combination of AI and IoT makes a great home security system. IoT collects data from the surroundings, while AI takes the data and makes predictions accordingly. In this case, these predictions help in creating alerts. CCTV cameras were traditionally used for home security systems, with attendees required to monitor the footage. Because of technological advancements, this process can be completed with the least amount of human intervention. Other work has been done in the field of home security systems that use facial recognition and motion detection. Those systems don’t exhibit good accuracy in detecting multiple human faces. This work focuses on a detailed survey of different facial recognition techniques along with motion detection techniques as per the requirements of a home security system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Motion tracking

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Precision Forecasting of Stock Prices: Leveraging XGBoost and Technical Indicator for Advanced Predictive Modeling 1

Precision Forecasting of Stock Prices: Leveraging XGBoost an...

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1st International Conference on Intelligent Systems and computational Networks, ICISCN 2025

作者： Yuvaraj, S. Chenni Kumaran, J. Vinston Raja, R. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Kattankulathur Chennai India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529246

The investigation aims to evaluate the performance of machine learning techniques, particularly the XGBoost regression method, for stock price prediction with the help of technical indicators. The research targets the adjusted closing price to reduce the autocorrelation problem in time series. The Five Days and Ten Days Exponential Moving Averages (EMA-5 and EMA-10) are involved in refining feature selection. The performance of the model was evaluated using different error metrics, namely, Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Coefficient of Determination (R2). The results indicate that the XGBoost model, combined with EMA-5 and EMA-10, achieves high predictive performance, obtaining an r2 value of 0.98. It implies that incorporating short-term EMAs into advanced machine learning techniques drastically improves stock price forecasting. © 2025 IEEE.

关键词： Time series

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EFFICIENT SPECTRAL METHODS FOR EIGENVALUE PROBLEMS OF THE INTEGRAL FRACTIONAL LAPLACIAN ONABALLOFANYDIMENSION

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Journal of computational Mathematics 2024年第4期42卷 1032-1062页

作者： Suna Ma Huiyuan Li Zhimin Zhang Hu Chen Lizhen Chen College of Science Nanjing University of Posts and TelecommunicationsNanjingChina State Key Laboratory of Computer Science/Laboratory of Parallel Computing Institute of SoftwareChinese Academy of SciencesBeijingChina Department of Mathematics Wayne State UniversityDetroitUSA School of Mathematical Sciences Ocean University of ChinaQingdaoChina Beijing Computational Science Research Center BeijingChina

An efficient spectral-Galerkin method for eigenvalue problems of the integral fractional Laplacian on a unit ball of any dimension is proposed in this *** symmetric positive definite linear system is retained explicitly which plays an important role in the numerical *** a sharp estimate on the algebraic system's condition number is established which behaves as N4s with respect to the polynomial degree N,where 2s is the fractional derivative *** regularity estimate of solutions to source problems of the fractional Laplacian in arbitrary dimensions is firstly investigated in weighted Sobolev *** the regularity of eigenfunctions of the fractional Laplacian eigenvalue problem is readily ***,rigorous error estimates of the eigenvalues and eigenvectors are *** experiments are presented to demonstrate the accuracy and efficiency and to validate the theoretical results.

关键词： Integral fractional Laplacian Spectral method Eigenvalue problem Regularity analysis Error estimate

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Improved Integrated Deep Model for Pap-Smear Cell Analysis

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Journal of Systems science and Information 2024年第1期12卷 113-124页

作者： Somasundaram DEVARAJ Nirmala MADIAN Gnanasaravanan SUBRAMANIAM Rithaniya CHELLAMUTHU Muralitharan KRISHANAN School of Electronics Engineering Vellore Institute of TechnologyVellore 632014India Department of Biomedical Engineering Dr.N.G.P Institute of TechnologyCoimbatore 641045India Karunya Institute of Technology and Sciences Coimbatore 641114India Department of Computer Science Institute of Mathematical ScienceSungkyunkwan UniversitySuwon 16419South Korea

Cervical cancer is the fourth most common malignancy to strike a woman *** discovered early enough,it can be effectively *** there is a chance of error owing to human error,the Pap smear is a good tool for first screening for cervical *** also takes a lot of time and effort to *** aim of this study was to reduce the possibility of error by automating the process of classifying cervical cancer using Pap smear *** the purpose of this study,dual convolution neural networks with LSTM were employed to classify images due to deep learning approaches inspire distinct features and powerful classifiers for many computer vision *** proposed deep learning model based on convolution neural networks(CNN)with the long short-term memory(LSTM)network is to learn features which give better recognition *** overall model is known as *** which‘smear’indicates‘pap-smear cancer cells’and‘net’refers to neural *** parameters such as,Accuracy,Precision,Recall,Accuracy,Sensitivity,and Specificity are used to validate the *** proposed method provides the improved accuracy of 99.57 percentage for classification of the pap-smear *** proposed approaches demonstrate the effectiveness of our contributions by testing and comparing with the state-of-the-art techniques.

关键词： pap-smear deep learning convolution neural network smear-net

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Fractional Linear Matroid Matching Is in Quasi-NC 32

Fractional Linear Matroid Matching Is in Quasi-NC

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32nd Annual European Symposium on Algorithms, ESA 2024

作者： Gurjar, Rohit Oki, Taihei Raj, Roshan Department of Computer Science and Engineering Indian Institute of Technology Bombay Mumbai India Department of Mathematical Informatics Graduate School of Information Science and Technology The University of Tokyo Japan

ISBN: (纸本)9783959773386

The matching and linear matroid intersection problems are solvable in quasi-NC, meaning that there exist deterministic algorithms that run in polylogarithmic time and use quasi-polynomially many parallel processors. However, such a parallel algorithm is unknown for linear matroid matching, which generalizes both of these problems. In this work, we propose a quasi-NC algorithm for fractional linear matroid matching, which is a relaxation of linear matroid matching and commonly generalizes fractional matching and linear matroid intersection. Our algorithm builds upon the connection of fractional matroid matching to non-commutative Edmonds' problem recently revealed by Oki and Soma (2023). As a corollary, we also solve black-box non-commutative Edmonds' problem with rank-two skew-symmetric coefficients. © Rohit Gurjar, Taihei Oki, and Roshan Raj;licensed under Creative Commons License CC-BY 4.0.

关键词： Parallel algorithms

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Deep Learning for Depression Detection Using Twitter Data

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Intelligent Automation & Soft Computing 2023年第5期36卷 1301-1313页

作者： Doaa Sami Khafaga Maheshwari Auvdaiappan KDeepa Mohamed Abouhawwash Faten Khalid Karim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computational Intelligence SRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Engineering M.Kumarasamy College of EngineeringKarur639113India Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)College of EngineeringMichigan State UniversityEast LansingMI48824USA

Today social media became a communication line among people to share their happiness,sadness,and anger with their *** is necessary to know people’s emotions are very important to identify depressed people from their *** depression detection helps to save people’s lives and other dangerous mental *** are many intelligent algorithms for predicting depression with high accuracy,but they lack the deﬁnition of such *** machine learning methods help to identify depressed *** the accuracy of existing methods was not *** overcome this issue,the deep learning method is used in the proposed method for depression *** this paper,a novel Deep Learning Multi-Aspect Depression Detection with Hierarchical Atten-tion Network(MDHAN)is used for classifying the depression ***,the Twitter data was preprocessed by tokenization,punctuation mark removal,stop word removal,stemming,and *** Adaptive Particle and grey Wolf optimization methods are used for feature *** MDHAN classiﬁes the Twitter data and predicts the depressed and non-depressed ***,the proposed method is compared with existing methods such as Convolutional Neur-al Network(CNN),Support Vector Machine(SVM),Minimum Description Length(MDL),and *** suggested MDH-PWO architecture gains 99.86%accuracy,more signiﬁcant than frequency-based deep learning models,with a lower false-positive *** experimental result shows that the proposed method achieves better accuracy,precision,recall,and *** also mini-mizes the execution time.

关键词： Depression detection twitter data tweets deep learning swarm intelligence multi-aspect depression detection prediction

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Machine Learning Driven Cardiovascular Disease Prediction Among Male Senior Adults

Machine Learning Driven Cardiovascular Disease Prediction Am...

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2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Elango, Poongothai Kasthuri, A. Mariammal, G. Saravanan, T.R. Srm Institute Of Science And Technology Department Of Computational Intelligence Chennai India Arulmigu Subramania Swamy Arts And Science College Department Of Computer Science Thoothukudi Vilathikulam India Vel Tech Rangarajan Dr. Sagunthala R & D Institute Of Science And Technology Department Of Computer Science And Engineering Chennai Avadi India Srm Institute Of Science And Technology Department Of Computational Intelligence Chennai Kattankulathur603203 India

ISBN: (纸本)9798350364828

The heart is a critical organ at the heart of the circulatory system, acting as a powerful muscle pump that keeps life going by constantly pumping blood throughout the body. Heart illnesses, often known as cardiovascular diseases (CVDs), are a broad category of ailments that affect the heart and blood arteries. The severity of these illnesses ranges from minor to life-threatening. Heart disease prediction using machine learning is essential for improving patient care, reducing healthcare costs, and addressing a major public health challenge. It leverages advanced data analytics to enable early detection, personalized treatment, and more efficient allocation of resources, ultimately leading to better health outcomes and a positive impact on public health. Heart disease prediction involves the use of machine learning and data analysis techniques to assess an individual's risk of developing heart-related conditions. This process begins with collecting a wide range of medical data from kaggle for analysis, including demographics, medical history, and clinical measurements. In this analysis, Random forest is used as a feature estimate for the study of cardiovascular disease prediction for male senior adults in the age group of 37 to 70 years since we analyzed from the existing research that it performs better for heart disease. Recursive Feature Elimination with Cross-Validation (RFECV) is used to assess feature importance. It trains the model using logistic regression and achieves an accuracy of 82.4%. Predicting cardiovascular health among senior male adults is of utmost importance to proactively manage and mitigate the risks associated with these conditions. © 2024 IEEE.

关键词： Forecasting

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