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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Kalpana, Ponugoti Almusawi, Muntather Chanti, Yerrolla Sunil Kumar, V. Varaprasad Rao, M. Avn Institute of Engineering & Technology Department of Computer Science and Engineering Telangana India The Islamic University Najaf Iraq Sr University School of Computer Science & Artificial Intelligence Warangal India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India Cvr College of Engineering Department of Data Science Hyderabad India

ISBN: (纸本)9798350317558

By introducing the processing and resources storage to the network edge, edge computing is a new, promising computing paradigm that drastically decreases network traffic and service latency. Many edge computing applications consist of interdependent tasks, wherein the results of one task are the inputs of another. The important and difficult problem of where to position each running task to optimise Quality-of-Service (QoS) is how to offload these tasks to edge of network. In this work, implemented a novel Deep Reinforcement Learning based Task Offloading (DRLTO) method utilized as the intelligent task offloading that uses a Directed Acyclic Graph (DAG) to represent the dependent tasks and off-policy reinforcement learning powered by a Sequence-to-Sequence (S2S) neural network. This research outcomes show that the DRLTO achieved less cloud processing time, and number of single terminal tasks, and percentage of failed task when compared to Deep reinforcement learning-based cloud-edge collaborative mobile computation offloading (DRL-CCMCO) and DeepEdge. © 2024 IEEE.

关键词： and task offloading Deep reinforcement learning Multi-access edge computing sequence to sequence neural networks

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Human Action Recognition Using Difference of Gaussian and Difference of Wavelet

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Big Data Mining and Analytics 2023年第3期6卷 336-346页

作者： Gopampallikar Vinoda Reddy Kongara Deepika Lakshmanan Malliga Duraivelu Hemanand Chinnadurai Senthilkumar Subburayalu Gopalakrishnan Yousef Farhaoui Department of Computer Science and Engineering(AI&ML) CMR Technical CampusHyderabad 501401India. Department of Information Technology Kakatiya Institute of Technology and ScienceWarangal 506015India. Department of Electronics and Communication Engineering Malla Reddy Engineering College for Women(Autonomous)Hyderabad 500100India. Department of Computer Science and Engineering S.A.Engineering College(Autonomous)Thiruverkadu 600077India. Department of Electronics and Communication Engineering Saveetha Institute of Medical and Technical Sciences(SIMATS)Chennai 602105India. Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600062India. IDMS Team STI LaboratoryFaculty of Sciences and TechniquesMoulay Ismail University of MeknèsErrachidia 25003Morocco.

Human Action Recognition(HAR)attempts to recognize the human action from images and *** major challenge in HAR is the design of an action descriptor that makes the HAR system robust for different environments.A novel action descriptor is proposed in this study,based on two independent spatial and spectral *** proposed descriptor uses a Difference of Gaussian(DoG)filter to extract scale-invariant features and a Difference of Wavelet(DoW)filter to extract spectral *** create a composite feature vector for a particular test action picture,the Discriminant of Guassian(DoG)and Difference of Wavelet(DoW)features are *** Discriminant Analysis(LDA),a widely used dimensionality reduction technique,is also used to eliminate duplicate ***,a closest neighbor method is used to classify the *** and UCF 11 datasets were used to run extensive simulations of the suggested strategy,and the accuracy assessed after the simulations were run on Weizmann datasets for five-fold cross validation is shown to perform *** average accuracy of DoG+DoW is observed as 83.6635%while the average accuracy of Discrinanat of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 80.2312%and 77.4215%,*** average accuracy measured after the simulation of proposed methods over UCF 11 action dataset for five-fold cross validation DoG+DoW is observed as 62.5231%while the average accuracy of Difference of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 60.3214%and 58.1247%,*** the above accuracy observations,the accuracy of Weizmann is high compared to the accuracy of UCF 11,hence verifying the effectiveness in the improvisation of recognition accuracy.

关键词： human action recognition difference of Gaussian difference of wavelet linear discriminant analysis Weizmann UCF 11 accuracy

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Rainfall Forecasting Using Deep Learning and Cloud Image Analysis

Rainfall Forecasting Using Deep Learning and Cloud Image Ana...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： K. RamaKrishna Reddy V. Sravya Reddy V. Sriram Sathvik Shaik Mahaboob Subhani Department of Computer Science and Engineering(AI & ML) Vignan Institute of Technology and Science

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

This study introduces a new approach to rainfall prediction using deep learning and Convolutional Neural Networks (CNNs) for analyzing cloud image data sourced from diverse platforms. Traditional methods often struggle to capture precipitation patterns' complexity, necessitating advanced technologies like CNNs. The methodology emphasizes meticulous data preprocessing, including multiple discriminations, ratio adjustment, and augmentation, optimizing input data for CNNs to enhance quality, robustness, and predictive model effectiveness. A binary classification component enhances prediction accuracy and provides valuable insights into rainfall occurrence's binary nature. Using the multi-class weather dataset (MWD), this study explores weather image classification, examining dataset structure and showcasing sample images for each weather type. Two models with distinct architectures are trained from scratch, alongside a transfer learning model utilizing the VGG 16 pre-trained model. Result demonstrate promising performance across all models on the test dataset, highlighting their ability to accurately classify weather types. This study contributes to advancing rainfall prediction methodologies, offering insights into CNN application for weather analysis and forecasting, addressing critical needs in various sectors reliant on accurate weather predictions.

关键词： Deep learning Rain Accuracy Transfer learning Predictive models Real-time systems Convolutional neural networks Forecasting Meteorology Image classification

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Machine Learning and Artificial Intelligence for Predictive Modeling in Antimicrobial Resistance Data Sets, Challenges, and Future Directions

Machine Learning and Artificial Intelligence for Predictive ...

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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： Ajeeb Sagar Vinothkumar Kolluru Ujjwal Jaiswal Gaurav Kumavat Shreyas Rajendra Hole Ankit Kumar Computer science Engineering (AI&ML) Dayananda Sagar University Bangalore India Department of Data science Stevens Institute of Technology Hoboken U.S.A. Computer Science and Engineering PES UNIVERSITY Bangalore India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

The phenomenon of Antimicrobial Resistance (AMR) is emerging strategically rather quickly and efficiently on a global scale. Because of due AMR Corona-virus outbreaks, previously effective antibiotics are becoming obsolete. The situation is only becoming dire as a result of chronic infections, increased deaths and subsequent healthcare expenditures. Increasingly, Artificial Intelligence solutions and Machine Learning strategies are contributing towards optimizing large volume data sets and offer introduction of new intervention methodologies. Within the scope of the current work, we aim to focus on data set amalgamation through Open-Source genomic data along with phenotype and environmental data sets within ml frameworks. While genomic datasets explain portion of resistance around genes, phenotype datasets gather ‘recorded’ datasets of resistance patterns and Environmental datasets monitor the distribution of AMR loops. Opportunities for improving model ethics, integration of data, and addressable generalization gaps to subsequently increase prediction accuracy are studied in focus. Predictability in AMR detection through leveraging ai increases accuracy and optimizes clinical decision-making alongside public health responsiveness. With the help of exact models, it is also possible to predict alterations in the resistance evolution timeline with new resistance genes and while clinically useful to determine right treatment options, they can aid clinical decisions. ai and ml mechanisms enable streamlined AMR surveillance, effectively guiding the direction for inline policy and containment control centers for antibiotic sustainability frameworks. ai, along with ml developments and emergence, is looked upon to take the lead in delays in AMR deployment. ml would intensively work in maintaining the effectiveness of antibiotics and redefining infection healthcare management.

关键词： Ethics Phenotypes Explainable ai Antibiotics Genomics Predictive models Data models Bioinformatics Public healthcare Immune system

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ai-Driven Provenance Tracking for Mitigating Fraud in E-Commerce Platforms Using Neural Networks 2

AI-Driven Provenance Tracking for Mitigating Fraud in E-Comm...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Reddy, Avala Raji Prabhakar, P. B. Edwin Al-Farouni, Mohammed Annapoorna, Errabelli Shukla, Aasheesh Kumar, Neelam Sanjeev Department of Mechanical Engineering CMR Technical Campus Telangana Hyderabad India New Prince Shri Bhavani College of Engineering and Technology Department of CSE Tamil Nadu Chennai600073 India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq GRIET Bachupally Department of AI&ML Telangana Hyderabad India GLA University Department of Electronics & Communication Engineering Mathura India SRM Institute of Science & Technology Vadapalani Department of Computer Science & Engineering Chennai26 India

ISBN: (纸本)9798350368109

This paper offers an overview of a novel ai-based provenance tracking system that can reduce fraud in e-commerce environments through its neural network. This system has been designed and its effectiveness has been validated on the four datasets - transaction records (500, 000 records), supply chain logs (300, 000 records), user behavioral patterns (1, 000, 000 records), and product images (100, 000 records). Preprocessing steps might include data cleaning, data normalization, and data transformation as we prepared and formatted the data for use in building machine learning models. CNNs were compared along with RNNs and models having a blend of CNN and RNN architectures. In this work, the hybrid model has been proved to give better performance with an accuracy of 96. % 2%, precision of 95. 8%, recall of 94. 88% on a precision level of 92%, a recall of 5%, and an F1 score of 95. This represents a 1% improvement over the previous results, and is indicative of the capabilities of the hybrid model to work with multiple data types for identification of fraudulent patterns. The system was implemented in a real-time e-commerce environment and had an average response time to the user of 150ms, the system was able to handle 200 TPS and the system up-time was 99. 8% It was able to identify 4500 fraudulent transactions out of 300 false and fraudulent positive transactions and 200 false and fraudulent negative transactions to a fraud prevention of 95 %. 5%. The research used 'MATLAB' for data pre-processing and model building, which allows for the development of more optimal and less resource-intensive models. The results prove that the system has a potential of adding a positive value in the ways of e-commerce security promotion and is a holistic approach to combating fraud. © 2024 IEEE.

关键词： Crime

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An Analysis on Virtual Mouse Control using Human Eye

An Analysis on Virtual Mouse Control using Human Eye

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Image Processing and Capsule Networks (ICIPCN), International Conference on

作者： P. M. Naidu N. Muthukumaran S. Chandralekha K. Tejaswini Reddy K.Sri Vaishnavi Department of Computer Science and Engineering (AI&ML) Vignan Institute of Technology and Science Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798350367171

ISBN: (纸本)9798350367188

Technological advances have enabled hands-free computer interaction, especially for people with limited hand function, such as amputees and paraplegics. However, current solutions face limitations in terms of input methods, accuracy, functionality, and accessibility. The proposed model aims to solve these problems by developing a reliable, user-friendly, and accurate system by utilizing facial expressions, especially eye and mouth movements. The proposed system aims to improve reliability and expressiveness by combining two modalities. Using advanced image processing technology, errors caused by lighting changes are minimized to provide a smooth user experience. The virtual mouse interface provides comprehensive features such as scrolling, clicking, and typing, improving the experience of users with different needs. By providing a reliable, versatile, and convenient hands-free interaction system, this study aims to give users greater independence, improve communication, and increase their participation in the digital world.

关键词： Accuracy Image processing computer network reliability Computational modeling Mouth Lighting User experience

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Advancing Marine Species Detection: A Comparative Analysis of YOLOv5 and YOLOv8 in Challenging Underwater Environment

Advancing Marine Species Detection: A Comparative Analysis o...

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Women in Computing (InCoWoCo), International Conference for

作者： R Bharath H U Ujwal K Chandan R C Ravindranath R Chandramma H R Rakshith Department of Computer Science and Engineering Global Academy of Technology Bengaluru Karnataka India Department of CSE(AI&ML) Global Academy of Technology Bengaluru Karnataka India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

This work offers a thorough comparison of the YOLOv5 and YOLOv8 object detection algorithms with a focus on the identification and categorization of ten different marine species. To address the urgent demand for sophisticated computer vision solutions in marine biology, we assess these models' performance, paying particular attention to their accuracy and effectiveness in difficult underwater settings. The chosen marine species are diverse in terms of size, shape, and color, providing a strong baseline against which to evaluate the models' performance. The investigation and results provide light on the advantages and disadvantages of the models by carefully examining detection metrics, false positives, and false negatives. The study offers helpful advice for scholars and practitioners working in marine biology and other related fields by providing concrete suggestions for model selection based on observed performance.

关键词： YOLO Measurement computer vision Shape Biological system modeling Computational modeling Color Fish Object recognition Dolphins

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Application of computer Software in Chemical Data Processing using Spider Monkey Optimization Algorithm 2

Application of Computer Software in Chemical Data Processing...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Rana Veer Samara Sihman Bharattej, R. Rupavath, Rana Veer Samara Sihman Brahmatej Hussein, Abbas Hameed Abdul Anjaneyulu, Madugula Upendra Roy, B.P. Saint Leo University Department of Computer Science St Leo United States Indiana Wesleyan University Marion United States Ahl Al Bayt University College of Pharmacy Karbala Iraq Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Department of Ai&ml Hyderabad India Channabasaveshwara Institute of Technology Gubbi Department of Electronics and Communication Engineering Tumkur India

ISBN: (纸本)9798350372892

The development of chemical-based products and functional materials is crucial for modern technologies, yet it often entails significant costs and time delays. Artificial intelligence and machine learning present novel approaches to leverage data for addressing these challenges;however, they encounter specific hurdles in the chemical domain. To address these issues, a software platform has been introduced to facilitate training and development of Recurrent Neural Networks (RNN). This platform incorporates the Spider Monkey Optimization (SMO) algorithm to optimize model's hyperparameters, including neurons number, rate of learning, and activation function. This software package goes beyond neural network training and development by encompassing design and operating-parameter optimization, essential aspects of chemical engineering processes. Within this framework, the weights that were optimized and bias obtained from neural network were stored and utilized in other SMO process for optimizing important process variables, like swiftness and temperature, according to user specifications. © 2024 IEEE.

关键词： Software packages

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Implementing IoT and Deep Learning-Based Educational Systems for Assessing Practical Learning with Cloud Computing Applications

Implementing IoT and Deep Learning-Based Educational Systems...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Hardeo Kumar Thakur Minal Bafna Jagendra Singh Usikela Naresh Susmi Biswas Saggurthi Kiran School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Physics Agrawal PG College Jaipur India Department of Computer Science and Engineering(AI&ML) CVR College of Engineering Rangareddy India Department of Management Science Haldia Institute of Management MAKAUT West Bengal India Department of CSE (AI&ML) CMR Technical Campus (Autonomous) Hyderabad India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

In this study, the practical application of technologies related to IoT and machine learning in the robotics education context is examined based on the use of a robot with two proximity sensors. The trial system was designed to leverage the real-time sensor data and monitor and optimize student interactions with the robot in the course of performing such tasks as object manipulation and palletization. The sensor data is used to critically track the accuracy of student actions and help mentors provide feedback on the issue. The machine learning models used in the study, which are Recurrent Neural Network (RNN), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM), are applied to analyze the sensor data and predict and classify the student's actions so that corrective measures can be taken in time. The RNN model has the highest accuracy and efficiency for the task, as it reaches the level of 96.7 % compared to 80% for KNN, 71% for SVM, and 75% for LSTM. This advantage is explained by the capability to model temporal dependencies or otherwise capture with its memory the sequences present in the sensor data. The data is transmitted securely and efficiently to the cloud platforms and back due to the implementation of the IoT, which also helps to deploy the model more effectively. Overall, it is concluded that the integration of IoT and machine learning is beneficial and transformative in educational contexts as it provides students with personalized real-time feedback. In this way, the level of proficiency of the students in dealing with robotic and IoT-based systems is significantly enhanced, preparing them to successfully address the future challenges of industry and academia in the sphere.

关键词： Support vector machines Recurrent neural networks Education Machine learning Nearest neighbor methods Robot sensing systems Data models Real-time systems Sensors Long short term memory

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Simplex Social Spider Optimization with Decision Tree Consumer Behavior Patterns

Simplex Social Spider Optimization with Decision Tree Consum...

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Integrated Intelligence and Communication Systems (ICIICS), International Conference on

作者： Mohammed I. Habelalmateen S. Sindhuja C. Rajan M. Hemamalini R. Navin Kumar Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering (AI&ML) Sri Eshwar College of Engineering Coimbatore India Department of Computer Science and Engineering (AI&ML) KSR College of Technology Tiruchengode India Department of Tamil New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Computer Applications Nandha Engineering College (Autonomous) Erode India

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

The population growth and the economic development are growing rapidly as a result of this makes more understanding the population behavior towards buying things. Understanding consumer behavior plays a major role for businesses in order to enhance the product quality and to provide a satisfactory product for consumers. The consumer behavior is considered in various factors based on age, their income, and their previous purchase history b considering all this factors a chart known as Customer Relationship Management (CRM) is made in order to maintain all the details of the customers of that particular company. In this study we proposed Simplex Social Spider Optimization (SSSO)-Decision Tree of the consumer behavior. This technique uses each customer information as a spider web and splits the data based on the feature values. The results indicated that specific features, such as purchase frequency, average transaction value, and product preferences, significantly influenced customer classification outcomes. A decision tree is used for the classification of the consumer behavior. This enhances the predicting and classifying the customer's attitude while purchasing the products. This research highlights the potential of predictive analytics in tailoring marketing strategies and improving CRM. The proposed method have achieved the accuracy of 92.42 % The findings suggest that businesses can leverage these insights to optimize targeted promotions and enhance customer experience, ultimately driving sales and fostering brand loyalty.

关键词： Consumer behavior Accuracy Customer relationship management Product design Quality assessment Decision trees Optimization Standards Random forests Business

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