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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Murthy, Anantha Prathwini Kulkarni, Sanjeev Savitha, G. Nitte Karkala Institute of Computer Science and Information Science Srinivas University Department of Master of Computer Applications India Department of Master of Computer Applications Karkala India Srinivas University Institute of Engineering and Technology Department of Computer Science and Engineering Mangalore India Manipal Institute of Technology Manipal Academy of higher Education Manipal Department of Data Science and Computer Applications India

ISBN: (纸本)9798350350593

Yakshagana, a traditional theater form from Karnataka, India, features a unique combination of vibrant costumes, dynamic dance movements, and elaborate facial makeup, making character and actor identification a challenging task for automated systems. This research work presents a novel approach to classify Yakshagana characters into two primary categories, Vaishnava and Shaiva, and to identify the actors performing these characters using advanced machine learning techniques. Our research employs a Cyclic Gated Recurrent Neural Network (Cyclic GRNN) for classification and identification tasks. For character category classification, we integrate YOLOv3 and Faster R-CNN models, achieving an accuracy of 92.85% with YOLOv3 and 88.285% with Faster R-CNN. The categorization is specifically focused on distinguishing between Vaishnava and Shaiva characters. Additionally, for actor name identification, we utilize a RESNET-50 model, attaining a high accuracy of 95.60%. The results demonstrate the efficacy of Cyclic GRNN combined with state-of-the-art object detection and image classification models in accurately recognizing and categorizing Yakshagana characters and actors. This research contributes to the preservation and digital documentation of Yakshagana by providing robust tools for automated identification, thereby facilitating cultural heritage studies and enhancing audience engagement with this traditional art form. © 2024 IEEE.

关键词： Recurrent neural networks

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Decentralized Generative AI Model Deployment Using Microservices

Decentralized Generative AI Model Deployment Using Microserv...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Jhingran, Sushant Bansal, Nidhi Chaturvedi, Rekha Singh, Ajeet Arora, Yojna School of Engineering and Technology Sharda University Department of Computer Science & Engineering Uttar Pradesh Greater Noida India Pune India School of Information Security and Data Science Manipal University Jaipur Department of Data Science and Engineering Jaipur India Moradabad Institute of Technology Department of Computer Science & Engineering Uttar Pradesh Moradabad India

ISBN: (纸本)9798331517953

The microservices are continuously growing in domains of communication using cloud and AI. Access to microservice can be done using any respective cloud environment. Access microservices using cloud require multiple configurations. These configurations can be set up in Kubernetes or containerized environments to lightweight any application which increases the performance of microservices. Multiple requests can create a concurrent call, which causes slow response. Generative AI models can be used to solve concurrent calls. Generative AI used a modular approach for microservice communications with API gateway. This allows microservices to function in a variety of contexts, including cloud, edge devices, and containers. This process refers to a decentralized approach which can only be implemented in the case of microservices and provide improvement in latency along with bandwidth. © 2024 IEEE.

关键词： Cloud Container Docker Generative AI Microservices

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Chaotic Resilience: Enhancing IoT Security Through Dynamic data Encryption 8th

Chaotic Resilience: Enhancing IoT Security Through Dynamic D...

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8th International Symposium on Intelligent Informatics, ISI 2023

作者： Francis, E. Geo Sheeja, S. Depertment of Computer Science Karpagam Academy of Higher Education Coimbatore India Department of Data Science Sri Krishna Adithya College of Arts and Science Coimbatore India

ISBN: (纸本)9789819721467

In the ever-expanding landscape of the Internet of Things (IoT), safeguarding the security and privacy of data transmissions among IoT devices has become increasingly critical. This research introduces an innovative approach known as "Chaotic Resilience" to bolster the security of data exchanged between IoT devices. While traditional encryption methods are effective, they often rely on fixed cryptographic keys, making them susceptible to attacks over time. Chaotic Resilience introduces an element of dynamic unpredictability into the encryption process, making it considerably more resistant to brute-force attacks and key extraction techniques. The proposed technique involves the integration of chaotic systems, such as the 4D chaotic map, into the encryption process. These chaotic systems generate an ongoing sequence of unpredictable values, which are then combined with the plaintext data through a dynamic encryption algorithm. The result is ciphertext that becomes highly sensitive to initial conditions and practically immune to standard cryptographic attacks. Moreover, the dynamic nature of the encryption process ensures that even if an attacker gains access to the encryption algorithm, they cannot predict future encryption keys. Through extensive simulations and experiments, we demonstrate the effectiveness of Chaotic Resilience in thwarting various attack scenarios, including brute-force attacks, chosen-plaintext attacks, and known-plaintext attacks. Additionally, Chaotic Resilience demonstrates a 20% increase in encryption speed and a 15% reduction in resource utilization, establishing it as an efficient and robust IoT security solution. Our results underscore that Chaotic Resilience significantly enhances the security and resilience of IoT data transmissions, offering a promising avenue for strengthening IoT security in an increasingly interconnected world. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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Supply Chain Management Using Internet of Things and Blockchain for Smart Meat Packaging 5

Supply Chain Management Using Internet of Things and Blockch...

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5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Kour, Kanwal Preet Singh, S. Kanwal Deep Bali, Malvinder Singh Ghantasala, G. S. Pradeep Rajesh Sharma, R. Ananthanagu, U. Manipal University School of Computer Science and Engineering Jaipur India Federation University Department of Computer Science Australia Manipal University Department of Data Science and Engineering Rajasthan Jaipur India Alliance College of Engineering and Design Alliance University Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350389609

In recent times, blockchain has evolved the security of traditional supply chain systems. Different issues of supply chain management like flexibility and reliability can be easily addressed using blockchain. Meat Packing Industry involves the complete process of processing, packaging and supplying the meat and its products throughout the world for human consumption. The animals used for the meat supply chain include pigs, sheep, cattle etc. Due to the enormous increase in the number of meat-eaters worldwide, its packing and timely supply are of utmost importance. The meat packing industry has revolutionized a lot by using the latest supply chain methods based on new technologies like blockchain. These include adulteration, long-term delays leading to spoilage of the product, mixing of other similar products which are lower in price etc. All these issues can be easily handled by using blockchain and IoT for meat supply chain management. Using these technologies, the challenges ranging from timely transportation of good quality meat to the best quality meat delivered to the customer can be easily addressed. In this paper, an effective blockchain and IoT-based model has been proposed which will counter the above challenges and improve the reliability and security factor. © 2024 IEEE.

关键词： Supply chain management

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Classification of hyperspectral images using fusion of CNN and MiniGCN with SVM

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International Journal of Digital Earth 2023年第1期16卷 3601-3617页

作者： Wenbing Wu Tariq Sadad Mejdl Safran Sultan Alfarhood Xiaojian Yuan Big Data College Fuzhou University of International Studies and TradeFuZhouPeople’s Republic of China Department of Computer Science University of Engineering&TechnologyMardanPakistan Department of Computer Science College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia

Convolutional neural networks(CNNs)have gained popularity for categorizing hyperspectral(HS)images due to their ability to capture representations of spatial-spectral ***,their ability to model relationships between data is *** convolutional networks(GCNs)have been introduced as an alternative,as they are effective in representing and analyzing irregular data beyond grid *** have *** computationally intensive,minibatch GCNs(miniGCNs)enable minibatch training of large-scale *** have improved the classification performance by using miniGCNs to infer out-of-sample data without retraining the *** addition,fuzing the capabilities of CNNs and GCNs,through concatenative fusion has been shown to improve performance compared to using CNNs or GCNs ***,support vector machine(SvM)is employed instead of softmax in the classification *** techniques were tested on two HS datasets and achieved an average accuracy of 92.80 using Indian Pines dataset,demonstrating the effectiveness of miniGCNs and fusion strategies.

关键词： CNN hyperspectral images MiniGCN SVM

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AI Based Criminal Detection and Recognition System for Public Safety and Security using novel CriminalNet-228 4th

AI Based Criminal Detection and Recognition System for Publi...

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4th International Conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Murthy, Jamuna S. Siddesh, G.M. Department of Computer Science and Engineering Ramiah Institute of Technology Bengaluru India Department of Artificial Intelligence and Data Science Ramiah Institute of Technology Bengaluru India

ISBN: (纸本)9789819726103

The recent surge in public space criminal activities underscores the need for an efficient system to promptly detect, recognize, and track criminals. Existing AI-based criminal detection literature, while insightful, has limitations such as the complexity to analyze video data, time, and speed of accuracy when it comes to training the algorithms that call for further advancements. Hence proposed AI-driven system addresses this demand, aiming to automate criminal identification, equipping law enforcement with a potent tool for proactive crime prevention and resolution. The system utilizes the innovative "CriminalNet-228" Convolutional Neural Network (CNN) architecture, meticulously trained on a vast criminal image dataset for enhanced detection accuracy. To bolster face detection, additional computational resources like parallel processing and distributed computing are employed, enabling real-time analysis of extensive CCTV footage. Notably, the system tracks identified criminals, providing law enforcement with real-time situational awareness. CriminalNet-228 achieves an impressive overall Mean Average Precision (mAP) of 0.65 and excels in detecting facial features as small as 4 × 4 pixels, demonstrating its detail-oriented recognition capabilities. When evaluating Proposed CriminalNet-228 in comparison to established state-of-the-art techniques like Fast-RCNN, Yolov7, and AlexNet, it surpassed them in terms of precision, recall, f-measure, and accuracy, achieving an impressive accuracy rate of 99.2%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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EmoCNN: Unleashing Human Emotions with Customized CNN Using Different Optimizers 2

EmoCNN: Unleashing Human Emotions with Customized CNN Using ...

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2nd International Conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Sahana, M. Umesh, Praneetha Kodipalli, Ashwini Rao, Trupthi Department of Computer Science & Engineering Global Academy of Technology Bangalore India Department of Artificial Intelligence & Data Science Global Academy of Technology Bangalore India

A key development towards enhancing computer-human interaction is emotion recognition. This publication describes a technique called EmoCNN, which uses deep learning techniques to precisely identify and classify human emotions, emphasizing improving model performance using different optimizers. Our research intends to contribute to the creation of more effective systems that improve computer-human interaction by solving the problems associated with emotion recognition. By bridging the gap between humans and robots, accurate emotion detection enables systems to perceive emotions for customized and responsive interactions. AI-powered assistants, chatbots, and social robots all benefit from emotion recognition by providing more responsive, empathic and interesting user experiences. Emotion-aware technologies can also enhance user feedback analysis, human-centered design, and monitoring of mental health. Using a human emotion detection dataset, we carried out comprehensive experiments focusing on the happy, sad, and neutral emotion classes. Constructing a customized EmoCNN model with convolutional layers, a hidden layer, ReLU activation, and max-pooling was the focus of our computational work. We investigated various optimizers and evaluated how they affected accuracy, convergence speed and loss minimization. The results demonstrated that the EmoCNN model, which had been trained using the Adam optimizer, gave the best accuracy in distinguishing between emotions. Our paper provides a comparative analysis, highlighting the superiority of EmoCNN over existing models, showcasing its ability to achieve higher validation accuracy (89%) and more efficient emotion recognition when compared to previous approaches with minimal loss. Our research advances the field of emotional computing by demonstrating how well EmoCNN can identify and categorizes various human emotions. This discovery has significant ramifications for the creation of emotion-aware computers, which can better und

关键词： Emotion Recognition

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Diabetes Prediction using Hybrid Machine Learning Techniques

Diabetes Prediction using Hybrid Machine Learning Techniques

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Naik, Bhukya Devendar Spoorthy, Anumula Baherji, Sakshi Rout, Saroja Kumar Sethi, Bijaya Kumar Vardhaman College of Engineering Department of Information Technology Hyderabad India Vardhaman College of Engineering Department of Computer Science & Engineering Data Science Hyderabad India

ISBN: (纸本)9798350373783

The rise in blood glucose levels is the primary factor contributing to the development of diabetes. Given the significance of preventing diabetes or delaying its onset, despite numerous efforts utilizing machine learning for medical diagnostics, there remains a notable gap in research concerning long-term disease prediction, especially for type 2 diabetes. However, the traditional method of diagnosing diabetes involves patients undergoing blood glucose tests administered by doctors, which can be limited by clinical resources. Many patients consequently encounter delays in getting a diagnosis. To create a predictive model for diabetes diagnosis, this study used six traditional machine learning models: boosting, neural networks, decision trees, random forests, logistic regression, and support vector machines. The study employed machine learning (ML) algorithms to predict diabetes using an authentic dataset from Safety Pressure Primary Health Care. With a validation accuracy of 84%, the study offers important new information on who is most likely to develop type 2 diabetes. By precisely predicting the type of diabetes and examining the importance of each indication in the prediction process, the goal of this study is to improve the accuracy of diabetes prediction. © 2024 IEEE.

关键词： Prediction models

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A Real-Time computer Vision-Based Green Intensity Estimation Method for the Arecanut Tree Pesticide Spraying System

A Real-Time Computer Vision-Based Green Intensity Estimation...

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International Conference on VLSI, Signal Processing, Power Electronics, IoT, Communication and Embedded Systems, VSPICE 2023

作者： Nagaraja Hebbar, N. Sandeep, Bhat Artificial Intelligence and Data Science Department Srinivas Institute of Technology Mangaluru India Computer Science and Engineering Department Srinivas Institute of Technology Mangaluru India

ISBN: (纸本)9789819746569

In India, one of the commercial crops is arecanut. The majority of arecanut growers depend on the arecanut production. However, they are also having a great deal of difficulty finding skilled workers to do pesticide spraying jobs. This effort uses computer vision technologies to address the aforementioned problem. This work compares the HSV estimation with the green pixel estimate approach. This makes it easier to position the robot at the right height, where the pesticide spray will work best. In this case, the HSV estimation identified a better way to manage the robot’s motion. Using a wireless camera, the experimental setup achieves an incredibly innovative performance to capture an image of the arecanut crop. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Pixels

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Job Resumes Recommendation using Integration of Fuzzy Discernibility Matrix Feature Selection and Convolutional Neural Network Multi-label Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第13期49卷 49-58页

作者： Hayami, Regiolina Yusoff, Nooraini Daud, Kauthar Mohd Fatma, Yulia Department of Informatics Engineering Faculty Computer Science Universitas Muhammadiyah Riau Indonesia Faculty of Data Science and Computing Universiti Malaysia Kelantan 16100 Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Malaysia

The manual analysis of job resumes poses specific challenges, including the time-intensive process and the high likelihood of human error, emphasizing the need for automation in content-based recommendations. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNN) for Multi-label Text Classification (MLTC), offer a promising solution for addressing these challenges through artificial intelligence. While CNN is renowned for its robust feature extraction capabilities, it faces specific challenges such as managing high-dimensional data and poor data interpretability. To address these limitations, this study employs the Fuzzy Discernibility Matrix (FDM) feature selection technique to determine the relevance of skills for each job vacancy. FDM assigns weights to the features of each job category and ranks their relevance based on the highest scores, which are then utilized in the MLTC CNN model. The integration of FDM and MLTC CNN serves as the foundation for generating content-based recommendations derived from key features in job resumes. This study produces a content-based job recommendation system that displays the top three job categories, accompanied by explanations of the skills supporting each selected category. These recommendations also consider cosine similarity values between analyzed items and the integrated results of FDM and MLTC CNN. The application of FDM for feature weighting has been proven to enhance multi-label text classification outcomes by providing better insights during the feature selection process. With a recall of 97.26%, precision of 94.81%, and accuracy of 98.58%, the MLTC model integrating FDM feature selection and CNN demonstrates robust performance characteristics in content-based job recommendation tasks. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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