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4th IEEE International Conference on Artificial Intelligence in engineering and technology, IICAIET 2022

作者： bajpai, Abhishek Chaubey, Srishti Patro, bdk Verma, Abhineet Rajkiya Engineering College Department of Computer Science and Engineering UP Kannauj India B.Tech Computer Science and Engineering Rajkiya Engineering College UP Kannauj India

ISBN: (数字)9781665468374

ISBN: (纸本)9781665468374

Only 0.3 percent of the total water on Earth is available in rivers and ponds, and the majority of it is polluted to the point where drinking it directly can cause disease. In this paper, we will identify the quality of the river Ganges and check if it is portable and healthy. We aim to classify the water on some parameters using different classification algorithms, such as Random Forest, which is a supervised machine learning algorithm. This model's accuracy is around 99 percent, which is far superior to other approaches taken for water quality prediction. © 2022 IEEE.

关键词： Water quality

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Deep Learning based Driver Drowsiness Detection in Real Time Environment - An Enhanced Approach 4

Deep Learning Based Driver Drowsiness Detection in Real Time...

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

作者： Ajay, Ashin Ashok, Naveen Kumar balajiram, Gokul Tamizhselvan, Abinesh Thangavel, Senthil Kumar Somasundaram, K. Amrita Vishwa Vidyapeetham M. Tech in Artificial Intelligence Coimbatore India Amrita Vishwa Vidyapeetham B. Tech in Computer Science and Engineering Coimbatore India Amrita School of Computing Coimbatore Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India School of Physical Sciences Department of Mathematics Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9798331523923

Effective driver behavior monitoring is necessary due to the increase in traffic accidents caused by distracted driving. In order to identify distractions such as talking, drinking, napping, and using a phone, this research suggests a CNN-based methodology. The model uses facial characteristics such as eyes to detect and analyze these activities. Thousands of photos depicting various distracted driving postures are used to train it on a publicly accessible dataset. The strategy seeks to improve road safety by offering a reliable way to identify distractions. © 2025 IEEE.

关键词： Convolutional neural networks

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Enhanced IoT System for Real-Time Detection and Monitoring of Cardiac Events Using Machine Learning Algorithms 3

Enhanced IoT System for Real-Time Detection and Monitoring o...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Safia Naveed, S. Ameen, Nabeena Nulin Jeriba, J. Kcg College of Technology Department of Computer Science and Engineering Chennai India B. S. Abdur Rahman Crescent Institute of Science and Tech Department of Information Technology India

ISBN: (纸本)9798331518592

Heart attacks remain a primary cause of global mortality, highlighting the urgent need for systems that enable early detection and intervention. This study proposes a Smart IoT-based system designed to detect and monitor heart attack symptoms in real-time, thus facilitating timely medical response. The system integrates wearable sensors to collect continuous data on heart rate, stress levels, and positional changes, which is then processed through Arduino IDE and transmitted to an IoT server. Machine learning algorithms, including Support Vector Regression (SVR) and Artificial Neural Networks (ANN), are employed to analyze these signals for abnormal patterns indicative of a potential heart attack. Major findings indicate that this system accurately detects heart rate irregularities, and stress fluctuations and can alert healthcare providers, significantly improving remote monitoring capabilities. The implications of this study suggest that IoT-enabled monitoring systems can substantially enhance patient outcomes by allowing for early diagnosis and proactive care in remote settings, thereby reducing the risk of fatal heart events. © 2024 IEEE.

关键词： Wearable sensors

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Enhanced Monitoring and Security Measures with Video Analysis

Enhanced Monitoring and Security Measures with Video Analysi...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Jyothi, V. Esther Rakesh, G. Aruna, Vipparla Gorintla, Shobana Ayyappa, Yalanati Naidu, U. Ganesh Velagapudi Ramakrishna Siddhartha Engineering College Deemed to be University Department of Computer Applications A.P. Vijayawada India Department of Computer Science and Engineering Pothavarapadu A.P India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Avadi India B V Raju Institute of Technology Telangana Narsapur India

ISBN: (纸本)9798331509828

In modern surveillance, activities have increasingly become dependent on the continuous observation offered by CCTV systems. Still, with massive amounts of video data generated in a minute, sifting through this information manually to pick up any anomalies would be extremely burdensome and nearly impossible without significant human labor and vigilant watchfulness. CNN and CLIP models changed everything with regards to the working of surveillance systems. It makes use of CNN's capability for video frame processing and analysis to detect and classify these activities in the frames as either normal or suspicious. At the same time, it improves this by adding the CLIP model's capability of understanding textual descriptions and visual content together for better nuance detection in suspicious activities. This methodology transforms video surveillance by breaking video streams into frames and analyzing the behavior and interactions of persons in those frames. The synergy of CNN and CLIP models not only ensures real-time, efficient, and accurate surveillance but also minimizes dependency on extensive manual labor for monitoring. This paper aims to provide a contribution toward developing better security infrastructures in public spaces, transportation hubs, and private facilities. The introduction of this anomaly-detection mechanism can hugely improve the capability of implemented surveillance systems with a proactive response based on real-time detection and response capabilities against suspicious activities. © 2025 IEEE.

关键词： Video analysis

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Judging the Psychological Impact of News Articles on Rehab Patients: A Deep Learning Approach

Judging the Psychological Impact of News Articles on Rehab P...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Khan, Amit Majumdar, Dipankar Mondal, bikromadittya Rcc Institute of Information Technology Dept. of Information Technology Kolkata India Rcc Institute of Information Technology Dept. of Computer Sc & Engineering Kolkata India B.P. Poddar Institute of Mgmt & Tech. Dept. of Computer Sc & Engineering Kolkata India

ISBN: (纸本)9798350390759

Sentiment detection and analysis from review comments of products, blogs and other social media communication is a common practice. Lot of research work have been proposed and established in the domain. While the above may prove to be effective for sales and marketing challenges but the same effort for the sentiment analysis of news articles is scarce. but surprisingly, this can definitely establish as a potentially efficient instrument for medical domain judging the suitability of a news article for individuals with disturbed health condition like depression, heart diseases or mental retardation having risk of committing suicides. To address the same, this article presents a novel approach harnessing the potential of Machine Learning (ML) and Deep Learning (DL) to detect the sentiment of a news article so as to measure its impact on the individual readers. The proposed work uses various supervised ML algorithms such as SVM, Naïve bayes', Logistic Regression, Random Forest and Ensemble Method for assessing the inherent sentiment of news articles. Additionally, DL based Transformer model specifically bERT has also been employed to enhance the performance. Obtained results with various performance metrics clearly depict our model's excellent performance. © 2024 IEEE.

关键词： Deep learning

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DLMNN based Heart Disease Prediction with PD-SS Optimization Algorithm

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Intelligent Automation & Soft Computing 2023年第2期35卷 1353-1368页

作者： S.Raghavendra Vasudev Parvati R.Manjula Ashok Kumar Nanda Ruby Singh D.Lakshmi S.Velmurugan Department of Information and Communication Technology Manipal Institute of TechnologyManipal Academy of Higher EducationManipal576104India Department of Information Science and Engineering SDM College of Engineering and TechnologyDharwad580002India Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Engineering B.V Raju Institute of TechnologyTelangana502313India Faculty of Engineering and Technology SRM Institute of Science and TechnologyNCR CampusUttar Prasesh201204India School of Computing Science and Engineering VIT Bhopal UniversityMadhya Pradesh466114India Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India

In contemporary medicine,cardiovascular disease is a major public health *** diseases are one of the leading causes of death *** are classiﬁed as vascular,ischemic,or *** information contained in patients’Electronic Health Records(EHR)enables clin-icians to identify and monitor heart *** failure rates have risen drama-tically in recent years as a result of changes in modern *** diseases are becoming more prevalent in today’s medical *** year,a substantial number of people die as a result of cardiac *** primary cause of these deaths is the improper use of pharmaceuticals without the supervision of a physician and the late detection of *** improve the efﬁciency of the classiﬁcation algo-rithms,we construct a data pre-processing stage using feature ***-ments using unidirectional and bidirectional neural network models found that a Deep Learning Modiﬁed Neural Network(DLMNN)model combined with the Pet Dog-Smell Sensing(PD-SS)algorithm predicted the highest classiﬁcation performance on the UCI Machine Learning Heart Disease *** DLMNN-based PDSS achieved an accuracy of 94.21%,an F-score of 92.38%,a recall of 94.62%,and a precision of 93.86%.These results are competitive and promising for a heart disease *** demonstrated that a DLMNN framework based on deep models may be used to solve the categorization problem for an unbalanced heart disease *** proposed approach can result in exceptionally accurate models that can be utilized to analyze and diagnose clinical real-world data.

关键词： Machine learning random forest coronary heart disease cardiovascular optimization algorithm

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bioCrypt: Gradient-based facial encoding for key generation to encrypt and decrypt multimedia data

arXiv

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arXiv 2024年

作者： Patel, Ankit Kumar Paul, Dewanshi Giri, Sarthak Chaudhary, Sneha Gautam, bikalpa B.Tech Electronics & Computer Engineering Graduated from VIT University Chennai India B.Tech Computer Science & Engineering Graduated from VIT University Vellore India

Security systems relying on passwords are vulnerable to being forgotten, guessed, or breached. Likewise, biometric systems that operate independently are at risk of template spoofing and replay incidents. This paper introduces a biocryptosystem utilizing face recognition techniques to address these issues, allowing for the encryption and decryption of various file types through the Advanced Encryption Standard (AES). The proposed system creates a distinct 32-bit encryption key derived from facial features identified by Histogram of Oriented Gradients (HOG) and categorized using Support Vector Machines (SVM). HOG efficiently identifies edge-aligned facial features, even in dim lighting, ensuring that reliable biometric keys can be generated. This key is then used with AES to encrypt and decrypt a variety of data formats, such as text, audio, and video files. This encryption key, derived from an individual’s distinctive facial traits, is exceedingly challenging for adversaries to reproduce or guess. The security and performance of the system have been validated through experiments using several metrics, including correlation analysis, Shannon entropy, normalized Hamming distance, and the avalanche effect on 25 different file types. Potential uses for the proposed system include secure file sharing, online transactions, and data archiving, making it a strong and trustworthy approach to safeguarding sensitive information by integrating the uniqueness of facial biometrics with the established security of AES encryption. © 2024, CC bY.

关键词： Hamming distance

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Adaptable Individualized Investment Recommendation System

Adaptable Individualized Investment Recommendation System

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Sabiyath Fatima, N. Noor Alleema, N. Mahesh, C. Umanesan, R. Senthil, K. Santhosh Kumar, P. B.S.Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Information Technology Chennai India 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 Department of Computer Science and Engineering Ramapuram Chennai India

ISBN: (纸本)9798331522988

Internetization has played a massive role in the rapid development and growth of the financial investment industry. Due to the rapid development, there is a demand for more financial knowledge services to help make well-informed investments. Accessing and conducting investments through online financial platforms has made the field highly accessible. This has made the investment recommendation system a popular research area. Personalized recommendations will help the user make investments that are safe for their financial situation and also suit their particular needs and interests. This work proposes a method where investment recommendations are given based on three key factors. The user interest sectors, risk appetite-based clustering, and market sentiment of the stock. These factors will be calculated by the use of Lasso regression, SVM, stepwise regression, generalized boosted regression, random forest, ridge regression and fuzzy clustering. based on these three factors, the proposed method results in investment recommendations made for users that are personalized to the specific user. This allows existing investors to gain new opportunities and new investors to start investing with lower amount of risk using the information available. © 2024 IEEE.

关键词： Decentralized finance

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Crime Against Women in India: Statistical Analysis Using Linear Regression

Crime Against Women in India: Statistical Analysis Using Lin...

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Advanced Computing & Communication technologies (ICACCtech), International Conference on

作者： Zarin Tasnim Mim Rittika Paul Mollah Md Foysal Fairuz Nowshin Tohfa Shah Abrar Mahi B-Tech of Computer Science and Engineering-IoT Jain (Deemed-to-be University) Bangalore India CMS Business School Jain (Deemed-to-be University) Bangalore India B-Tech of Computer Science and Engineering East Delta University Chattagram Bangladesh B-Tech of Computer Science and Engineering-Cyber Security Chandigarh University Delhi India

ISBN: (数字)9798331519056

ISBN: (纸本)9798331519063

The crime reports of offenses occurring against women all over India are increasing day by day. It has become one of today's most concerning socio-economic and cultural issues. Crime against women hampers the overall prosperity and development of the nation. This crime violates human rights, impacts women's physical and mental health, and becomes an educational barrier to women's empowerment. This paper surveys the reasons for the influence of reported Crime Against Women (CAW) in 35 states and union territories in India from 2001–2012. The dataset describes different types of Crime Against Women (CAW): rape, kidnapping, abduction, assault on women, insult to the modesty of women, cruelty by husband or relative. This paper also contains a report on the arrested rate for crimes against women in India from 2001–2012. According to the comparison between these two datasets, the person arrest rate is more than the crime against women rate in every state in India. We used machine learning-based linear regression analysis to format the relationship between two datasets, where crime against women is the dependent variable and people arrested rate is the independent variable. This paper discusses two types of linear regression: Simple Linear Regression and Multiple Linear Regression. The relation between these two datasets helps to predict the hot location of crimes, and the activeness of law enforcement regarding the crimes and encourages victims to take action. For the datasets, P-values, R-squared, and adjusted R-squared values are used to evaluate the statistically significant prediction in explaining the variation in crime rates. This prediction is used to reduce the rate of crime against women all over India.

关键词： Surveys Analytical models Statistical analysis Law enforcement Linear regression Mental health Machine learning Predictive models Unemployment Socioeconomics

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Streamlined breast Cancer Detection Using Ultra Wideband Radar without Invasive Measures 2

Streamlined Breast Cancer Detection Using Ultra Wideband Rad...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Arunachalam, P. Latha, P. Nessariose Jose, Naduvathezhath Godwin James, T. Prabahar Vivekanandan, G. Arya, Aayushi Department of Biomedical Engineering Vel Tech Rangarajan Dr. Sugunthala R&D Institute of Science and Technology No-42 Avadi-Vel Tech Road Vel Nagar TN Chennai India Department of Computer Sciences and Engineering V.S.B. Engineering Collage Karur India Denken Solutions India Department of Computer Science and Engineering India Department of CSE Sri Sairam Institute of Technology Chennai India School of Technology Woxsen University India

ISBN: (纸本)9798350383867

The paper proposes the new radar technology for the first stages of detection of the disease without the subjecting the patient to surgery. In this paradigm, such high-end radar technologies such as bistatic and monostatic patterns assist in identifying small tumours that are far from the surface of the breast tissue. One of them is applying the Ultra-Wideband pulses and the specific type is the seventh derivative Gaussian pulse. These pulses are shaped in a very strict manner, and which makes for sharp transition band pass Finite Impulse Response filters for the best data transfer and to eliminate interferences. The capability for sustaining practices recommended by the Federal Communication Commission concerning spectral standards allows signals to proceed safely through breast tissue. by modifying the amplitude and phase of the transmitted pulse in the study, it increases the power of the main lobe radiated as well as on the same note reduces the levels of sidelobes and losses in the channel as these are accomplished using radar antennas. It found that because of the characteristics of the backscattered signals, there are specific temporal and spectral patterns that can reliably indicate the existence of tumor. based on stimulations, there are huge disparities in electric field strength introduced in normal and malignant breast tissue, which supports the efficiency of the new radar system. Moreover, among the important information accumulating duties we can distinguish Specific Absorption Rate (SAR) which specifies the tumour place as it being illustrated by the raised energy absorption in breast malignant tissue relatively to healthy tissue. Similar experiments are also carried out with an actual heterogeneous breast phantom with tumors, and it can also be seen that the results are almost identical with the above simulation results and hence it can be understood that the enhanced radar technology is good enough for the earliest detection of breast cancer. At thi

关键词： Diseases

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