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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Tomar, Vivek Bisht, Kamlesh Singh Panwar, Ayush Dimri, Reetesh Kumar Negi, Mayank Singh Sharma, Swati Computer Science & Engineering Department Uttarakhand Dehradun India Kiet Group of Institutions Computer Science & Engineering Department U.P. Ghaziabad India

ISBN: (纸本)9798350387490

Human skin is an important part of the body that needs attention. Skin diseases are quite common in a country like India with varied climate and high pollution levels. The usual diagnosis of skin diseases is based on visual examination and certain tests performed by a dermatologist. But this is a time-consuming process and can also end up in the wrong diagnosis. Skin disease diagnosis has undergone a revolution because of the significant development in the field of machine learning, notably convolutional neural networks (CNN). CNNs have the potential to completely change the way skin illnesses are diagnosed, tracked, and treated. This study investigates the application of various machine learning techniques in skin problem identification. By using CNN analysis of skin disease images, identification of patterns and making accurate predictions can be done with an unprecedented accuracy and consistency. In addition to diagnosis, machine learning models can treat different skins individually according to their type, thus simplifying the doctor's role. This study aims to use CNN to improve dermatology practices by introducing efficiency and accuracy. This study includes a comprehensive literature review examining existing CNN models and performance metrics. Goals include collecting health data, using CNN's predefined models, comparing their performance, recommending the best models for diagnosing skin diseases, and improving the user interface. It presents a benchmark-based neural network model that may contribute to advances in dermatology in identifying skin diseases through machine learning using data from HAM10000 and DERMNET. The effectiveness of the model is determined by comparing it with other existing models by evaluating quality indicators such as accuracy, precision, and F1 score. © 2024 IEEE.

关键词： Convolutional neural networks

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Early Prediction of Alzheimer's Dementia Using Random Forest Machine Learning Algorithm: A Data-Driven Approach 8

Early Prediction of Alzheimer's Dementia Using Random Forest...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Vani Shree, K. Pradeep, K. Rambabu, Bandi Surendran, R. Balamurugan, K.S. Karpaga Vinayaga College of Engineering and Technology Department of Computer Science Engineering Tamil Nadu Chengalpattu India Chennai Institute of Technology Department of Biomedical Engineering Chennai India CVR College of Engineering Department of Computer Science Engineering Hyderabad India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Karpaga Vinayaga College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu Chengalpattu India

ISBN: (纸本)9798331520762

Alzheimer's dementia is liberal neurological ailment categorized by memory loss, cerebral failure, and it is the utmost communal form of dementia and typically affects older adults. Every year, 6.7 million people are affecting by Alzheimer's dementia (AD) in USA and 4.3% probability of developing Alzheimer's ailment earlier the age of 65 in India. The principal issue associated with delayed prediction of Alzheimer's dementia lies in the incapacity to intervene during the formative years of the dementia progression, thereby missing opportunities for timely intervention and treatment. Available techniques for finding of Alzheimer's dementia with machine learning algorithms resulted in inability to predict the dementia early. For proactive diagnosis of Alzheimer's dementia, proposed a novel technique using Random Forest categorizer and also compared among various machine learning classifiers. Resulted in obtained accurate results with 97.5% of accuracy. © 2025 IEEE.

关键词： Random forests

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Application of AI and ML in Wound Healing and Skin Regeneration 4

Application of AI and ML in Wound Healing and Skin Regenerat...

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

作者： Petkar, Taniya G. Kumar, Praveen Sarate, Kirtiksha U. Faculty of Engineering and Technology Department of Computer Science & Medical Engineering Maharashtra Sawangi Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Design Maharashtra Sawangi Wardha442001 India

ISBN: (纸本)9798331523923

By enabling precise, individualized, and effective treatments, the integration of artificial intelligence (AI) and machine learning (ML) into wound and skin healing is revolutionizing healthcare. Artificial intelligence-controlled tools improve wound assessment, anticipate healing, and collaborate with regular examinations using cutting-edge dressings and imaging technologies. AI computations work on the management of express data to enhance treatment systems, while advancements in nanomaterials like marine collagen and therapies employing young microorganisms further accelerate skin recovery. Despite encouraging findings, there are still problems, such as potential overfitting and dataset limitations. In general, machine learning is changing the way that injuries are treated by providing proactive, tailored solutions that aim to understand outcomes and lower the cost associated with clinical outcomes. © 2025 IEEE.

关键词： Personalized medicine

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A System Design for 3D Visualization of Underwater Multi-Target Tracking Based on Unity 3D and GMPHD Filter

A System Design for 3D Visualization of Underwater Multi-Tar...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Kim, Seungho Lee, Scott Uk-Jin Hanyang University Bio-Artificial Intelligence Department of Computer Science and Engineering Ansan Korea Republic of Hanyang University Department of Computer Science and Engineering Ansan Korea Republic of

ISBN: (纸本)9798331510756

This paper proposes a design for a visualization system for underwater multi-target tracking based on the Gaussian Mixture Probability Hypothesis Density (GMPHD) filter using Unity 3D engine. Traditional analysis methods for underwater tracking algorithms rely primarily on two-dimensional graphs and numerical data, making it challenging to intuitively understand the complex behavior of tracking systems in three-dimensional underwater environments. Our proposed system design provides a framework for 3D visualization of target movements, sonar sensor detection areas, and filter estimation results, along with an interactive interface for parameter adjustment. The proposed architecture consists of four main components: simulation core, visualization module, environment simulation, and user interface, which would enable researchers to analyze algorithm performance under various scenarios effectively. Through real-time visualization and parameter adjustment capabilities, the system is designed to offer immediate feedback on how different parameters affect tracking performance. This architectural approach aims to provide researchers with a more intuitive understanding of the GMPHD filter's operation and facilitate the optimization of tracking algorithms for underwater environments. The proposed system design demonstrates the potential of 3D visualization tools in advancing underwater multi-target tracking research and algorithm development. © 2025 IEEE.

关键词： Target tracking

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A Comprehensive Evaluation of Methods For DDoS Attack Classification 8

A Comprehensive Evaluation of Methods For DDoS Attack Classi...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Pipaliya, Bhakti Gandhi, Ankita Sutaria, Kruti Parul University Piet Computer Engineering Department Gujarat Vadodara India Parul University Piet Computer Science & Engineering Department Gujarat Vadodara India

ISBN: (纸本)9798350376425

Distributed denial-of-service (DDoS) attacks may disrupt the normal functioning of the targeted networks, severely impacting internet services. Classifying distributed denial of service attacks is essential for developing effective mitigation measures, and this research aims to provide readers with a solid grounding in the subject. Among this project's goals are the collection and organization of data pertaining to distributed denial of service attacks, as well as the investigation of potential defenses and countermeasures. The purpose of this article is to provide a comprehensive review of the present state of DDoS attack classification, with an emphasis on important deep learning and machine learning approaches. Beneficial to the field, the paper summarizes recent developments and suggests avenues for further study. In the case of effective DDoS detection, this is especially the case when choosing features and using deep learning algorithms. © 2024 IEEE.

关键词： Adversarial machine learning

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Disparity Computation With Low Intensity Quantization on Stereo Image Pairs

IEEE Transactions on Computational Imaging

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IEEE Transactions on Computational Imaging 2024年 10卷 318-328页

作者： Lin, Huei-Yung Hsu, Tsai-Yu National Taipei University of Technology Department of Computer Science and Information Engineering Taipei10608 Taiwan National Chung Cheng University Department of Electrical Engineering Minhsiung621301 Taiwan

From variate bit-rate stereo matching, it is observed that the image pair with a low intensity quantization level is still capable of providing good disparity maps. In this article, a mathematical model representing the level of disparity discontinuity is proposed to formulate the mismatching prediction based on the intensity quantization. It is used to derive the minimum quantization level for quality-assured stereo matching. Due to the high computational cost of stereo processing, the reduction of image data usage will benefit both network training and real-time inference. The formulation presented in this work is investigated extensively for various types of scenes for disparity computation. In the experiments, the feasibility of our approach is validated with Middlebury, KITTI 2015, and synthetic Scene Flow datasets. © 2015 IEEE.

关键词： Image coding

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Metaheuristics Based Node Localization Approach for Real-Time Clustered Wireless Networks

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computer Systems Science & engineering 2023年第1期44卷 1-17页

作者： R.Bhaskaran P.S.Sujith Kumar G.Shanthi L.Raja Gyanendra Prasad Joshi Woong Cho Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Computer Science and Engineering MEA Engineering CollegeMallapuram679325KeralaIndia Department of Electronics and Communication Engineering Rajalakshmi Institute of TechnologyKuthambakkamChennai600124India Department of Electronics and Communication Engineering Sri Eshwar College of EngineeringCoimbatore641202India Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea Department of Software Convergence Daegu Catholic UniversityGyeongsan38430Korea

In recent times,real time wireless networks have found their applicability in several practical applications such as smart city,healthcare,surveillance,environmental monitoring,*** the same time,proper localization of nodes in real time wireless networks helps to improve the overall functioning of *** study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization(IM-EECNL)approach for real-time wireless *** proposed IM-EECNL technique involves two major processes namely node localization and ***,Chaotic Water Strider Algorithm based Node Localization(CWSANL)technique to determine the unknown position of the ***,an Oppositional Archimedes Optimization Algorithm based Clustering(OAOAC)technique is applied to accomplish energy efficiency in the ***,the OAOAC technique derives afitness function comprising residual energy,distance to cluster heads(CHs),distance to base station(BS),and *** performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency.A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio(PDR)of 0.985.

关键词： Wireless networks real time applications clustering node localization energy efficiency metaheuristics

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Dynamic generative R-CNN

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Neural Computing and Applications 2025年第10期37卷 7107-7120页

作者： Saffarini, Rasha Khamayseh, Faisal Awwad, Yousef Sabha, Muath Eleyan, Derar IT Engineering Arab American University Jenin Palestine Department of Computer Science Palestine Polytechnic University Hebron Palestine Department of Computer System Engineering Palestine Technical University Tulkarm Palestine Department of Multimedia Technology Arab American University Jenin Palestine Department of Applied Computing Palestine Technical University Tulkarm Palestine

Different techniques have been developed for object detection and recognition. These techniques can be divided into single-shot and two-shot methods. Single-shot methods focus on real-time applications, while two-shot methods are used in applications requiring higher accuracy. However, different versions of the two-shot techniques produce limited results in terms of accuracy and speed, or both. Therefore, this study proposes a novel model called dynamic generative R-CNN (DGR-CNN) that reduces the number of proposed regions using a dynamic programming model that applies the graph similarity method over graph-based image segmentation. Additionally, the proposed model employs DCGAN technique to improve detection performance. DGR-CNN reduces the overall detection and classification time and enhances the detection accuracy. The PASCAL VOC2007 and MS COCO datasets were utilized to evaluate the model. The results showed that DGR-CNN significantly reduces the number of candidate regions compared to the selective search algorithm employed in R-CNN and fast R-CNN. Although fast R-CNN utilizes 2000 regions and faster R-CNN utilizes 300 regions, DGR-CNN reduces the number of regions to approximately 130. The mean average precision of the proposed method was 75.1% on the PASCAL VOC2007, while fast and faster R-CNN scored 66.9% and 69.9%, respectively. Moreover, the DGR-CNN model significantly improved the classification accuracy when tested on the MS COCO dataset, achieving an MAP of 68.76%, compared with 32.64% and 42.3% for fast and faster R-CNN. This increase in accuracy was achieved without significantly compromising the speed compared with faster R-CNN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Image segmentation

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Hybrid Technique-based Optimal Energy Management in Smart Home Appliances

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IEIE Transactions on Smart Processing and Computing 2024年第5期13卷 425-434页

作者： Shirley, C.P. Depaa, R.A.B. Priya, A. Sarala, R. Solanki, Rajdeep Singh Malini, K.V. Venkatakrishna Reddy, Ch. Department of Computer Science and Engineering Karunya Institute of Technology and Sciences Coimbatore India Department of Civil Engineering Dr. M.G.R Educational and Research Institute Maduravoyal Chennai95 India School of Architecture Koneru Lakshmaiah Educational Foundation Andhra Pradesh Guntur India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai Rameshwaram High Road Viraganoor Madurai625009 India Department of Computer Science Medi-Caps University Indore India Department of Electrical and Electronics Engineering Sri Sairam College of Engineering Anekal India Department of Electrical and Electronics Engineering Chaitanya Bharathi Institute of Technology Hyderabad75 India

This manuscript presents a hybrid method for optimal energy management in smart home appliances. The proposed approach combines the Ebola Optimization Search Algorithm (EOSA) with the performance of spiking neural networks (SNN). The key objective of the proposed strategy is cost reduction through day-ahead load scheduling while also considering the best demand response (DR), and self-consumption of photovoltaic (PV). The EOSA method manages air conditioning (AC) and ensures thermal comfort. In addition, the SNN method was used to predict the optimal system control signal. Comparative analysis with existing systems, such as PSO, RFA, and BCO, showed that the proposed strategy resulted in 13% cost savings. The proposed system considered parameters, such as solar radiation, occupant presence, and electricity prices. The proposed technique is implemented in the MATLAB software, and the performance is compared with existing approaches. The efficiency of the proposed approach is higher than the existing techniques. © 2024 The Institute of Electronics and Information Engineers.

关键词： Demand response

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Robust String Stabilization of Connected Vehicles with Jerk Feedforward

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IEEE Transactions on Automatic Control 2024年第6期70卷 3992-3999页

作者： Ma, Guoqi Sam Ge, Shuzhi The Department of Mechanical Engineering Texas A&M University College StationTX77843 United States The Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore

This technical note investigates the effect of communicated jerk on string stabilization of connected vehicles with uncertain parasitic actuation lags. First, by feeding forward the jerk of the preceding vehicle through Vehicle-to-Vehicle (V2V) communication, a modified cooperative adaptive cruise control law is formulated. Second, the corresponding governing equation which characterizes the propagation of the inter-vehicular spacing errors along the stream is deduced. Based on the resultant inter-vehicular spacing error propagation equation, the robust internal stability criterion is first derived. Then, the robust string stability condition is elaborated together with a codesign procedure developed for the coupled control gains and the time headway with respect to the model uncertainty bounds in a stepwise decoupling manner. In addition, utilizing the established dependent relation of the feasible time headway on the control gains, the conditions such that the implementable time headway with jerk feedforward is smaller than the lower bound of the time headway for the case without jerk feedforward are obtained. An illustrative example and comparative numerical simulations are finally provided to corroborate the effectiveness of the achieved theoretical results. ©1963-2012 IEEE.

关键词： Adaptive cruise control

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