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

作者： Hansora, Pinal Prajapati, Viral Thakkar, Amit Rathod, Ajaysinh Mannanuddin, Khaja Alsarmi, Haitham Said Naseer Cspit Charusat Cse Department Changa388421 India Snpit It Department Umrakh India Department of Computer Science & Design Gujarat Vasad India Sr University School of Cs & Ai Department of Computer Science & Engineering Hasanparthy Warangal India Sohar International Bank Muscat Oman

ISBN: (纸本)9798331509675

Quantum computing are breakthroughs aimed to revolutionize several sectors, although they also present considerable cybersecurity threats. Quantum computers can manage quite greater quantities of information than the most powerful conventional supercomputers and has the capability to do complex computations. Despite being in the early stages of development, quantum computers have the potential to be regarded as cybersecurity concerns. Quantum computing is a phenomenon that should be seen as a technology primed to facilitate significant innovations in the future, while also presenting different concerns. The quantum cybersecurity threat encompasses data breaches of sensitive health and financial information, challenges to the integrity of digital assets, and vulnerabilities in the encryption supporting cryptocurrencies, among other possible risks. This review paper focuses on quantum computing and its effects on different security methods in the present generation. It covers the quantum computer threats to cryptography, post quantum cryptography and a comparison of different approaches to cryptography. This work also analyzes machine learning and ai in quantum security and its challenges and trends for the future. The literature review has incorporated references from more than 25 reputed Scopus-listed journal and conference proceeding. © 2025 IEEE.

关键词： Cybersecurity

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Support Vector Machines for Soil Quality Assessment in Sustainable Farming 7

Support Vector Machines for Soil Quality Assessment in Susta...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Nainwal, Ankita Alabdeli, Haider Varshney, Neeraj Deepak, A. Reddy, N. V. Uma Reddy, K. Jyothsna Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Computer Engineering and Applications Mathura India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Tamilnadu Chennai India New Horizon College of Engineering Department of Artificial Intelligence & Machine Learning Bangalore India Mlr Institute of Technology Department of CSE-AI&ML Telangana Hyderabad India

ISBN: (纸本)9798350350067

In order to understand the soil's health, fertility, and crop appropriateness, soil quality assessments are an important part of sustainable agricultural techniques. Using their accuracy and resilience in dealing with complicated, nonlinear datasets, Support Vector Machines (SVMs) are investigated in this research as a potential tool for soil quality evaluation. Our study presents a comprehensive methodology for using SVM to analyze soil properties, including pH, nutrient content, moisture levels, and organic matter. We employ a dataset encompassing diverse soil samples from different geographical regions and farming conditions. The proposed SVM-based model is trained and validated using this dataset, demonstrating high predictive accuracy and reliability in classifying soil quality. Additionally, we compare the performance of SVM with other machine learning techniques such as Random Forest and k-Nearest Neighbors, highlighting the advantages of SVM in terms of precision and computational efficiency. The results indicate that SVM can serve as a powerful tool for farmers and agronomists, facilitating informed decision-making and promoting sustainable agricultural practices. In conclusion, this research underscores the potential of SVM in soil quality assessment, contributing to the advancement of precision agriculture. Future work will focus on integrating SVM with remote sensing technologies and expanding the dataset to include more diverse soil types and environmental conditions. © 2024 IEEE.

关键词： Support vector machines

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An Assessment of Neural Network Efficacy in Hospital Readmission Prediction for Diabetic Patients

An Assessment of Neural Network Efficacy in Hospital Readmis...

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International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS)

作者： Atharv Revankar Akshay Hegde Prarthana Rajapurohit Lakshmi Snigdha Paladugula Srinivas K S Computer Science (AI & ML) PES University RR Campus Bangalore India Computer Science and Engineering PES University RR Campus Bangalore India

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

Predicting hospital readmission among diabetic patients is an important aspect of improving the conditions of the patients and ensuring healthcare resources are optimally used. This paper seeks to find how effective neural networks in predicting hospital readmissions are by increasing the accuracy. The UCI Machine Learning Repository dataset was pre-processed for missing values, outliers, and categorical encoding. Class imbalance was also handled using the SMOTE technique. The final developed neural network with Adam optimizer and binary cross-entropy loss function found an accuracy of 93% from a balanced test set and 91% for the original imbalanced dataset. Class 0 (no readmission) found precision of 0.91; class 1 (readmission) found precision of 0.70. Recall for class 0 was 1.00, and for class 1, it was 0.01. The AUC-ROC for the imbalanced dataset was 0.65. Such findings highlight the potential of the model in recognizing patients at high risk and underline its utility, at the same time as it shows the model's boundaries. The developed research fosters knowledge on how neural networks can find application in this field and will support healthcare workers in developing preventive interventions, while also revealing areas for possible further refinement.

关键词： Training Knowledge engineering Accuracy Hospitals Computational modeling Neural networks Machine learning Predictive models Diabetes Testing

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Non-invasive estimation of the powder size distribution from a single speckle image

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Light(science & Applications) 2024年第10期13卷 2221-2230页

作者： Qihang Zhang Ajinkya Pandit Zhiguang Liu Zhen Guo Shashank Muddu Yi Wei Deborah Pereg Neda Nazemifard Charles Papageorgiou Yihui Yang Wenlong Tang Richard D.Braatz Allan S.Myerson George Barbastathis Department of Electrical Engineering and Computer Science Massachusetts Institute of TechnologyCambridgeMA 02139USA Singapore-MIT Alliance for Research and Technology(SMART)Centre Singapore 117543Singapore Department of Chemical Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA Department of Mechanical Engineering Massachusetts Institute of TechnologyCambridgeMA 02139USA Process Chemistry Development Takeda Pharmaceuticals International Co40 Landsdowne StCambridgeMA 02139USA ShinrAI Center for AI/ML Data Sciences InstitutesTakeda Pharmaceuticals International Co650 E Kendall StCambridgeMA 02142USA Present address:Department of Precision Instruments Tsinghua UniversityBeijing 100084China

Non-invasive characterization of powders may take one of two approaches:imaging and counting individual particles;or relying on scattered light to estimate the particle size distribution(PSD)of the *** former approach runs into practical difficulties,as the system must conform to the working distance and other restrictions of the imaging *** latter approach requires an inverse map from the speckle autocorrelation to the particle *** principle relies on the pupil function determining the basic sidelobe shape,whereas the particle size spread modulates the sidelobe *** recently showed that it is feasible to invert the speckle autocorrelation and obtain the PSD using a neural network,trained efficiently through a physics-informed semi-generative *** this work,we eliminate one of the most time-consuming steps of our previous method by engineering the pupil *** judiciously blocking portions of the pupil,we sacrifice some photons but in return we achieve much enhanced sidelobes and,hence,higher sensitivity to the change of the size *** result is a 60×reduction in total acquisition and processing time,or 0.25 seconds per frame in our *** real-time operation in our system is not only more appealing toward rapid industrial adoption,it also paves the way for quantitative characterization of complex spatial or temporal dynamics in drying,blending,and other chemical and pharmaceutical manufacturing processes.

关键词： speckle size distribution

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A Comprehensive Review on Deep Learning Based Fall Detection in Elderly People

A Comprehensive Review on Deep Learning Based Fall Detection...

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Machine Learning and Autonomous Systems (ICmlAS), International Conference on

作者： S. Venkata Suryanarayana Lakshmi H.N Posimsetti T Chiranjeevi Swamy D. Bhanu Mahesh Department of Computer Science and Engineering (Data Science) CVR College of Engineering Hyderabad India Department of Computer Science and Engineering (AI&ML) CVR College of Engineering Hyderabad India Department of Artificial Intelligence and Machine Learning Swarnandhra College of Engineering and Technology Narsapuram Andhra Pradesh India Department of Computer Science and Engineering KMIT Hyderabad India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Elderly people are more vulnerable to falls, which can result in serious injuries, a lower quality of life, and higher medical expenses. Traditional fall detection methods, such as wearable sensors or vision-based systems, face limitations in accuracy, comfort, and practicality. Recent advancements in deep learning have significantly improved fall detection capabilities by leveraging powerful computational models for data analysis and feature extraction. With an emphasis on important methodologies, architectures, and datasets utilized in the area, this paper offers a thorough literature analysis of deep learning-based fall detection strategies. It investigates several deep learning techniques applied to sensor-based and video-based fall detection systems, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models. The review also emphasizes the difficulties encountered in practical application, including data privacy and computing cost, and the need for robust datasets. Finally, future research directions are proposed to address current limitations and improve fall detection systems' dependability and effectiveness for elderly care.

关键词： Deep learning Recurrent neural networks Reviews Computational modeling Convolutional neural networks Fall detection Older adults Wearable sensors Long short term memory Injuries

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Real-Time Gesture Recognition for Volume Control Using Python and ml Techniques 5

Real-Time Gesture Recognition for Volume Control Using Pytho...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Musthafa, Syed Murugan, Lavanya Mutharasu, M. Vadivel, T. Fathima, H. Leelavathi, M. M.Kumarasamy College of Engineering Department of Information Technology Tamil Nadu Karur639 113 India American International University Computer Science Kuwait Andhra Pradesh Madanapalle517325 India KSR College of Arts and Science Department of Computer Applications Tamil Nadu Tiruchengode637 215 India Sree Saraswathi Thyagaraja College Department of AI & ML Tamil Nadu Pollachi642205 India

ISBN: (纸本)9798331530013

This research study proposes a gesture-based volume control system that combines computer vision and machine learning to improve user interaction with audio devices. By applying the OpenCV library for real-time video processing and MediaPipe's hand-tracking module, the system interprets hand gestures to control the volume. This system measures the distance between the index finger and thumb. Using this distance, the system adjusts the system's audio volume by using the Pycaw library. The purpose is to have a touchless interface wherein the user can control audio levels by simple hand movements to enhance accessibility and usability across various environments. This is an promising alternative to traditional input methods as the demand for more natural human-computer interaction grows, this gesture-based approach represents a leveraging computer vision and machine learning technique, which accurately detects gestures in volume control. The goal of this project is to design a touchless volume control system that relies on gesture input, computer vision, and machine learning techniques in order to bring an intuitive user experience. The proposed system integrates OpenCV for real-time video processing and makes use of Media Pipe's advanced hand-tracking abilities to identify gestures, which are the distance between the thumb and index finger, to control the volume of audio. Pycaw is used to interact with the audio output of the system to dynamically control the volume. This gesture-based interface is designed to improve accessibility, usability, and user experience by providing a modern, intuitive alternative to traditional volume control methods. © 2024 IEEE.

关键词： Gesture recognition

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Emotion Recognition in Visual Art on Color and Texture Analysis using Improved Vision Transformer

Emotion Recognition in Visual Art on Color and Texture Analy...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： K G Suma Zaed Balasm N. Sasirekha Veera Manikandan P K. Sandhya Department of AI & ML School of Computer Science and Engineering Vellore Institute of Technology Andhra Pradesh India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering Sona College of Technology Salem India Department of Electrical and Electronics Engineering Dhanalakshmi Srinivasan College of Engineering and Technology India Department of Computer Science and Engineering (AI&ML) Mohan Babu University Tirupati India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

Nowadays, Emotion Recognition plays a key role in social communication interaction in which human emotions are interpreted from facial expressions and speech. Several challenges occur in emotion recognition such as capturing context of any emotion also facial attributes make the recognition process complex. To overcome these drawbacks Improved Vision Transformer (IViT) classifier is proposed for emotion recognition. Initially, the input data is collected from the Japanese Female Facial Expression (JAFFE) to collect color features and visual features. Then, the input data is processed using min max scaling method which normalizes pixel values. After that, colour and texture-based features are extracted by utilizing Rotation Invariant Local Binary Patterns (RI-LBP) with Hue Saturation and Value (HSV) and Red Green Blue (RGB). Finally, classification is performed by IViT, which processes images as sequences of patches. Hence, the proposed IViT model achieved better output when compared to existing Visual–Audio Attention Network (VAANet) in terms of accuracy by obtaining 96.6% respectively.

关键词： Emotion recognition Visualization computer vision Art Accuracy Image color analysis Face recognition Speech recognition Feature extraction Transformers

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Improving the Interpretability through Maximizing Mutual Information for EEG Emotion Recognition

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IEEE Transactions on Affective Computing 2024年第2期16卷 744-757页

作者： Yang, Hua Philip Chen, C.L. Chen, Bianna Zhang, Tong North Carolina State University Department of Computer Science United States South China University of Technology Guangdong Provincial Key Laboratory of AI Large Model and Intelligent Cognition The School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center The Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Trustworthy Graph Neural Networks (GNNs) for EEG emotion recognition should identify emotions accurately and elucidate corresponding rationales. Current GNNs have achieved notable performance by dynamically modeling emotional connections between EEG channels. However, these GNNs lack interpretability due to the absence of explicit rationale behind their predictions. This paper conducts a comprehensive identification of important EEG channels to enhance the interpretability of EEG emotion recognition from the perspective of mutual information. Specifically, an Adjacency-Explainable Graph Neural Network (AEG) for ante-hoc interpretability is proposed to capture genuine EEG emotional connections, which gives a theoretical guarantee to remove spurious connections. Moreover, a Channel-wise Adaptive Class Activation Mapping Explainer (CACA) for post-hoc interpretability is developed to locate the EEG channels that contribute most to predictions. Experimental results on three datasets, i.e., SEED, SEED-IV, and DREAMER, prove that imbuing training processes with enhanced interpretability ensures significant performance improvements in emotion recognition. Quantitative comparisons of post-hoc interpretability also demonstrate the superiority of CACA. Furthermore, this paper illustrates two potential applications of the proposed methodologies, showing their broader utility and significance. © 2010-2012 IEEE.

关键词： Graph neural networks

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Optimized Feature Selection and Transfer Learning for Accurate Fundus Image Classification in Glaucoma Detection 3

Optimized Feature Selection and Transfer Learning for Accura...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Mahaveerakannan, R. Rajakumar, B. Rajesh Sharma, R. Pushpa, S. Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India JNN Institute of Engineering Department of AI & DS Chennai Periyapalayam Highway Tamil Nadu Chennai601102 India Alliance University Alliance College of Engineering and Deaign Department of Computer Science and Engineering Bangalore India St. Peter's Institute of Higher Education and Research Avadi Department of Computer Science and Engineering Chennai54 India

ISBN: (纸本)9798350354171

There needs to be a global response to the alarming rise of retinal disorders. One of the most serious eye diseases, glaucoma can lead to permanent blindness. Manual evaluations are commonly used to diagnose glaucoma by ophthalmologists. However, this method is laborious, subjective, and prone to errors. In order to bolster and support the current diagnostic methods, it is essential to establish automated procedures. Researchers generally agree that optic dise (OD) and optic cup (OC) segmentation can help in glaucoma screening using fundus imaging. A number of studies have suggested decision assistance systems for glaucoma diagnosis that are based on artificial intelligence (ai). The precision and efficiency of current ai-based approaches, however, are severely lacking. The segmentation process becomes more difficult when dealing with objects of varying sizes, backgrounds, and pixel intensity values. At the outset, the flamingo search optimisation algorithm (FSOA) enhances classification accuracy by optimally selecting characteristics. This research proposes a model for automatic fundus picture classification using a deep residual neural network (ResNet) and the transfer learning technique. By including a residual structure, ResNet34 is able to avoid information loss while extracting high-quality fundus image features, making it an outstanding image classification tool. With fewer fundus images, the transfer learning method can better express the information for classification by abstracting the deep features from the shallow features. © 2024 IEEE.

关键词： Image segmentation

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Multiple Vehicles Detection and Tracking Using Ground and Aerial Images for Vehicular Management System 10th

Multiple Vehicles Detection and Tracking Using Ground and ...

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10th International Conference on Future Data and Security engineering, FDSE 2023

作者： Ali, Sardar Jaffar Omer, Muhammad Raza, Syed Muhammad Choo, Hyunseung Department of AI Systems Engineering Sungkyunkwan University Seoul Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Seoul Korea Republic of

ISBN: (纸本)9789819982950

Vehicle Management Systems (VMSs) are crucial in location-based services for optimizing transportation efficiency, traffic flow, and overall mobility for individuals and businesses. To further enhance their performance, smart VMSs must leverage existing traffic data it gathers by detecting and tracking vehicles periodically. However, despite recent advancements in traffic monitoring, there remains a need for an efficient and up-to-date system, especially in dense traffic or situations requiring aerial monitoring. This paper focuses on the importance of detecting and tracking multiple vehicles in such systems, presenting a two-way validation architecture that integrates CCTV and drone images to enhance accuracy and authenticity. The architecture utilizes the YOLO pretrained model for CCTV images, which is subsequently fine-tuned for drone images. Results demonstrate an accuracy of 99% and a mean average precision of up to 97%. Lastly, the proposed architecture is used for two real-time applications of vehicle counter and lane detection which shows its validity and ability in aforementioned situations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Drones

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