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

作者： Karthikeyan, M. Saravana Blessie, A. Alice Raj, J. Relin Francis Krishnan, R. Santhana Haribabu, K. Balamurugan, S. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Government College of Engineering Department of Electronics and Communication Engineering Tirunelveli India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Scad College of Engineering and Technology Department of Mechanical Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Mathematics Madurai India

ISBN: (纸本)9798331509828

This paper presents a Multi-Modal Deep Learning framework for User-Centric Car Recommendations, aimed at transforming the car selection process through advanced artificial intelligence techniques. The proposed system integrates a wide range of data sources, including search history, showroom visits, car performance metrics, and social media engagements, to deliver highly personalized, accurate, and timely car recommendations that align with individual preferences, budgets, and lifestyles. The framework leverages multi-modal data processing to optimize car selection by analyzing both qualitative and quantitative user inputs, ensuring precise and context-aware recommendations. By addressing challenges such as choice overload and decision fatigue, the system enhances user decision-making capabilities. Real-time processing ensures that users receive up-to-date and relevant suggestions, improving engagement and overall satisfaction. The inclusion of a feedback loop further refines the system, enabling continuous improvement and adaptation to evolving user needs and market trends. This innovative framework represents a significant advancement in car recommendation systems by combining data diversity, state-of-the-art deep learning models, and real-time analytics. Its ability to provide dynamic, personalized, and actionable insights empowers users to make confident, well-informed decisions during their car-buying journey. With its robust and scalable design, this framework offers a transformative approach to modern automotive recommendation systems, setting a new standard for user-centric experiences. © 2025 IEEE.

关键词： Budget control

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Efficiency Evaluation of a Combining Compact 1D CNN and Signal Filtering Prototype Algorithm for Vibration Signal Classification 4

Efficiency Evaluation of a Combining Compact 1D CNN and Sign...

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4th International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics, ICA-SYMP 2025

作者： Krinsukont, Thosapon Sirithanapipat, Taweedej National Science and Technology Development Agency National Electronics and Computer Technology Center Pathumthani Thailand Kasetsart University Department of Mechanical Engineering Bangkok Thailand

ISBN: (纸本)9798331505400

This paper presents an experiment and results of the modified CNN algorithm, it was developed by combining a compact 1D convolution neural network with a tuned signal filter (low-pass filter in this experiment). The algorithm was specifically designed for signal shape detection, prediction, and classification. An experiment aimed to evaluate the created algorithm's structure and signal filter to ensure its suitability for vibration signal data analysis. A testbed was built using an inverter motor equipped with various unbalanced conditions by increasing screws. Four-class unbalanced conditions were studied compared with the normal condition. Each conditions produced the distinct signal shape, with slight differences, making them suitable for evaluating the proposed algorithm's performance. The results demonstrate the satisfied performance of the algorithm, which effectively classifies vibration signals unbalance-3 (precision 54%), unbalance-4 (precision 77%) and a group of normal, unbalance-1, and unbalance-2. The group of normal, unbalance-1, and unbalance-2 can't be easily classified due to the tiny differences in the signal shapes, caused by small mass variations of the unbalanced screw unbalance-1, and unbalance-2. In conclusion, the algorithm proved satisfied effectiveness for its intended purpose of vibration signal classification. © 2025 IEEE.

关键词： Testbeds

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Comparative Evaluation of Marker-Controlled Method and Gradient Distance Transformation through Watershed Segmentation 9

Comparative Evaluation of Marker-Controlled Method and Gradi...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Choudhary, Nilam Rathore, Pramod Singh Kumar, Dinesh Swami Keshvanand Institute of Technology Management & Gramothan Department of Computer Science & Engineering Jaipur India Manipal University Jaipur Department of Computer And Communication Engineering Jaipur India Swami Keshvanand Institute of Technology Management & Gramothan Department of Electronics And Communication Engineering Jaipur India

ISBN: (纸本)9798350394474

Image segmentation is a fundamental process in computer vision and image analysis, with applications in various fields. By dividing digital images into distinct regions or objects based on their characteristics, such as color, intensity, or texture, image segmentation allows for more efficient object recognition, accurate medical image analysis, and improved computer vision algorithms. This paper aims to provide a comprehensive examination of one popular method for image segmentation, the watershed transformation, and discuss its limitations and potential remedies. By understanding the challenges and possible solutions in digital image segmentation, researchers and practitioners can enhance the accuracy and effectiveness of their image analysis techniques. © 2024 IEEE.

关键词： Image segmentation

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IoT based Women Safety Bag

IoT based Women Safety Bag

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2023 IEEE International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Kumar, M. Ashok Pradeepa, P. Muthu, A. Essaki Krishnan, R. Santhana Saravanan, K. Narayanan, K. Lakshmi SCAD College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India PSNA College of Engineering and Technology Department of Information Technology Dindigul India SCAD College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Anna University Regional Campus Department of Computer Science and Engineering Tirunelveli India Francis Xavier Engineering College Department of Electronics and Communication Engineering Tirunelveli India

ISBN: (纸本)9798350397208

Women's safety has become one of the chief concerns in India, since quite a large number of violence takes place against women's like sexual assault, abuse and murder. Women's are worshiped as God in this country yet her safety is at stake. Even when Women's started to equally flourish in every sectors starting from a Taxi Driver to a CEO of the top MNC, there are major safety issues which still prevent them to excel in their sector. Women's of all ages are affected by some sort of problems against their rights. It tends to increase as we let things grow without noticing them. The reports from National Crime Records Bureau (NCRB) which was released in 2021 which clearly states that more than 4, 00,000 cases were registered under crime against women. Hence, there should be a system which helps them to seek help when they are feeling unsecured. Therefore we propose a system which will assist the women to seek help when they are in danger. This system facilitates them to contact their relative when they experience unsecured situations. © 2023 IEEE.

关键词： Crime

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Application of TQWT based filter-bank for sleep apnea screening using ECG signals

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Journal of Ambient Intelligence and Humanized Computing 2024年第1期15卷 893-904页

作者： Nishad, Anurag Pachori, Ram Bilas Acharya, U. Rajendra Discipline of Electrical Engineering Indian Institute of Technology Indore Indore453552 India Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore599489 Singapore Department of Biomedical Engineering School of Science and Technology SIM University Singapore599491 Singapore Department of Biomedical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia

The sleep apnea is a disease in which there is the absence of airflow during respiration for at least 10 s. It may occur several times during the night sleep. This disease can lead to many types of cardiovascular diseases. To detect this disease, signals obtained from many channels of polysomnography are to be observed visually by physicians for the long duration. This procedure is expensive, time-consuming, and subjective. Hence, it is required to build an automated system to detect the sleep apnea with few channels. This paper uses single-lead electrocardiogram (ECG) signal to detect apneic and non-apneic events. The proposed method uses tunable-Q wavelet transform (TQWT) based filter-bank instead of TQWT to decompose the segment of ECG signal into several constant bandwidth sub-band signals. Then centered correntropies are computed from the various sub-band signals. The obtained features are then fed to the various classifiers to select the optimum performing classifier. In this work, we have obtained the highest classification accuracy, specificity, and sensitivity of 92.78%, 93.91%, and 90.95% respectively using random forest classifier. Hence, our developed prototype is ready for validation with the huge database and clinical usage. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

关键词： Filter banks

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Hybrid Grey Wolf and Dipper Throated Optimization in Network Intrusion Detection Systems

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computers, Materials & Continua 2023年第2期74卷 2695-2709页

作者： Reem Alkanhel Doaa Sami Khafaga El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Rashid Amin Mostafa Abotaleb B.M.El-den Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science University of Engineering and TechnologyTaxilaPakistan Department of System Programming South Ural State UniversityChelyabinsk454080Russia

The Internet of Things(IoT)is a modern approach that enables connection with a wide variety of devices *** to the resource constraints and open nature of IoT nodes,the routing protocol for low power and lossy(RPL)networks may be vulnerable to several routing ***’s why a network intrusion detection system(NIDS)is needed to guard against routing assaults on RPL-based IoT *** imbalance between the false and valid attacks in the training set degrades the performance of machine learning employed to detect network ***,we propose in this paper a novel approach to balance the dataset classes based on metaheuristic optimization applied to locality-sensitive hashing and synthetic minority oversampling technique(LSH-SMOTE).The proposed optimization approach is based on a new hybrid between the grey wolf and dipper throated optimization *** prove the effectiveness of the proposed approach,a set of experiments were conducted to evaluate the performance of NIDS for three cases,namely,detection without dataset balancing,detection with SMOTE balancing,and detection with the proposed optimized LSHSOMTE *** results showed that the proposed approach outperforms the other approaches and could boost the detection *** addition,a statistical analysis is performed to study the significance and stability of the proposed *** conducted experiments include seven different types of attack cases in the RPL-NIDS17 *** on the 2696 CMC,2023,vol.74,no.2 proposed approach,the achieved accuracy is(98.1%),sensitivity is(97.8%),and specificity is(98.8%).

关键词： Metaheuristics grey wolf optimization dipper throated optimization dataset balancing locality sensitive hashing SMOTE

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Enhanced Multi-Scale Convolutional Neural Network with Attention Mechanism for Accurate and Efficient Automated Hematological Diagnostics from Blood Smear 3

Enhanced Multi-Scale Convolutional Neural Network with Atten...

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3rd International Conference on electronics and Renewable Systems, ICEARS 2025

作者： Santosh Kumar, B. Basha, C.H. Hussaian Jini Kumari, M. Pradeep Kumar, S. Sathya, V. Rajaram, K. New Horizon College of Engineering Department of Computer Science and Engineering Bengaluru560103 India Sr University Department of Electrical and Electronics Engineering Warangal506371 India Chennai Institute of Technology Department of Electronics and Communication Engineering Chennai600069 India Rajalakshmi Engineering College Department of Computer Science Design Department Thandalam Chennai602105 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai600062 India Velalar College of Engineering and Technology Department of Biomedical Engineering Thindal Erode638012 India

ISBN: (纸本)9798331509675

Hematological disorders require accurate and timely diagnosis for effective treatment and patient management. This research develops a new framework that uses deep learning to rapidly identify blood disorders from peripheral blood pictures. Our system uses an AECM and MSCNN combination to diagnose anemia, infections, and leukemia with greater accuracy. The MSCNN captures image details at multiple scales while the AECM detects meaningful areas in blood images which boosts detection results. Our system reached 98.5% total accuracy after validating its performance on public blood smear images. The detection method delivered 97.8% precision and 98.2% recall values during testing. Our diagnostic framework makes blood test results quicker and more affordable while reducing patient labs demands in healthcare environments that face resource constraints. © 2025 IEEE.

关键词： Convolutional neural networks

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An Active Reconfigurable Mixer in GaN technology for 5G Power Amplifier Wi-Fi Communications 6

An Active Reconfigurable Mixer in GaN Technology for 5G Powe...

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6th Parul International Conference on engineering and technology, PICET 2024

作者： Kumar, Rajesh Babu, Tosif Abdul Gaffar Kanaujia, Binod Kumar Tiwari, Devesh Department of Computer Science & Engineering Galgotias College of Engineering &Technology Greater Noida India Department of Electronics & Communication of Engineering PIET Gujarat Vadodara India Department of Electronics & Communication of Engineering National Institute of Technology Jalandhar Jawaharlal Nehru University New Delhi110067 India Department of Computer Science & Engineering Graphic Science and Engineering Graphic Era Era Hill University Uttarakhand Dehradun India

ISBN: (纸本)9798350369748

The mixer's key attributes in this article are its high power output, high conversion gain, high efficiency, and good isolation between board positions. We have designed and implemented a mixer that can be reconfigured dynamically. Using a voltage gain of 12.3 dB, the local oscillator (LO) signal is amplified. The purpose of the active reconfigurable mixer is to enhance the cross-driver platform's performance. Alongside, with the occurrence of a mixer a high-power level is attained. It is noteworthy to highlight that the third-order intermodulation (IIP3) of the new configuration is -9 dBm, which is regarded as negligible. The mixer runs at 6 GHz and is powered by 0.15-µm GaN technology. © 2024 IEEE.

关键词： Gallium nitride

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Automated 20-20-20 Timers Using Facial Detection and Facial Recognition 4

Automated 20-20-20 Timers Using Facial Detection and Facial ...

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4th IEEE Annual Flagship India Council International Subsections Conference, INDISCON 2023

作者： Das, Saumodip Pal, Geetanjali Manna, Somsubhra Dhara, Arnab Sen, Anindya Heritage Institute of Technology Department of Electronics and Communication Engineering Kolkata India Heritage Institute of Technology Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350333558

This paper proposes two automated 20-20-20 timers using facial detection and facial recognition respectively. computer Vision Syndrome (CVS) is a common phenomenon these days, resulting in eye irritation and headaches. A possible prevention of CVS is adopting the 20-20-20 rule. Two types of 20-20-20 timer exist in the market, but both have scopes of improvement. While one type of timer needs to be manually set by the user like a stopwatch, the other type of timer detects the ON -screen time of monitors as a parameter. Both these models fail to detect whether the user is actually looking at the screen, and interrupt the work schedule of the users. The authors have developed a smart automated 20-20-20 timer that uses haar-cascade classifiers for detecting the face and accordingly adapt to the situation. This timer is able to detect the facial features in real time, and alert the user after 20 minutes of screen time. Furthermore, the authors have developed another smart 20-20-20 timer using YOLOv5s. This timer can recognise multiple users looking at the screen and run the timer independently for each person. While the haar-cascade enabled timer is preferred for homes and personal laptops, desktops and PCs;the YOLOv5s enabled timer is suited for offices, schools, colleges and libraries. This paper proposes an automated smart 20-20-20 timer system. © 2023 IEEE.

关键词： Python

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Future IoT sensing: Silvaco simulation of graphene-gated p-i-n photosensor on SOI for blue and ultraviolet light 25

Future IoT sensing: Silvaco simulation of graphene-gated p-i...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Bansal, Shonak Bansal, Arnav Aljaidi, Mohammad Prakash, Krishna Chandigarh University Department of Electronics and Communication Engineering Gharuan India Chandigarh University Department of Computer Science and Engineering Gharuan India Zarqa University Department of Computer Science Zarqa Jordan University of Business and Technology College of Engineering Jeddah21448 Saudi Arabia Nri Institute of Technology Department of Electronics and Communication Engineering Agiripalli Andhra Pradesh Vijayawada521212 India

ISBN: (纸本)9798331540012

The Internet of Things (IoT) revolutionizes how devices interact and communicate, creating a vast network of interconnected systems with diverse applications, including healthcare, environmental monitoring, and smart cities. A critical component of these IoT systems is the ability to detect and respond to various environmental stimuli, such as light. In this context, photosensors play a pivotal role, particularly in detecting blue and ultraviolet (UV) light, essential for applications like UV index monitoring, flame detection, and secure communications. This paper proposes a lateral p-i-n photosensor design incorporating a single-layer graphene gate, built using silicon-on-insulator (SOI) technology. The device aims to enhance the detection of blue and ultraviolet light. The sensor achieves a larger depletion region with minimal gate bias voltage by employing a graphene gate, leading to superior quantum efficiency. The photosensor exhibits a photocurrent-to-dark current ratio of 1.6×105, a rise time of 2.2 ns, a fall time of 2.0 ns, and an external quantum efficiency of 51.66%. Leveraging graphene's unique optical and electrical characteristics, this gated structure outperforms conventional alternatives. Its compatibility with modern integrated circuit fabrication techniques makes it a promising candidate for future IoT system integration. Simulations conducted using the Silvaco ATLAS platform, a widely used simulation software for semiconductor device modeling and optimization, provide critical insights into the device's operation. The results demonstrate the potential for superior sensitivity and efficiency in IoT-driven blue and UV light detection. © 2024 IEEE.

关键词： Silicon on insulator technology

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