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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Mamatha, T. Aditya, G.V. Meghana, S. Anvitha, S. Veekshitha, P. Sreenidhi Institute Of Science And Technology Computer Science And Engineering Department Telangana India Institute Of Science And Technology Computer Science And Engineering Department Telangana India

ISBN: (纸本)9798350318609

Automatic timetable generation is a complex optimization problem with practical applications in various domains such as education, healthcare, and event management. The challenge lies in efficiently scheduling activities while satisfying numerous constraints and objectives. In this study, we propose an OptiSchedule algorithm for automatic timetable generation. The algorithm employs a combination of heuristic search techniques and metaheuristic optimization methods to iteratively improve timetable solutions. It starts with initializing a timetable grid and iteratively refines the solution by generating neighbouring solutions and selecting the most promising ones based on an evaluation function. Through extensive testing and validation, our OptiSchedule algorithm demonstrates significant improvements in timetable quality and efficiency compared to existing approaches. The algorithm effectively minimizes conflicts, optimizes resource utilization, and balances workload distribution. Furthermore, it provides flexibility for users to input constraints and preferences, allowing customization to specific scheduling requirements. The OptiSchedule algorithm represents a significant advancement in the field of automatic timetable generation. Its ability to produce high-quality schedules while considering complex constraints makes it a valuable tool for educational institutions, healthcare facilities, and businesses alike. By streamlining scheduling processes and optimizing resource allocation, OptiSchedule contributes to improved operational efficiency and overall organizational performance. Through rigorous experimentation and evaluation, our study demonstrates the effectiveness of the OptiSchedule algorithm in improving timetable quality and reducing scheduling overhead. Compared to traditional methods, OptiSchedule generates timetables with fewer conflicts and better resource utilization, leading to enhanced productivity and satisfaction among stakeholders. Moreover, its fl

关键词： Iterative methods

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An Optimised Task Scheduling of Remote Sensing Data Processing for Smart Patient Health Monitoring

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Remote Sensing in Earth Systems Sciences 2024年第4期7卷 542-553页

作者： Ravikishan, Surapaneni Reddy, B. Eswar Rao, K. V. Sambasiva Department of Computer Science and Engineering JNTUA Anantapur 515002 India Department of Computer Science and Engineering NRI Institute of Technology Vijayawada 521212 India

In healthcare, remote sensing technologies are popular for smart patient health monitoring. Real-time health assessment and early intervention are possible using remote sensing data from wearable sensors and imaging equipment. However, processing and analysing huge remote sensing data are complex. Task scheduling improves processing workflow for accurate and fast health monitoring. This research offered the optimised task scheduling of remote sensing data processing for smart patient health monitoring, to describe the processing activities such as data preparation, task scheduling algorithms, and decision-making involved in data analysis from remote sensing. We used datasets associated with Internet of Things (IoT) devices that patients wear as sensors on their wrists. We used min–max normalisation to standardise the data’s scale after preprocessing the data and choose the task scheduling algorithm methods to distribute work among resources effectively. We proposed the multi-objective dwarf mongoose optimised with Deep Q Network (MODMO-DQN), which aims to address the task scheduling issue for remote sensing data processing in the Internet of Things by monitoring vital sign data of faraway patients. Next, to optimise the e-health services, an optimisation module built on top of MODMO-DQN is developed. The experiment compares the variation in completion time in the remote sensing data process. The results showed that the proposed perform to compare the existing methods. This will make it possible to evaluate the suggested works in terms of throughput, latency, energy usage, and task scheduling efficiency. The performance analysis shows that the recommended method is successful and may be a feasible and efficient solution to track patient vital sign data in Internet of Things-based e-Health. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Cloud E-health Min–max normalisation Multi-objective dwarf mongoose optimised with Deep Q Network (MODMO-DQN) Remote sensing Smart healthcare Task scheduling algorithm

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A top-down character segmentation approach for Assamese and Telugu handwritten documents

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3275-3287页

作者： Dutta, Prarthana Muppalaneni, Naresh Babu Department of Computer Science and Engineering National Institute of Technology Assam Silchar786003 India Department of Computer Science and Engineering The Assam Kaziranga University Assam Jorhat785006 India Indian Institute of Information Technology Design and Manufacturing Kurnool Andhra Pradesh Kurnool India

Digitization offers a solution to the challenges associated with managing and retrieving paper-based documents. However, these paper-based documents must be converted into a format that digital machines can comprehend, as they primarily understand alphanumeric text. This transformation is achieved through Optical Character Recognition (OCR), a technology that converts scanned image documents into a format that machines can process. A novel top-down character segmentation approach has been proposed in this work, involving multiple stages. Our approach began by isolating lines from handwritten documents and using these lines to segment words and characters. To further enhance the character segmentation, a Raster Scanning object detection technique is employed to isolate individual characters within words. Thus, the character segmentation results are integrated from the results of the vertical projection and raster scanning. Recognizing the significance of advancing digitization of handwritten documents, we have chosen to focus on the regional languages of Assam and Andhra Pradesh due to their historical and cultural importance in India’s linguistic diversity. So, we have collected datasets of handwritten texts in Assamese and Telugu languages due to their unavailability in the desired form. Our approach achieved an average segmentation accuracy of 93.61%, 85.96%, and 88.74% for lines, words, and characters for both languages. The key motivation behind opting for a top-down approach is two-fold: firstly, it enhances the accuracy of character recognition, and secondly, it holds the potential for future use in language/script identification through the utilization of segmented lines and words. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Object detection

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Finding the transcription factor binding locations using novel algorithm segmentation to filtration (S2F)

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3347-3358页

作者： Theepalakshmi, P. Srinivasulu Reddy, U. Department of Computer Science and Engineering Gandhi Institute of Technology and Management Karnataka Bengaluru India Machine Learning and Data Analytics Lab Center of Excellence in Artificial Intelligence Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India

The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered which entails finding the length motifs which differ by at most d substitutions. However, identifying the high-quality pattern (, d) is challenging. It is intended to address the above problem with motif discovery and handle it using the proposed algorithm S2F (Segmentation to Filtration) based on the qPMS (quorum Planted Motif Search) algorithm model. From the entire DNA sequences, five percent are chosen at random to be used in the motif discovery process. This random sub segment (subseg) portion is split up into base, sub k-mers, and its sizes (motif length ()) are determined by the iterative approach. Corresponding to the sizes of and d (mutations), the k-mers are chosen which participated in filtration techniques and the base k-mer count and frequency are updated. The highest frequency of k-mer is recognized as the motif. The algorithm’s performance was evaluated using the two real datasets Escherichia coli cyclic AMP receptor protein (CRP) and mouse Embryonic Stem Cell (mESC) ChIP-seq (Chromatin Immuno Precipitation) dataset. Results from the experiments show that S2F can identify the motifs and appear faster compared to previous state-of-the-art PMS (Planted Motif Search) and qPMS algorithms. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： DNA sequences

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Optimizing Waste Management: Integrated Pollution Detection and Systematic Reporting for Sustainable Disposal 2

Optimizing Waste Management: Integrated Pollution Detection ...

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Samuel Prabakaran, M. Sophia, S. Sophas Samuel, S. Karunya Institute of Technology and Sciences Department of Computer Engineering Coimbatore India Karunya Institute of Technology and Science Department of Computer Science and Engineering Coimbatore India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365269

Effective waste management and pollution control are paramount for sustainable environmental stewardship. This study presents a comprehensive approach leveraging cutting-edge technologies such as YOLO object recognition, Faster R-CNN, and SVM-based AQI computation. The system integrates YOLOv7 for real-time rubbish classification, facilitating swift and precise trash sorting and disposal decisions. Additionally, it employs Faster R-CNN for fog density analysis, enabling thorough estimations of air pollution levels. To calculate the Air Quality Index (AQI), support vector machine (SVM) algorithms combine various air quality indicators, ensuring thorough and accurate assessments. Enhanced trash classification capabilities enable more accurate evaluation of the environmental impacts of waste management systems. By integrating fog density monitoring with AQI calculation, the approach offers comprehensive trend analysis of pollution, aiding in the formulation of data-driven environmental policies and effective pollution management measures. Furthermore, the system evaluates soil quality, complementing its waste management and pollution control functions. Despite its advancements, the system faces challenges in real-world implementation, including data acquisition, model training, and system integration. Addressing these challenges is critical for realizing the full potential of integrated waste management and pollution control systems. © 2024 IEEE.

关键词： Data acquisition

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Enhancing CCTV Violence Detection: A Comparative Study of Deep Learning Models for Violence Detection in Surveillance Videos 8

Enhancing CCTV Violence Detection: A Comparative Study of De...

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8th IEEE International Conference on Information and Communication technology, CICT 2024

作者： Rodrigues, Kenneth Dsouza, Glen Phansopkar, Omkar Bide, Pramod Sardar Patel Institute of Technology Department of Computer Science and Engineering Mumbai India Sardar Patel Institute of Technology Department of Computer Engineering Mumbai India

ISBN: (纸本)9798331531539

With the large number of CCTV cameras located worldwide, ensuring people's safety has become much easier. Despite this, it is impossible to keep track of 100s of CCTV cameras simultaneously. Therefore, deep learning methods of violence detection from surveillance footage have been proposed to reduce human intervention. This solution presents a comparison of the performance of three-dimensional (3D) Convolutional Neural Networks (CNNs), VGG networks, and MobileNet models combined with Long Short-Term Memory (LSTM) units and Multi-Stream Networks to detect violence in CCTV video footage, providing a comprehensive analysis of these models based on accuracy, hardware requirements, and other performance metrics essential to real-world applications. The majority of the existing systems are trained on violent footage from sports or movies, the models in this solution are trained on a combination of datasets consisting of CCTV footage which provides an accurate representation of real-world surveillance footage, thus improving model accuracy. Existing research does not study the effect of combining very deep CNN models like VGG with advanced techniques like multi-stream models. This solution explores the impact of integrating such models to create a more robust solution. All the models proposed and evaluated have produced accurate results and are capable of identifying violence based on surveillance footage, with an accuracy of up to 98.46%. Unlike other solutions that do not take into account the memory and prediction time of different models, this solution identifies the ideal model depending on the available resources and infrastructure, helping make a decision based on real-world requirements. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on facial expression recognition based on multimodal data fusion and neural network

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International Journal of Wireless and Mobile Computing 2024年第1期27卷 47-55页

作者： Han, Yi Wang, Xubin Lu, Zhengyu School of Mechanical Science and Engineering Huazhong University of Science and Technology Hubei Wuhan China Anyang Institute of Technology Department of Computer Science and Information Engineering Henan Province Anyang China School of Computer Science and Information Engineering Shanghai Institute of Technology FengXian District Shanghai China

Facial expression recognition is a challenging task when neural network is applied to pattern recognition. Most of the current recognition research is based on single source facial data, which generally has the disadvantages of low accuracy and low robustness. In this paper, a neural network algorithm of facial expression recognition based on multimodal data fusion is proposed. The algorithm is based on the multimodal data, and it takes the facial image, the histogram of oriented gradient of the image and the facial landmarks as the input, and establishes Convolutional Neural Network (CNN) designed to extract features from facial image, neural network designed to extract features from facial Landmarks and Neural Network (LNN) designed to extract features from Histogram of gradient (HNN), three sub-neural networks to extract data features, using multimodal data feature fusion mechanism to improve the accuracy of facial expression recognition. Experimental results show that, the algorithm has a great improvement in accuracy, robustness and detection speed. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Data fusion

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Artificial Intelligence Enabled Future Wireless Electric Vehicles with Multi-Model Learning and Decision Making Models

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Tsinghua Science and technology 2024年第6期29卷 1776-1784页

作者： Gajula Ramesh Anil Kumar Budati Shayla Islam Louai A.Maghrabi Abdullah Al-Atwai Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering&TechnologyHyderabad 500090India Institute of Computer Science and Digital Innovation(ICSDI) UCSI UniversityKuala Lumpur 56000Malaysia also with Department of ECE KoneruLakshmaiah Education FoundationHyderabad 500090India ICSDI UCSI UniversityKuala Lumpur 56000Malaysia Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah 21448Kingdom of Saudi Arabia Department of Computer Science Applied CollegeUniversity of TabukTabuk 47512Kingdom of Saudi Arabia

In the contemporary era,driverless vehicles are a reality due to the proliferation of distributed technologies,sensing technologies,and Machine to Machine(M2M)***,the emergence of deep learning techniques provides more scope in controlling and making such vehicles energy *** existing methods,it is understood that there have been many approaches found to automate safe driving in autonomous and electric vehicles and also their energy ***,the models focus on different aspects *** is need for a comprehensive framework that exploits multiple deep learning models in order to have better control using Artificial Intelligence(AI)on autonomous driving and energy *** this end,we propose an AI-based framework for autonomous electric vehicles with multi-model learning and decision *** focuses on both safe driving in highway scenarios and energy *** deep learning based framework is realized with many models used for localization,path planning at high level,path planning at low level,reinforcement learning,transfer learning,power control,and speed *** reinforcement learning,state-action-feedback play important role in decision *** simulation implementation reveals that the efficiency of the AI-based approach towards safe driving of autonomous electric vehicle gives better performance than that of the normal electric vehicles.

关键词： wireless vehicles deep learning multi-model learning reinforcement learning Artificial Intelligence(Al)

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Cerebral cortex contributions to Parkinson's disease tremor:what exactly generates the tremor?

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Neural Regeneration Research 2025年第12期20卷 3535-3536页

作者： Manuel Bange Hao Ding Muthuraman Muthuraman Informatics for Medical Technology Institute of Computer ScienceUniversity AugsburgAugsburgGermany Department of Neurology Neural Engineering with Signal Analytics and Artificial Intelligence(NESA-AI)University Clinic WürzburgWürzburgGermany

Pathological tremor is one of the cardinal symptoms in Parkinson's disease (PD).Tremor is comprised of involuntary,rhythmic,a nd oscillating movements that can vary according to the circumstances under which they occur,the body parts that are involved,and the frequency at which they *** example,tremors can be mild to severe,are stress sensitive,and can affect arms,legs,or the head (Dirkx and Bologna,2022).

关键词： tremor cortex exactly

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Enhanced Textual Image Classification with Ensemble Learning of MobileNetV2 and ResNet50

Enhanced Textual Image Classification with Ensemble Learning...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in technology, IC-SIT 2024

作者： Tripathy, Sasmita Pattanaik, Satya Ranjan Rabindranath, Mohanty Hitesh Dash, Ratnakar Centurion University of Technology and Management Department of Computer Science and Engineering Bhubaneswar India Gandhi Institute for Technology Department of Computer Science and Engineering Bhubaneswar India National Institute of Technology Rourkela Department of Computer Science and Engineering Rourkela India

ISBN: (纸本)9798350369175

Textual image classification is crucial in various applications, such as document digitization and automatic language identification. Although ensemble learning has been increasingly utilized to improve the accuracy of deep learning models, the optimal approaches for this task are still under-explored. This research evaluates the effectiveness of ensemble learning techniques for textual image classification, focusing on combining Convolutional Neural Networks (CNNs) like MobileNetV2 and ResNet50. The dataset used in this study includes images in multiple languages, adding complexity to the classification task. By leveraging ensemble learning, we enhanced the performance of these models, achieving an accuracy of 77%, surpassing the individual CNN models. These findings suggest that ensemble learning can significantly boost the effectiveness of deep learning models in textual image classification tasks. © 2024 IEEE.

关键词： Contrastive Learning

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