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

作者： Nair, Surabhi S. John, Asha Susan Raju, Juby Musaliar College Of Engineering And Technology Department Of Electronics And Communication Engineering Pathanamthitta India Kalasalingam Academy Of Research And Education Department Of Electronics And Communication Engineering Virudhanagar Tamil Nadu Krishnan kovil626 126 India Hindustan Institute Of Technology And Science Department Of Computer Science Engineering TamilNadu India

ISBN: (纸本)9798350364828

The disease known as lung cancer, which is common and frequently deadly, starts in the cells of the lungs and causes symptoms like exhaustion, chest pain, and persistent coughing. Since small cell lung cancer (SCLC) is less frequent but more dangerous than non-small cell lung cancer (NSCLC), knowledge of its causes, including smoking, pollution, and genetic factors, is essential. Survival rates are greatly increased by early identification by about 20%. This study comprehensively analyzes deep learning and machine learning models-based lung cancer prediction methods from 2016 to 2023. The review emphasizes how well these models work to achieve greater accuracy. Expanding on this, a new method is suggested that combines data augmentation and denoising preprocessing methods with a CNN in a ResNet architecture for classification. The goal of this hybrid model is to maintain and improve accuracy levels in the identification of benign and malignant lung pictures. This methodology offers promising promises for more accurate and reliable lung cancer prediction by utilizing advances in both preprocessing and classification techniques. © 2024 IEEE.

关键词： Diseases

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GUI-Enabled Wearable Solution for Tremor Detection and Fall Prevention

GUI-Enabled Wearable Solution for Tremor Detection and Fall ...

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International Conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2023

作者： Agarwal, Anubhav Mathur, Yash Yadav, Mahendra Kumar Singh, Mohini Preetam Meerut Institute of Engineering & Technology Uttar Pradesh Meerut India Department of Computer Science and Engineering Meerut Institute of Engineering & Technology Uttar Pradesh Meerut India Department of Electronics and Communication Engineering Meerut Institute of Engineering & Technology Uttar Pradesh Meerut India

ISBN: (纸本)9789819746491

The tremors can be caused by different diseases in varied demographic depending on age or the cause of diseases. The injuries resulting from accidents are also one of the reasons for the occurrence of tremors and hence affect a huge population every year. The proposed work is aimed at improving the elderly people's safety, tremors due to other related diseases, and security in intelligent surroundings. Our ground-breaking method integrates angle detection and fall detection to produce a comprehensive safety solution catered to the unique requirements of the senior demographics. Additionally, real-time fall detection is made possible by the combination of wearable gadgets and addressing the increased vulnerability of the elderly to unintentional slips and falls. Immediate alarms are set out, informing the emergency services, family members, or caregivers to ensure prompt help and potentially life-saving interventions. The device is calibrated and tested with patients with different diseases integrating features in a mobile application and Web site to provide real-time updates on an angle and fall;hence, the day-wise updates regarding falling of the subject can be seen through graphs on the Web site as well as on the mobile application. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fall detection

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An Efficient Attendance Management with Deep Learning

An Efficient Attendance Management with Deep Learning

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2024 IEEE International Conference on Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications, COSMIC 2024

作者： Shivanna Hema, H.P. Janardhana, D.R. Srinivas, P.M. Sahyadri College of Engineering and Management Department of Computer Science and Engineering Mangaluru India D S Dinakar Polytechnic College Department of Electronics and Communication Engineering Shivamogga India Nitte Meenakshi Institute of Technology Department of Computer Scince and Engineering Bengaluru India

ISBN: (纸本)9798331517892

Accurate attendance recording is critical in educational institutions to ensure students' and teachers' consistency and discipline. Traditional manual attendance systems are frequently inefficient and prone to errors, such as proxy attendance and missed entries, which may impact the accuracy of attendance records. To address these challenges, an automatic attendance system that uses camera-based facial recognition technology is proposed. The latest technology records accurate attendance by taking visuals of students as soon as the class starts and comparing them to an already-existing database. Face recognition happens through human detection. The concept used here is deep learning. For students, this automated solution streamlines attendance management by ensuring that records are accurate and up to date without the need for manual entry. The system efficiently keeps track of the presence of teachers, notifying the Head of department (HOD) right away in the case of an excused absence, therefore preventing disruptions during scheduled lessons. This technology significantly increases the reliability and efficiency of attendance tracking by automating the process. Enhancing record accuracy and accountability leads to a more regulated and effective learning environment that benefits teachers and students alike. This technological method offers greater precision and oversight, leading to improved institutional administration and educational outcomes. © 2024 IEEE.

关键词： Students

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IoT based Automated Rehabilitation using 3D Printed Hand and Health Monitoring for Paralyzed Patients 5

IoT based Automated Rehabilitation using 3D Printed Hand and...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Govindaraj, S. Maheswari, S. Loganathan, P. Sharanya Nivasini, J.S. Ponkarthika, M. Velalar College of Engineering and Technology Department of Biomedical Engineering Erode India Excel Engineering College Department of Electronics and Communication Engineering Namakkal India Erode Sengunthar Engineering College Department of Computer Science and Design Erode India Kongu Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (纸本)9798350389609

Paralysis is a condition characterized by the loss of muscle function in part of the body, leading to impaired movement and control. It can significantly impact an individual's daily life, mobility, and overall well-being. Paralysis can result from various factors, including spinal cord injuries, strokes, traumatic injuries, neurological disorders, and certain medical conditions. According to the World Health Organization (WHO), millions of people worldwide live with some form of paralysis, with varying degrees of severity. Tracking the improvement of patients using these techniques might be difficult. Rehabilitation results inadequate monitoring of critical health factors including temperature and blood oxygen levels. The suggested approach is creating a 3D - printed hand incorporating Internet of Things sensors. Paralysis patients can benefit from this hand real-time sensory feedback and customized therapy routines. IoT sensors are incorporated in the hand to track the patient's hand movements, blood oxygen saturation (SpO2), heart rate, and muscle activity (EMG). Arduino UNO serves as the central controller that facilitates automation, while the Wi-Fi module and voice control enhance user interaction. Additionally, Peltier modules keep the affected extremities at their ideal temperature, improving patient comfort and promoting blood flow. Suggested approach provides a multi-dimensional approach to enhance their entire quality of life and health condition for people with this difficult medical condition by combining all those features. © 2024 IEEE.

关键词： Sensory feedback

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Artificial Intelligence and Deep Learning-based Model for Indoor Environment: with Virtual Reference Tag Allocation 5

Artificial Intelligence and Deep Learning-based Model for In...

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5th International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2024

作者： Mathavan, N. Sivaranjani, S. Department of Electronics &Communication Engineering Nadar Saraswathi College of Engineering and Technology Vadapudupatti Tamil Nadu Theni India Department of Computer Science and Engineering Sethu Institute of Technology Kariyapatti Tamil Nadu Virudhunagar India

By the evolution of context-aware application, indoor location positioning gains much more attention worldwide. Radio Frequency Identification (RFID) is one such wireless positioning technology adopted widely. Moreover, enhancing the tracking accuracy and thus reducing the tracking error remains a challenging task in RFID based indoor environment. So as to overcome this aspect, a localization and tracking method depending on virtual reference tag (VRT) is employed in the indoor environment. This research work includes four modules like (i) data collection and Received Signal Strength Indication (RSSI) estimation;(ii) Optimal allocation of VRT using Spider Monkey Optimization (SMO) at which the allocation of VRT is made by considering number of tags, environmental factors (humidity and temperature), and SNR. Based on this data, the RFID reader allocates VRT for each grid so as to increase the tracking accuracy;(iii) Artificial Neural Network (ANN) module-based localization at which the localization of unknown tags is made;and (iv) tracking of moving target tag is carried based on Aquila Swarm Optimization-Long short-term memory (ASO-LSTM) approach. The optimization at this stage is employed to choose optimal position which enhances the accuracy of tracking. Thus, finally the location estimation is carried by this approach. The simulation is carried in NS3.26 network simulator and the performance are estimated in terms of SNR, tracking error, mean error, tracking accuracy, mean time, and standard deviation. The performance value is then compared with traditional models to show the enhancement of proposed mode over the other conventional techniques. © 2024 The Authors. Published by Elsevier B.V.

关键词： Long short-term memory

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Exploring the Role of Automated Reasoning in Big Data Analysis 15

Exploring the Role of Automated Reasoning in Big Data Analys...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Neetha, S.S. Sahoo, Girija Shankar Karthigaikuar, P. Jayasudha, S. Shinde, Gitanjali R. Lone, Zeeshan Ahmad Department of Computer Science and Information Technology Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore641032 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350370249

In large-scale data analysis, computer reasoning is an important role. This era can pull significant files from substantial statistics and in a well-timed manner. In this way, through automatic feel, facts scientists and researchers can pick styles out of your statistics or even locate anomalies that could not have been detected in records units. Additionally, it speeds up constructing decision-making models and stimulates artificial intelligence. Artificial intelligence permits herculean records mining and semantic analysis methods, including device learning, ontologiesystem gaining knowledge and natural language processing. Likewise, it offers a platform for creating and monitoring valuable findings, trends or patterns. This also helped to enhance the accuracy of analytic results and provides a more efficient way for statistics scientists & researcher s to analyze large amounts of data. This generation poses a chance to enhance state-of-the-art strategies by making the correct picks. © 2024 IEEE.

关键词： Aberrations Discriminations Electronic Mechanical Mindset

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Detection and Mitigation of Attacks in MANETs: A Comprehensive Survey of Security Techniques 4

Detection and Mitigation of Attacks in MANETs: A Comprehensi...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Mohandas, R. Sivapriya, N. Vaigandla, Karthik Kumar Kirubasankar, K. Chennai Institute of Technology Department of Electronics & Communication Engineering Tamil Nadu Chennai India Cauvery College for Women Department of Computer Applications Tamilnadu Trichy India Balaji Institute of Technology and Science Department of Electronics & Communication Engineering Telangana Warangal India

ISBN: (纸本)9798331529635

MANETs are decentralized and self-organizing networks with no fixed infrastructure. They are very flexible but, at the same time are vulnerable to the huge diversity of security threats. The dynamic topology and the lack of central control along with the reliance on wireless communication expose MANETs to several attacks, which are blackhole, wormhole, Sybil, and DoS attacks. This is a survey paper that covers the most common security threats towards MANETs and probes for different detection and prevention techniques proposed to mitigate those risks. Various methodology are examined, ranging from cryptographic methods and trust-based models to machine learning and intrusion detection systems, comparing the effectiveness, scalability, and adaptability of those approaches in different network environments. The paper further outlines some real strengths and weaknesses of the currently used solutions, indicates potential key challenges, and states the future directions that could form the way for strengthening the security capabilities of MANETs with respect to evolving emerging technologies such as 5G and beyond. The aim is to demonstrate the ongoing state of the attack detection and prevention in MANETs, thereby offering insights into promising research opportunities. © 2024 IEEE.

关键词： Network intrusion

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Optimizing Energy Consumption in Smart Grids Using Demand Response Techniques

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Distributed Generation and Alternative Energy Journal 2024年第1期39卷 111-136页

作者： SwornaKokila, M.L. Venkatarathinam, R. Rose Bindu, Joseph P. Manivasagam, M.A. Kishore, Kakarla Hari Department of Computing Technologies School of Computing College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar Kattankulathur TN Chennai India Department of Information Technology Sri BalajiChockalingam Engineering College A.C.S. Nagar Irumbedu Thiruvannamalai District Tamil Nadu Arni India Department of Mathematics Dayananda Sagar College of Engineering Bangalore India Computer Science and Engineering Siddartha Institute of Science and Technology Andhra Pradesh India Department of Electronics and Communication Engineering KoneruLakshmaiah Education Foundation Guntur Andhra Pradesh Vaddeswaram India

Smart grids have developed as a potentially game-changing strategy for controlling the demand and supply of energy. Unfortunately, peak demand is a significant source of grid instability and rising energy prices, making it one of the most critical difficulties in smart grids. During times of high energy demand on the grid, demand response (DR) strategies incentivize consumers to change how they use energy. This study’s overarching goal is to learn how DR methods may be used to help smart grids make better use of their energy resources. The primary research is to develop a smart DR system that can predict times of high energy demand and proactively alter usage to reduce such periods. Machine learning strategies are utilized in the proposed system to estimate peak demand via past data, weather predictions, and other variables. The system will then alter energy use based on real-time data from smart meters along with other sensing devices to meet the projected demand. The simulation model will include several scenarios for testing the DR system’s flexibility, including a range of weather conditions, load profiles, and grid topologies. Several indicators, including peak demand reduction (80.04%), energy savings (38.09%), environmental consequences, and reaction time ( © 2024 Taylor and Francis Inc.. All rights reserved.

关键词： Smart power grids

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Enhancing Sugarcane Disease Diagnosis: UNET Architecture for Red Rot Identification 2

Enhancing Sugarcane Disease Diagnosis: UNET Architecture for...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Kumar, Arvind Singh, Jitendra Sharma, Rohit Department of Computer Science and Engineering Srm Institute of Science and Technology Delhi Ncr Campus Up Ghaziabad India Department of Electronics and Communication Engineering Abes Engineering College Up Ghaziabad India

ISBN: (纸本)9798331530389

Image segmentation is an important part of many computer vision problems and existing approaches such as pixel by pixel classification and manual annotation can be either in efficient or inaccurate. This research explored the way UNET architecture can circumvent these constraints to yield accurate segmentation. A diverse image dataset was applied to the UNET, and then its performance compared to conventional segmentation methods. Its capability to capture both the local and global characteristics contributes to the model's improved performance, especially in complex settings. To detect red rot disease in sugarcane, these characteristics were used with an accuracy of 97%. UNET can be further improved by integrating it with other deep-learning techniques and its future research should focus on optimizing UNET for special use cases. This technology can also be used by farmers and agricultural experts to better pinpoint and tackle crop diseases, which could lead to better harvest yields and less financial baggage. © 2025 IEEE.

关键词： Image segmentation

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Toxic Comment Detection based on Improved Bi Directional Long Short Term Memory 1

Toxic Comment Detection based on Improved Bi Directional Lon...

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

作者： Kamma, Vidya Wbaid, Saef Sudhakar, K. Nisha, A. Saravanan, T. Neil Gogte Institute of Technology Department of Computer Science and Engineering Rangareddy Hyderabad India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Data Science Bengaluru India Dhanalakshmi Srinivasan College of Engineering and Technology Department of Computer Science and Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331529246

In recent years, toxic comment detection evolved as important in detecting hatred and harmful content present in user-generated content. However, a few challenges are raised in detecting toxic comments like context sensitivity and missing toxic content. To overcome these issues, improved Bi-directional Long Short-Term Memory (Bi-LSTM) is proposed for toxic comment detection. Initially, data is preprocessed with lemmatization and stop words removal methods for converting the texts to its base form and eliminates the common words respectively. Then, the Word to Vector (Word2Vec) technique converts the words into a dense vector representation by capturing semantic relationships and contextual meanings. Finally, improved Bi-LSTM is employed for detecting the toxic comments by capturing both past as well as future context with its bidirectional architecture and focuses on important words within the context dynamically. From the results, proposed Improved Bi-LSTM model attained better outcomes in terms of accuracy (acc) of 94.32%, precision (pr) of 98.07%, and recall of 95.98% compared to existing Convolutional Neural Networks (CNN)-BiLSTMs. © 2025 IEEE.

关键词： Semantics

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