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5th International Conference on computer and Communication Technologies, IC3T 2023

作者： Mishra, Vishnu Kumar Mishra, Megha Dewangan, Bhupesh Kumar Parijatha, Kimidi Choudhury, Tanupriya Kotecha, Ketan Shri Shankaracharya Technical Campus Chhattisgarh Bhilai India Department of Computer Science and Engineering OP Jindal University Raigarh India Department of Computer Science and Engineering Malla Reddy College of Engineering Telangana Secunderabad India CSE Department Symbiosis Institute of Technology Symbiosis International University Lavale Campus Maharashtra Pune412115 India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India

ISBN: (纸本)9789819997060

PBL is an educational approach that centers on learners actively engaging with open-ended problems rather than just absorbing predetermined content. This method encourages in-depth understanding and the development of problem-solving skills. Notably, the Markov PBL approach doesn’t focus on providing answers but instead emphasizes the cultivation of valuable skills and attributes. These include attention to detail, enhanced coordination, and the strengthening of group dynamics and communication. The field of pedagogy has experienced a significant impact worldwide due to the pandemic. This global crisis led to the temporary closure of schools, colleges, and universities. In response, many educational institutions swiftly adopted online learning as the primary mode of instruction. Online learning encompasses various courses delivered entirely via digital platforms, with the exception of Massive Open Online Courses (MOOCs). Compared to traditional brick-and-mortar classroom settings, online learning via virtual classes conducted over the internet offers a different set of advantages. It provides learners with opportunities to enhance their skills through practical application and critical assessment. Additionally, it facilitates the retrieval of consolidated knowledge and encourages continuous learning within a collaborative team environment. This study aims to explore both the advantages and disadvantages of applying the Markov PBL method within the context of online learning. While the benefits are apparent, there are also challenges and limitations associated with this approach, which we will delve into further. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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Exploring the Impact of Carbon Emissions on Supply Chains of Imperfect Quality Items: A Game Theory Perspective 2nd

Exploring the Impact of Carbon Emissions on Supply Chains of...

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2nd Biennial International Symposium on Fluids and Thermal Engineering, FLUTE 2023

作者： Mittal, Mandeep Dhanda, Anil Raghav, Tisha Phanden, Rakesh Kumar Department of Mathematics School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Mathematics Amity Institute of Applied Sciences Amity University Uttar Pradesh Noida India Amity School of Engineering and Technology Amity University Uttar Pradesh Noida India

ISBN: (纸本)9789819731725

This research study proposes a cooperative and non-cooperative game theory strategy. The study’s objectives are to examine how carbon emissions impact the supply chain, especially in means of quality control and cost restriction, and to evaluate the effectiveness of game theory in addressing the issue. To gather and evaluate both quantitative and qualitative data for the study, a mixed-methods technique is used. The importance of reducing carbon emissions in supply chains is emphasized in the study, as are the benefits of using cooperative and non-cooperative game theory strategies to achieve this goal. The paper improves knowledge on supply chain management by offering an approach for simulating carbon emissions in a network of supply chains. The results of this study benefit supply chain management and assist academics, policymakers, and sustainability practitioners. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Game theory

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Resource Provisioning in Cloud Computing using Fuzzy Logic Control System: An adaptive approach 2

Resource Provisioning in Cloud Computing using Fuzzy Logic C...

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2nd International Conference on Business Analytics for technology and Security, ICBATS 2023

作者： Alnawayseh, Saif. E. A. Muhammad, Muhammad Hassan Ghulam Hassan, Zahid Fatima, Mayraj Aslam, Muhammad Shoukat Ibrahim, Amer Ateeq, Karamath Mutah Univesity Faculty of Engineering Electrical Engineering Department Jordan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan PMAS-Arid Agriculture University Rawalpindi Pakistan American University in Th Emirates College of Computer and Information Technology Dubai United Arab Emirates Skyline University BSIT Faculty Sharjah United Arab Emirates

ISBN: (纸本)9798350335644

The need for cloud computing has been increasing widely in this era since last two decades due to its continual benefits include security, disaster recovery, increased collaboration, cost saving and virtualization etc. This innovation introduces an enormous transformation of conventional applications in business industry. However, cloud users indulge themselves not acquiring a sufficient amount of computing resources caused by resource confliction. A challenging and significant problem in cloud computing is Resource Provisioning in virtualization environment that relates with selecting, deploying and run time management of software and hardware resources. On the contrary, such situations also exist where resources are found in virtual servers either under or over-utilization. In order to deal with these problems, proposed framework in this paper is demonstrated using Fuzzy Logic Control System towards characterization the processes' priorities by means of employing input parameters (workload, processing time, and process age) keeping in view of the fact that expert judgment is required for taking into account these parameters. © 2023 IEEE.

关键词： Virtualization

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An Introduction to Gossip Protocol Based Learning in Peer-to-Peer Federated Learning 3

An Introduction to Gossip Protocol Based Learning in Peer-to...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Naik, Dishita Grace, Paul Naik, Nitin Jenkins, Paul Mishra, Durgesh Prajapat, Shaligram Birmingham City University United Kingdom School of Computer Science and Digital Technologies Aston University United Kingdom Cardiff School of Technologies Cardiff Metropolitan University United Kingdom School of Computer Science & Information Technology Symbiosis University of Applied Sciences India International Institute of Professional Studies Devi Ahilya University India

ISBN: (纸本)9798350343274

Machine learning (ML) has been progressively implemented in a distributed manner to harness the data abundance produced on billions of end user devices. Federated learning (FL) is a type of distributed machine learning that provides robust data privacy by training ML models locally on each participating node without directly exchanging raw data with others. The local ML model updates from all nodes are aggregated in order to obtain a global model. There are different ways to aggregate local model updates for obtaining a global model, such as centralized and decentralized/peer-to-peer. Centralized FL (CFL) requires a server to collect and aggregate all model updates for producing a global model, whereas decentralized FL (DFL)/peer-to-peer FL (P2PFL) requires coordination among all the nodes to communicate and aggregate all models updates and produce a global model. In CFL, the server can pose a bottleneck problem as it is a single point of failure, though, this issue can be resolved using DFL/P2PFL. However, the design of the decentralized/peer-to-peer architecture is complex and challenging, and may incur significant communication overhead due to a large number of nodes involved in the learning process. Gossip protocols are one of the most effective ways to communicate in DFL/P2PFL and optimises its performance, therefore, this decentralized learning is also known as gossip protocol-based learning. Considering the importance of gossip protocol in DFL/P2PFL, this paper will analyse gossip protocol, working of gossip protocol, types of gossip protocol, benefits and limitations of gossip protocol, usages of gossip protocol in DFL/P2PFL and its two main types structured P2PFL and unstructured P2PFL. © 2023 IEEE.

关键词： Aggregates

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Next-Generation Advanced Security systems for Communities Using Integrated IoT and Blockchain Over Cloud Computing

Next-Generation Advanced Security systems for Communities Us...

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2024 International Symposium on Securing Next Generation Systems Using Future Artificial Intelligence Technologies, SNSFAIT 2024

作者： Quraishi, Aadam Rusho, Maher Ali Alghayadh, Faisal Yousef Mahalakshmi, V. Soni, Mukesh Bhatt, Mohammed Wasim Intervention Treatment Institute HoustonTX United States Department of Lockheed Martin Engineering Management University of Colorado Boulder BoulderCO United States Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Computer Science College of Engineering and computer science Jazan University Jazan45142 Saudi Arabia Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Model Institute of Engineering and Technology J&K Jammu India

According to human requirements, various structures are being constructed and require some protection from fire accidents, floods, earthquakes, any gas leaks, and any other concerns that may arise in the neighborhood. So far, their security goal is to propose a system integrated with blockchain and an IoT system. We structure the system in different steps in our proposal to process the alarm rung by IoT devices. This IoT gadget may be used in either a private or public setting. To do this, we must process the various settings for checking and responding to the devices. Data centers that interact between sender and recipient will verify blockchain data. Finally, we provide a security solution to avoid reply assaults, transmission interruptions, and data integrity. This entire system may work together to promote community safety and eliminate avoidable disputes. © 2024 Copyright for this paper by its authors.

关键词： Data integrity

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An Overview of GPT-4's Characteristics through the Lens of 10V's of Big Data 3

An Overview of GPT-4's Characteristics through the Lens of 1...

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3rd International Conference on Intelligent Cybernetics technology and Applications, ICICyTA 2023

作者： Alyasiri, Osamah Mohammed Akhtom, Dua'a Alrasheedy, Mashary N. Al-Furat Al-Awsat Technical University Karbala Technical Institute Karbala Iraq Universiti Sains Malaysia School of Computer Sciences Penang Malaysia University of Ha'il Department of Computer Science Applied College Ha'il Saudi Arabia Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Bangi Malaysia

ISBN: (纸本)9798350394559

The vast reservoirs of archival and consequential data within Big Data present an invaluable resource for organizations, capable of fortifying business decisions on factual evidence rather than subjective perceptions. The comprehensive framework of the 10 Vs - volume, veracity, velocity, variety, value, validity, variability, venue, vocabulary, and vagueness - represents the most sophisticated approach to defining Big Data. This research explores the potential of integrating GPT-4, a pre-trained Natural Language Processing (NLP) model, into the handling of Big Data within the context of these 10V's. Highlighting GPT-4's pivotal role in the era of massive information, the study confirms its alignment with key aspects of Big Data processing, including volume, variety, velocity, value, validity, vocabulary, and addressing vagueness, despite its limitation to textual and image data. Consequently, this research serves as a foundational guide for both researchers and industries, proposing GPT-4 as an effective AI tool for handling extensive and diverse Big Data with enhanced efficiency. © 2023 IEEE.

关键词： Data Analytics

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Real-Time Enhanced GAN-Powered Intrusion Prevention System: Safeguarding Networks with Advanced AI 2

Real-Time Enhanced GAN-Powered Intrusion Prevention System: ...

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2nd International Conference on Computing and Data Analytics, ICCDA 2024

作者： Sutherlin Subitha, G. Seldev Christopher, C. Wilfred Blessing, N.R. Sheeja Kumari, V. Stella Mary's College of Engineering Department of Computer Science and Engineering Tamil Nadu Kanyakumari India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Tamil Nadu Nagercoil India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Saveetha School of Engineering Simats Univeristy Department of Computational Intellegence Chennai600025 India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542573

In an era of escalating cyber threats and evolving networks, the demand for robust intrusion prevention is paramount. Introducing the Real-Time Enhanced GAN-Powered Intrusion Prevention System (RT-EGAN-IPS), a state-of-the-art solution fortified with advanced AI. RT-EGAN-IPS harnesses the power of Generative Adversarial Networks (GANs) to revolutionize real-time intrusion detection. GANs, renowned for their data synthesis capabilities, excel at uncovering subtle anomalies and emerging threats within network data. Its real-time adaptability enables it to swiftly identify novel attack patterns by continuously learning from incoming network data. Deep reinforcement learning and predictive analytics empower RT-EGAN-IPS to not only detect intrusions but autonomously respond, bolstering network protection. Versatile in handling multi-modal data streams, from text logs to multimedia content, RT-EGAN-IPS thrives on a collaborative approach. Security experts collaborate with the system for real-time validation and feedback. Dynamic thresholding ensures responsiveness to evolving threats, supported by low-latency responses and an ensemble approach. This abstract unveils RT-EGAN-IPS, a holistic AI-driven network security paradigm, promising a new level of protection in the ever-changing realm of cyber threats. © 2024 IEEE.

关键词： Network intrusion

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Synergistic Fusion of Multi-Contrast Imaging and Computational Image Synthesis for Quadruple Contrast-Enhanced Images and Multi-Map Generation

Synergistic Fusion of Multi-Contrast Imaging and Computation...

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2024 International Conference on Communication, computer sciences and Engineering, IC3SE 2024

作者： Mishra, Poonam Khalaf, Hameed Hassan Arabi, Mohammed Yousif Al-Shaikhli, Taha Raad Applied Mathematics Aset Amity University Chhattisgarh Raipur India Al-Manara College For Medical Sciences Maysan Iraq College Of Computer National University Of Science And Technology Dhi Qar Iraq AL-Nisour University College Department Of Medical Laboratories Technology Baghdad Iraq

ISBN: (纸本)9798350366846

Accurate tissue maps and a wide range of imaging modalities are required for medical imaging assessments and therapies. Our exclusive technology employs multi-contrast imaging and cutting-edge computer image synthesis to produce high-quality multi-maps and pictures of body regions and disorders. This approach uses deep learning fusion and computational image synthesis to construct multi-maps of disease markers and tissue features from several imaging modalities. Quality inspection and validation assure the correctness of the images and multi-maps. In terms of memory, accuracy, precision, and other performance measures, our strategy surpassed all other approaches. Reliable data gathering and visualization of physiological structures may enhance patient outcomes and medical imaging technologies. Applying the research results to other clinical contexts necessitates confirming the findings with real clinical data. The technology available might help medical practitioners comprehend and analyse complicated biological systems. There may be personalized treatment regimens and diagnostic imaging. © 2024 IEEE.

关键词： Medical imaging

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MRDPGA: a multiple restart dynamic population genetic algorithm for scheduling road traffic

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Journal of Electrical Systems and Information technology 2023年第1期10卷 1-18页

作者： Zachariah, Babangida Misra, Sanjay Odion, Philip O. Isah, Saidu R. Department of Computer Science Nigerian Defence Academy Kaduna Nigeria Department of Applied Data Science Institute for Energy Technology Halden Norway

A genetic algorithm is a biologically inspired stochastic approach to finding solutions to optimization problems. However, unlike its deterministic counterpart, it cannot guarantee a globally optimal solution since it may be trapped within a local optimum of the search space. Most researchers have focused on proposing new techniques for various parameters of genetic algorithms. That is a mutation, crossover, or selection algorithm. This research proposes a modification to the standard genetic algorithm, which may serve as a framework that can integrate any of these parameters for their contribution to the final solution of the genetic algorithm. The multiple restart dynamic population genetic algorithm (MRDPGA) proposed in this research was used in training the parameters of the adaptive neuro-fuzzy inference system (ANFIS) for scheduling road vehicular traffic flows. The results of training the ANFIS models based on the different clustering methods showed that the MRDPGA-based ANFIS controller performed better with the mean square error (MSE) of 0.299 and root mean square error (RMSE) of 0.547 in the training phase; and MSE of 0.272 and RMSE of 0.521 in the testing phase. Using the controllers for traffic flow scheduling, the results showed that the MRDPGA-trained controllers performed better in terms of average waiting time (AWT) minimization and total arrived vehicles (TAV). The best-performing controller achieved 50.40% AWT minimization and 21.44% TAV improvement. Analyzing the results using a one-tailed t-test for paired two-sample means showed that the MRDPGA algorithm had a significant impact on the controllers. Particularly the FCM controller, where (p = 0.0038) and (p = 0.0003) for AWT and TAV at a 95% confidence level. Thus, MRDPGA algorithms are recommended for further assessment in other optimization problems to ascertain their performance in those problem domains.

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Design and Implementation of a Hybrid Deep Learning Framework for Handwritten Text Recognition 3rd

Design and Implementation of a Hybrid Deep Learning Framewor...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Anand, Harshit Singh, Milind Rawade, Vivian Sahoo, Shubham Mishra, Sushruta Abualigah, Laith Kalinga Institute of Industrial Technology Deemed to Be University Bhubaneswar India Computer Science Department Al al-Bayt University Mafraq25113 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan Jadara Research Center Jadara University Irbid21110 Jordan

ISBN: (纸本)9789819708918

Text recognition technology has seen significant advancements in recent years, particularly with the use of Optical Character Recognition (OCR) to evaluate computer-generated text. However, there is much more work to be done in the field of Handwritten Text Recognition (HTR). The challenges posed by handwritten text, such as significant variations in strokes across writers, the vast variety of handwriting styles, human error and damages to the paper, present substantial difficulties in accurately identifying and recognizing handwritten alpha-numeric data. To address these challenges, we proposed a deep learning method that combines long short-term memory (MD-LSTM) and convolutional neural networks (CNN) architectures. This model can identify numbers and characters of the English language from input images. Based on MNIST dataset, bidirectional recurrent neural networks were used to construct the output sequence which was developed using the TensorFlow Framework. The accuracy for alphabets, numbers and alpha-numeric texts are 95.2%, 94.9% and 94.7% respectively. The mean character match index is computed to be 93.2%. The proposed model can substantially boost HRTs precision and efficiency making it more accessible. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

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