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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Raj Kumar, V.S. Kumaresan, T. Mythily, V. Raghavendran, N. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data science Sathyamangalam India Bannari Amman Institute of Technology Department of Computer Science and Engineering Sathyamangalam India Nandha Engineering College Department of Computer Science and Engineering Erode India Rmk college of Engineering and Technology Department of Artificial Intelligence and Data science Chennai India

ISBN: (纸本)9798350368413

Twitter plays an important role in understanding the consumer sentiment about the products. The advanced analytics and Natural Language Processing (NLP) are used to extract actionable insights from this data and useful for a business to improve features and address issues on time for better customer satisfaction and brand loyalty. Most of the current models suffer from a lack of effective ways to handle subtle sentiment analysis because of a lack of contextual understanding and proper feature extraction. Most traditional models cannot combine and optimize features that lead to obtaining sentiments. This leads to overfitting and problems caused by local minima. The proposed Emotion-Enhanced BERT (EE-BERT) model is an extension of the standard BERT to learn contextual embeddings in user sentiment. In this work, data preprocessing includes text cleaning, tokenization, dealing with emojis and emoticons, and lemmatization with the best quality data. The proposed work is basically a dual-stream architecture extended with butterfly optimizer. First, the advanced tokenization and semantic understanding techniques is used for extracting textual features. Second, the emotion-enhanced features extracted through emotion-hash vectorization to enhance the model's contextual awareness in social media data. This provides a unified framework that combines the feature extraction methods to enhance the accuracy in sentiment analysis. It demonstrated with an impressive accuracy of 98.2% on sentiment in posts and comments with 0.15 losses. The proposed work is free from overfitting and local minima in product quality enhancement. © 2024 IEEE.

关键词： Tweets

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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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Predicting Traffic Flow Using Dynamic Spatial-Temporal Graph Convolution Networks

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computers, Materials & Continua 2024年第3期78卷 4343-4361页

作者： Yunchang Liu Fei Wan Chengwu Liang School of Computer and Data Science Henan University of Urban ConstructionPingdingshan467036China School of Electrical and Control Engineering Henan University of Urban ConstructionPingdingshan467036China

Traffic flow prediction plays a key role in the construction of intelligent transportation ***,due to its complex spatio-temporal dependence and its uncertainty,the research becomes very *** of the existing studies are based on graph neural networks that model traffic flow graphs and try to use fixed graph structure to deal with the relationship between ***,due to the time-varying spatial correlation of the traffic network,there is no fixed node relationship,and these methods cannot effectively integrate the temporal and spatial *** paper proposes a novel temporal-spatial dynamic graph convolutional network(TSADGCN).The dynamic time warping algorithm(DTW)is introduced to calculate the similarity of traffic flow sequence among network nodes in the time dimension,and the spatiotemporal graph of traffic flow is constructed to capture the spatiotemporal characteristics and dependencies of traffic *** combining graph attention network and time attention network,a spatiotemporal convolution block is constructed to capture spatiotemporal characteristics of traffic *** on open data sets PEMSD4 and PEMSD8 show that TSADGCN has higher prediction accuracy than well-known traffic flow prediction algorithms.

关键词： Intelligent transportation graph convolutional network traffic flow DTW algorithm attention mechanism

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On convergence of covariance matrix of empirical Bayes hyper-parameter estimator

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Control Theory and Technology 2024年第2期22卷 149-162页

作者： Yue Ju Biqiang Mu Tianshi Chen School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong KongShenzhenShenzhen518172GuangdongChina Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technology10044StockholmSweden Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and System ScienceChinese Academy of SciencesBeijing100190China

Regularized system identification has become the research frontier of system identification in the past *** related core subject is to study the convergence properties of various hyper-parameter estimators as the sample size goes to *** this paper,we consider one commonly used hyper-parameter estimator,the empirical Bayes(EB).Its convergence in distribution has been studied,and the explicit expression of the covariance matrix of its limiting distribution has been ***,what we are truly interested in are factors contained in the covariance matrix of the EB hyper-parameter estimator,and then,the convergence of its covariance matrix to that of its limiting distribution is *** general,the convergence in distribution of a sequence of random variables does not necessarily guarantee the convergence of its covariance ***,the derivation of such convergence is a necessary complement to our theoretical analysis about factors that influence the convergence properties of the EB hyper-parameter *** this paper,we consider the regularized finite impulse response(FIR)model estimation with deterministic inputs,and show that the covariance matrix of the EB hyper-parameter estimator converges to that of its limiting ***,we run numerical simulations to demonstrate the efficacy of ourtheoretical results.

关键词： Regularized system identification Hyper-parameter estimator Empirical Bayes Convergence of covariance matrix

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Dynamic Resource Allocation in Edge Computing via Deep Deterministic Policy Gradient Reinforcement Learning 3

Dynamic Resource Allocation in Edge Computing via Deep Deter...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Gopal, Vidhya Surendar, U. Bajulunisha, A. Tamilselvi, M. Sathiyapriya, V. Saranya, N. Sona College of Technology Department of Computer Science and Engineering Salem India Srm institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Kundrathur India Roever Engineering College Department of Computer Science and Engineering Perambalur India Knowledge Institute of Technology Department of Computer Science and Engineering Salem India Karpagam College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798331508456

The difficulty with dynamic and heterogeneous natured edge computing environments is resource provisioning. Reinforcement Learning (RL) can be promising to solve the problems of resource allocation under conditions of dynamics and complexity. This research work presents a novel DDPG-based (Deep Deterministic Policy Gradient) approach for optimizing the dynamics of resource allocation in edge computing environments by exploiting the CRAWDAD KAIST-WIBRO dataset for the proposed model. This approach implements a neural network that learns the optimal policies of allocations with dynamism towards new changes in workloads and resource availabilities. In order to test the performance of this method, conducted several simulations over real-world workload and resource data, mapping the parameters like rewards and variation in episodes. The simulation results indicate that proposed approach performs superior compared to traditional heuristic-based approaches as well as other RL algorithms for resource usage, response time, and energy efficiency. This proposed approach permits optimized resource utilization along with quality of service, of paramount importance in executing complex and demanding applications at the edge computing environment. © 2025 IEEE.

关键词： Reinforcement learning

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A comparative analysis for deep-learning-based approaches for image forgery detection

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第1期8卷 1-10页

作者： Ch, Ravikumar Radha, Marepalli Mahendar, Maragoni Manasa, Pinnapureddy Department of Artificial Intelligence &Data Science Chaitanya Bharathi Institute of Technology Hyderabad 500075 India Department of Computer Science and Engineering CVR College of Engineering Hyderabad 501510 India Department of Computer Science and Engineering Neil Gogte Institute of Technology Hyderabad 500039 India

The detection of counterfeit photographs is critical in the digital age because of the widespread development of digital media and its significant impact on social networks. The legitimacy of digital content is being threatened by the growing sophistication of picture counterfeiting. With the help of pre-trained VGG-16 models and deep learning techniques that integrate Error Level Analysis (ELA) and Convolutional Neural Networks (CNNs), this study presents a fresh solution to this problem. The study thoroughly assesses and contrasts these models with a dataset that has been carefully chosen to bring the presented findings into perspective. To ensure a reliable evaluation of each model's performance 5000 experiments were carried out in total. With an accuracy rate of 99.87% and an accurate identification rate of 99% of hidden forgeries, the results demonstrate the exceptional effectiveness of the ELA-CNN model. However, despite its robustness, the VGG-16 model only achieves a significantly lower accuracy rate of 97.93% and a validation rate of 75.87%. This study clarifies the relevance of deep learning in the identification of image forgeries and highlights the practical ramifications of various models. Moreover, the research recognizes its constraints, especially for highly advanced counterfeits, and proposes possible paths for enhancing the accuracy and scope of detection algorithms. In the ever-changing world of digital media, the thorough comparative analysis provided in this study offers insightful information that can direct the creation of accurate forgery detection tools, protecting digital content integrity and reducing the effects of image manipulation. © (2023), (Society of Sytematic Innovation). All Rights Reserved.

关键词： Counterfeit images deep learning ELA-CNN Image forgery detection VGG-16 model

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Offline Signature Forgery Detection Based on Geometric Measures Using Tensorflow Model 2

Offline Signature Forgery Detection Based on Geometric Measu...

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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Lakshmi, A. Anagha Reddy, G. Siddarth Reddy, M. Sowmya Kathirisetty, Nikhila Vardhaman College of Engineering Department of Information Technology Hyderabad India Vardhaman College of Engineering Department of Computer Science and Data Science Hyderabad India

ISBN: (纸本)9798350370188

The most popular method for identifying people from past signatures is through signatures. By using a TensorFlow model which is a deep learning algorithm, we created a new system to verify signatures on bank checks and other important documents. The main point is that a signature plays a crucial role in banking because it is necessary for money withdrawals. In banks there are no efficient systems to check if a signature is real or fake as it is the most frequently used bio-metric method to verify a person's identity. This can lead to bank fraud. The Project will make it easier to tell whether a signature is genuine or not. Online and offline verification are the two methods of verification. We're going to use various geometric measurements to accomplish offline verification. Python libraries were employed in this case. In the testing phase the model's performance is evaluated. To protect the integrity and legitimacy of handwritten signatures, signature forgery detection is an essential responsi-bility. The capacity to recognize forged signatures with accuracy has grown more important as a result of the rise in digital transactions and the growing reliance on electronic documents. The act of duplicating or copying another person's signature with the purpose to deceive or gain illegal access is known as signature forging. Manually spotting these forgeries can be difficult because expert forgers are adept at closely imitating the visual style and traits of real signatures. Therefore, there has been a lot of interest in the development of automated methods and algorithms to identify fake signatures. © 2024 IEEE.

关键词： data mining

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AI-Driven Indoor Farming: An Algorithmic Approach to Sustainable Agriculture 1st

AI-Driven Indoor Farming: An Algorithmic Approach to Sustain...

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1st International Conference on Intelligent Computing, Smart Communication, and Network Technologies, ICICSCNT 2023

作者： Veeramalai, S. Saranya, K. Computer Science and Engineering Chennai İnstitute of Technology Chennai India Artificial Intelligence and Data Science Panimalar Engineering College Poonamallee Chennai India

ISBN: (纸本)9783031759567

Indoor farming has emerged as a promising alternative to traditional farming methods, offering a controlled and optimized environment for crop growth that can improve yield and reduce the environmental impact of agriculture. However, indoor farming can be resource-intensive and labor-intensive, and optimizing the growing conditions for each crop can be challenging. In this project, we propose an AI-based autonomous indoor farming system that uses advanced algorithms to manage the environmental conditions, monitor crop growth and health, and optimize crop yield. The system will use machine learning to analyze various data points such as temperature, humidity, light intensity, and nutrient levels to create an optimal growing environment for each crop. Additionally, computer vision will be used to monitor plant health and growth patterns, and adjust the environmental conditions accordingly. Our system will also provide a user interface for farmers to input their crop preferences, monitor crop growth and health, and receive real-time feedback and coaching from the algorithm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Efficient Image Captioning Based on Vision Transformer Models

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computers, Materials & Continua 2022年第10期73卷 1483-1500页

作者： Samar Elbedwehy T.Medhat Taher Hamza Mohammed F.Alrahmawy Department of Data Science Faculty of Artificial IntelligenceKafrelsheikh UniversityEgypt Department of Electrical Engineering Faculty of EngineeringKafrelsheikh UniversityEgypt Department of Computer Science Faculty of Computer and Information ScienceMansouraEgypt

Image captioning is an emerging field in machine *** refers to the ability to automatically generate a syntactically and semantically meaningful sentence that describes the content of an *** captioning requires a complex machine learning process as it involves two sub models:a vision sub-model for extracting object features and a language sub-model that use the extracted features to generate meaningful ***-based vision transformers models have a great impact in vision field *** this paper,we studied the effect of using the vision transformers on the image captioning process by evaluating the use of four different vision transformer models for the vision sub-models of the image captioning The first vision transformers used is DINO(self-distillation with no labels).The second is PVT(Pyramid Vision Transformer)which is a vision transformer that is not using convolutional *** third is XCIT(cross-Covariance Image Transformer)which changes the operation in self-attention by focusing on feature dimension instead of token *** last one is SWIN(Shifted windows),it is a vision transformer which,unlike the other transformers,uses shifted-window in splitting the *** a deeper evaluation,the four mentioned vision transformers have been tested with their different versions and different configuration,we evaluate the use of DINO model with five different backbones,PVT with two versions:PVT_v1and PVT_v2,one model of XCIT,SWIN *** results show the high effectiveness of using SWIN-transformer within the proposed image captioning model with regard to the other models.

关键词： Image captioning sequence-to-sequence self-distillation transformer convolutional layer

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Blockchain-Enabled Private and Secure Task Allocation Framework 16

Blockchain-Enabled Private and Secure Task Allocation Framew...

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16th International Conference on COMmunication Systems and NETworkS, COMSNETS 2024

作者： Orang, Ayda Valinezhad Dorri, Ali Gaspers, Serge Ruj, Sushmita School of Computer Science and Engineering Unsw Sydney Australia Data61 Csiro Australia School of Computer Science Queensland University of Technology Australia

ISBN: (纸本)9798350383119

In the realm of wireless communications and task allocations, where agents need to travel to perform tasks, a new challenge has emerged. This challenge involves efficiently distributing tasks among agents while considering their preferences and priorities. Unfortunately, many existing solutions rely on centralized systems, making them susceptible to various vulnerabilities, including privacy concerns and security threats. To address these issues, this paper introduces a novel solution named SEPP-TAP-a secure and privacy-preserving decentralized framework for multi-agent task allocation problems, harnessing the power of blockchain technology. SEPP-TAP not only protects agents' privacy and location information but also strengthens network security by mitigating threats like the Sybil attack and network flooding. Our approach employs innovative anonymization techniques to ensure that agents' identities remain concealed throughout successive task requests, breaking any associations between tasks performed by a particular agent. © 2024 IEEE.

关键词： blockchain privacy security smart contract task allocation

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