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4th International Conference on Computing, Mathematics and Engineering Technologies, iCoMET 2023

作者： Khaliq, Abdul Tahir, Mansoor Ahmed Nadeem, Ghalib Adil, Syed Hasan Jamshid, Junaid Memon, Jamil Ahmed Iqra University Department of Computer Science Karachi Pakistan Acbt Department of Computer Science Toronto Canada Neduet Department of Electronic Enginnering Karachi Pakistan Shanghai University Department of Information Technology China

ISBN: (纸本)9798350335316

In software-defined networking, The network control plane's physical separation from the forwarding plane from where several devices are being controlled from the control plane. Therefore, software-defined networking is an emergent dynamic architect, quite manageable, cost-effective, and readily adaptable which makes it immensely ideal for high bandwidth hence, dynamic for today's application. Whereas, with this architecture the network controlling and forwarding functions can be separated, allowing the underlying infrastructure to be abstracted for applications and network services and making network control easily programmable. Hence, the fundamental element for building SDN solutions is the OpenFlow® protocol. In this paper, our crucial objective is to provide (in particular) uninterrupted delivery of services using available resources using all networked devices, links, and the web. The servers are on. An average improvement of 23-53 % was observed using the round-robin forwarding strategy. © 2023 IEEE.

关键词： Software defined networking

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Machine Learning based Iris Recognition Modern Voting System

Machine Learning based Iris Recognition Modern Voting System

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2023 IEEE International Conference on Innovative Data Communication Technologies and Application, ICIDCA 2023

作者： Aparna, D.K. Dharshini, V.S. Rajeshkumar, G. Mohana Priya, D. Balasubrarnanie, P. Hamsanandhini, S. Bannari Amman Institute of Technology Department of Information Technology Sathyamangalam India Bannari Amman Institute of Technology Department of Information Science and Engineering Sathyamangalam India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Kongu Engineering College Department of Computer Science and Engineering Erode India

ISBN: (纸本)9798350397208

A paper ballot or an Electronic Voting Machine (EVM) based on Direct Response Electronic (DRE) or Identical Ballot Boxes have traditionally been used for voting. This study recommends a digital voting system based on Machine Learning algorithm that uses Iris recognition to address the flaws in the current voting process in order to fix the traditional voting system's flaws. A program called the Iris recognition-based Voting System identifies people based on the iris pattern of their eyes. Iris recognition is an automated biometric identification technology that analyses video evidence of one or both of an individual's iris to identify complex patterns that are distinct, stable, and visible from a distance. A voter may only cast one ballot, where the proposed technology prohibits multiple votes from the same person because it can spot duplicate entries. Additionally, this technique does away with the need for the user to carry a voter ID that has the relevant information since the Aadhar is incorporated with the voter ID thus enhancing the digitalization by means of digital verification of biometric and iris pattern available in Aadhar card of every user. At the voting venue, a simple iris scan will allow the voter's iris to be collected and used as identification. The iris recognition process consists of the following four steps: image acquisition, iris segmentation, feature extraction, and pattern matching. Iris recognition is one of the most trustworthy biometric modalities due to its high identification rate. Thereby this system eliminates the major drawbacks of traditional voting systems and enhances the digital voting by incorporating the modern transformation. © 2023 IEEE.

关键词： Feature extraction

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Transforming Classification with Federated Learning on Blockchain: A Unique Model Integration Approach

Transforming Classification with Federated Learning on Block...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Hao, Zhihao Zhang, Bob Li, Haisheng School of Computer Science and Artificial Intelligence Beijing Technology and Business University Beijing China Dept. of Computer and Information Science Faculty of Science and Technology University of Macau Taipa China Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau Taipa China

ISBN: (纸本)9798350368741

The need for robust machine learning models is particularly evident in the realm of biological pattern recognition. Traditional centralized methods often struggle, as they frequently depend on large datasets that are challenging to gather due to stringent data privacy regulations. To address these limitations while maintaining classification accuracy, we propose an innovative approach that unifies models with diverse architectures within a federated learning framework built on a blockchain network. This decentralized and trustworthy system fosters effective collaboration among various models. Furthermore, we have implemented a weight distribution mechanism designed to maximize the individual strengths of each model. By leveraging blockchains inherent transparency and auditability, this approach also ensures secure and traceable data exchanges among participants. Additionally, the adaptability of the framework allows it to be extended to other domains where privacy-preserving data sharing is critical. Our experimental results showcase that the proposed methodology significantly enhances performance in classification tasks compared to existing alternatives. © 2025 IEEE.

关键词： blockchain collaborative training Federated Learning heterogeneous models

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Collaborative decision making system in intelligent transportation system using distributed blockchain technology

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International Journal of Vehicle information and Communication Systems 2022年第1期7卷 64-81页

作者： Mohanta, Bhabendu Kumar Jena, Debasish Satapathy, Utkalika Ramasubbareddy, Somula Department of Computer Science and Engineering International Institute of Information Technology Odisha Bhubaneswar India Telangana Hyderabad India

Intelligent Transportation System (ITS) is one of the promising applications of the Internet of Things (IoT) as the IoT system provides an easy way to collect and monitor the system. One of the critical components to make a city smart is by managing the traffic. The mode of transportation in the city is different, like bike, car, bus, auto, and rickshaw. Most of the vehicles integrated with information Communication technology (ICT). As the vehicles share and access the information from the ITS infrastructure, some security issue exists. The security issues are trust management, privacy, data linking, and computational problems. In this paper, the authors first identify the security issue present in the ITS model, then propose a distributed architecture of the ITS system using Blockchain. Then, the Consensus algorithm is used to perform computations in a distributed platform. Ethereum platform used to create a distributed network. The implementation and security analysis are given at the end. © 2022 Inderscience Enterprises Ltd.

关键词： Decision making

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Cluster-preserving sampling algorithm for large-scale graphs

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science China(information sciences) 2023年第1期66卷 60-76页

作者： Jianpeng ZHANG Hongchang CHEN Dingjiu YU Yulong PEI Yingjun DENG National Digital Switching System E&T Research Center Information Engineering University Network Systems Department of the Strategic Support Force School of Computer Science and Technology Eindhoven University of Technology Center for Applied Mathematics Tianjin University

Graph sampling is a very effective method to deal with scalability issues when analyzing largescale graphs. Lots of sampling algorithms have been proposed, and sampling qualities have been quantified using explicit properties(e.g., degree distribution) of the sample. However, the existing sampling techniques are inadequate for the current sampling task: sampling the clustering structure, which is a crucial property of the current networks. In this paper, using different expansion strategies, two novel top-leader sampling methods(i.e., TLS-e and TLS-i) are proposed to obtain representative samples, and they are capable of effectively preserving the clustering structure. The rationale behind them is to select top-leader nodes of most clusters into the sample and then heuristically incorporate peripheral nodes into the sample using specific expansion strategies. Extensive experiments are conducted to investigate how well sampling techniques preserve the clustering structure of graphs. Our empirical results show that the proposed sampling algorithms can preserve the population's clustering structure well and provide feasible solutions to sample the clustering structure from large-scale graphs.

关键词： graph sampling clustering structure top-leader nodes expansion strategies large-scale graphs

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Tropical Cyclone Detection and Tracking Using YOLOv8 Algorithm 1

Tropical Cyclone Detection and Tracking Using YOLOv8 Algorit...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Rout, Saroja Kumar Kumar, Kottu Santosh Sahu, Ruchismita Barik, Shekharesh Pradhan, Samarendra Department of Information Technology Hyderabad India Department of Artificial Intelligence and Machine Learning Hyderabad India Templecity Institute of Technology and Engineering Department of Computer Science & Engineering Bhubaneswar India DRIEMS University Department of Computer Science & Engineering Cuttack India Data scientist Jayapur Cuttack India

ISBN: (纸本)9798350361780

Coastal areas around the world face a great threat from tropical cyclones, which makes timely and accurate identification essential for efficient disaster response. An enhanced method for detecting tropical cyclones is proposed in this work using the YOLOv8 (You Only Look Once version 8) object identification algorithm. Using the real-time object recognition capabilities of YOLOv8, the methodology is designed to tackle the challenges associated with finding cyclonic structures in satellite data. To detect crucial features typical of tropical cyclones - like cloud formations, circular symmetry, and dynamic atmospheric fluctuations - the YOLOv8 architecture is improved. To ensure its adaptability to different climatic circumstances, the model is trained using a varied dataset of labeled satellite photos. The YOLOv8-based methodology is superior to existing methods in detecting tropical cyclones with accuracy and efficiency almost in real-time, as demonstrated by the experimental results. Because of the algorithm's excellent precision, recall, and computing economy, early warning systems and catastrophe preparedness can benefit greatly from it. This research contributes to the ongoing effort to develop detection approaches for increased resilience in vulnerable coastal areas, while also utilizing YOLOv8's capabilities to address the global threat posed by tropical cyclones. This study can help clarify the necessity of improving models and conducting better observations of Tropical Cyclones. © 2024 IEEE.

关键词： Object detection

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Lexicon and Attention Based Handwritten Text Recognition System

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Machine Graphics and Vision 2022年第1-4期31卷 75-92页

作者： Kumari, Lalita Singh, Sukhdeep Rathore, Vaibhav Varish Singh Sharma, Anuj Department of Computer Science and Applications Panjab University Chandigarh India Punjab Moga India Computer Networking and Information Technology PRL Gujarat Ahmedabad India

The handwritten text recognition problem is widely studied by the researchers of computer vision community due to its scope of improvement and applicability to daily lives. It is a sub-domain of pattern recognition. Due to advancement of computational power of computers since last few decades neural networks based systems heavily contributed towards providing the state-of-the-art handwritten text recognizers. In the same direction, we have taken two state-of-the art neural networks systems and merged the attention mechanism with it. The attention technique has been widely used in the domain of neural machine translations and automatic speech recognition and now is being implemented in text recognition domain. In this study, we are able to achieve 4.15% character error rate and 9.72% word error rate on IAM dataset, 7.07% character error rate and 16.14% word error rate on GW dataset after merging the attention and word beam search decoder with existing Flor et al. architecture. To analyse further, we have also used system similar to Shi et al. neural network system with greedy decoder and observed 23.27% improvement in character error rate from the base model. © 2022 faculty of Applied Informatics and Mathematics - WZIM, Warsaw University of Life sciences - SGGW. All rights reserved.

关键词： Decoding

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AI-Driven Virtual Mock Interview Development 13

AI-Driven Virtual Mock Interview Development

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Mishra, Prabhat Kumar Arulappan, Arun Kumar Ra, In-Ho Thanga Mariappan, L. Gina Rose, G. Lee, Young-Seok School of Computer Science Engineering and Information Systems Vit University Vellore India School of Computer and Software Kunsan National University Gunsan Korea Republic of Anna University Department of Computer Technology Chennai India

ISBN: (纸本)9798350373332

The integration of Artificial Intelligence (AI) into educational technologies marks a significant shift in learning methodologies and operational dynamics within educational institutions. At the forefront is an AI-driven virtual mock interview platform designed to address the high Customer Acquisition Costs (CAC) in the edtech sector, especially for interview preparation services. This initiative harnesses a blend of AI technologies, including ADA 2 for creating context-aware embeddings and Machine Learning (ML), to transform the traditional mock interview process into a dynamic, cost-effective system. Central to the platform is its use of advanced Natural Language Processing (NLP) techniques and GPT-4 Large Language Model (LLM), automating the process of mock interviews and providing personalized feedback, ensuring a preparation journey that meets specific candidate needs and mirrors real interview scenarios. A key evaluation among 100 students from a cohort of 1800 demonstrated a 90% cost reduction for three mock interviews, reducing expenses from 3000 to just 300 per candidate. This cost efficiency significantly enhances access to quality interview preparation, improving student satisfaction and accessibility. Moreover, the platform provides valuable insights into student performance, setting a new standard in educational technology by offering an effective, personalized interview preparation experience. This project reflects a holistic approach to student development and the critical role of technology in addressing the evolving needs of learners. © 2024 IEEE.

关键词： Cost reduction

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CD-FL:Cataract Images Based Disease Detection Using Federated Learning

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computer Systems science & Engineering 2023年第11期47卷 1733-1750页

作者： Arfat Ahmad Khan Shtwai Alsubai Chitapong Wechtaisong Ahmad Almadhor Natalia Kryvinska Abdullah Al Hejaili Uzma Ghulam Mohammad Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Information Systems Department Faculty of ManagementComenius University in BratislavaOdbojárovBratislava440Slovakia Faculty of Computers&Information Technology Computer Science DepartmentUniversity of TabukTabuk71491Saudi Arabia Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan

A cataract is one of the most significant eye problems worldwide that does not immediately impair vision and progressively worsens over *** cataract prediction based on various imaging technologies has been addressed recently,such as smartphone apps used for remote health monitoring and eye *** recent years,advances in diagnosis,prediction,and clinical decision support using Artificial Intelligence(AI)in medicine and ophthalmology have been *** to privacy concerns,a lack of data makes applying artificial intelligence models in the medical field *** address this issue,a federated learning framework named CDFL based on a VGG16 deep neural network model is proposed in this *** study collects data from the Ocular Disease Intelligent Recognition(ODIR)database containing 5,000 patient *** significant features are extracted and normalized using the min-max normalization *** the federated learning-based technique,the VGG16 model is trained on the dataset individually after receiving model updates from two *** transferring the attributes to the global model,the suggested method trains the local *** global model subsequently improves the technique after integrating the new *** client analyses the results in three rounds to decrease the over-fitting *** experimental result shows the effectiveness of the federated learning-based technique on a Deep Neural Network(DNN),reaching a 95.28%accuracy while also providing privacy to the patient’s *** experiment demonstrated that the suggested federated learning model outperforms other traditional methods,achieving client 1 accuracy of 95.0%and client 2 accuracy of 96.0%.

关键词： Privacy-preserving cataract disease federated learning fundus images healthcare smartphone applications machine learning

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Comparative study of IoT- and AI-based computing disease detection approaches

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Data science and Management 2025年第1期8卷 94-106页

作者： Rhmann, Wasiur Khan, Jalaluddin Khan, Ghufran Ahmad Ashraf, Zubair Pandey, Babita Khan, Mohammad Ahmar Ali, Ashraf Ishrat, Amaan Alghamdi, Abdulrahman Abdullah Ahamad, Bilal Shaik, Mohammad Khaja Department of Computer Application Lovely Professional University Punjab Phagwara144411 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur522502 India School of Computing Science and Engineering VIT Bhopal University Kothrikalan MP Sehore466114 India Babasaheb Bhimrao Ambedkar University Lucknow226025 India College of Commerce and Business Administration Dhofar University Salalah211 Oman Faculty of Computer Studies Arab Open University 18211 Bahrain Department of Computer Application Shri Ramswaroop Memorial University Lucknow225003 India College of Computing and Information Technology Shaqra University Shaqra11961 Saudi Arabia

The emergence of different computing methods such as cloud-, fog-, and edge-based Internet of Things (IoT) systems has provided the opportunity to develop intelligent systems for disease detection. Compared to other machine learning models, deep learning models have gained more attention from the research community, as they have shown better results with a large volume of data compared to shallow learning. However, no comprehensive survey has been conducted on integrated IoT- and computing-based systems that deploy deep learning for disease detection. This study evaluated different machine learning and deep learning algorithms and their hybrid and optimized algorithms for IoT-based disease detection, using the most recent papers on IoT-based disease detection systems that include computing approaches, such as cloud, edge, and fog. Their analysis focused on an IoT deep learning architecture suitable for disease detection. It also recognizes the different factors that require the attention of researchers to develop better IoT disease detection systems. This study can be helpful to researchers interested in developing better IoT-based disease detection and prediction systems based on deep learning using hybrid algorithms. © 2024 Xi'an Jiaotong University

关键词： Adversarial machine learning

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