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7th International Conference on Information science and Systems, ICISS 2024

作者： Yao, Shumin Zhao, Qinglin Xu, Zezhou Feng, Li Li, Guanghui Tian, Liwei Sun, Yi School of Computer Science and Engineering Macau University of Science and Technology China Department of Broadband Communication Pengcheng Laboratory China School of Artificial Intelligence and Computer Science Jiangnan University China Department of Computer Guangdong University of Science and Technology China Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9798400717567

Single-carrier frequency domain contention (S-FDC) is an efficient wireless contention mechanism based on orthogonal frequency-division multiplexing (OFDM). In each round of S-FDC, each node randomly selects and signals on one of the OFDM subcarriers while listening to all subcarriers simultaneously. Each node independently detects the activated subcarrier with the smallest index and regards it as the winner subcarrier. Accurately detecting the winner subcarrier is crucial to ensuring the functionality of the network. However, it is extremely difficult in practice due to power leakage problems that arise from frequency asynchronization among nodes. In this paper, we propose a gated-recurrent-unit (GRU)-based model that incorporates S-FDC’s unique features as domain knowledge to enable accurate winner subcarrier detection. Extensive simulation experiments verify that our DL model is effective and outperforms state of the art. This research highlights the potential of DL in improving wireless communication systems. © 2024 Copyright held by the owner/author(s).

关键词： Frequency domain analysis

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A Blockchain based Framework for Verifying Drug Authenticity 24

A Blockchain based Framework for Verifying Drug Authenticity

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Hamid, Soomaiya Bawany, Narmeen Zakaria Fatima, Kiran Jinnah University for Women Center for Computing Research Department of Computer Science and Software Engineering Karachi Pakistan

ISBN: (纸本)9798350384307

Counterfeit drugs are fake medicines that are potentially harmful for health. Safeguarding the integrity of pharmaceutical distribution plays a crucial role in preventing the circulation of counterfeit drugs. Traditional drug supply chain systems have no effective mechanism to ensure the traceability and integrity of the drug supply chain process. Various ICT-based solutions have been adopted previously and numerous regulations were passed by drug regulatory authorities to ensure interoperable and traceable drug distribution process. However, these solutions could not guarantee a tamper-proof history of supply chain process. Blockchain technology has gained traction in many diverse fields including real-estate, identity management, digital voting, healthcare, etc. Therefore, we have proposed a decentralized framework to insure the authenticity and traceability of the drugs. Moreover, this study presents an overview of how blockchain technology can be employed to counter the fake medicines circulation by embedding the traceability in drug supply chain process. Comparison of various blockchain-based solutions and open research challenges in this regard are also presented. © 2023 IEEE.

关键词： Blockchain Technology Counterfeit Drugs decentralized framework Drug Supply Chain Traceability of Drugs Traditional Pharmaceutical Supply Chain

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Chat Bot in Banking Sector Using Machine Learning and Natural Language Processing 7th

Chat Bot in Banking Sector Using Machine Learning and Natura...

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7th IFIP TC 12 International Conference on Computational Intelligence in Data science, ICCIDS 2024

作者： Chokkalingam, S.P. Krishna, Pasumarti Vamsi Harshath, V. Reddy, Chavva Bala Kuswanth Sandeep, Yenumula Siva Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science Engineering Amrita School of Computing Amrita Vishwa Vidyapeetam Chennai601103 India

ISBN: (纸本)9783031699818

The demand for the effective customer services in banking industry has increased day by day and requirement of perfect solutions for the customer query also increased in these modern days. This research paper says about the design, development, various kind of chat bots present in now a days and how these chat bots are implemented and in which way it works according to customer queries. Chatbots are actually created to give accurate answers for banking inquires. The chat bot provides a user friendly interface for customers, they can ask information about their account balance, transaction history, fund transfer, loan application and other common banking tasks. The research methodology includes methods for how to access a normal chat bot in easy steps and various chatbots present now a days in banking industry and training of chatbot to understand and respond to diverse customer queries. The chat bot has advance machine learning algorithms and natural processing language to adapt for different queries and improve its performance in future and ensuring the perfect accuracy level of the answers. The system is combined with bank database so customers can see their bank details, ensuring real time access for their account information, secure transaction. Our research findings reveals that after coming of chat bot in banking sector the human customer services has been decreased and customer satisfaction has increased. After many testing of this model and according to user feedback the chat bot provides accurate solutions for their quires except in some situations. This research contains about growing field of artificial intelligence customer service solutions and potential benefits of implementing a banking chat bot to address common customer services ultimately leading to improved customer engagement and loyalty. © IFIP International Federation for Information Processing 2024.

关键词： Customer satisfaction

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A Comprehensive Approach to Groundwater Quality Prediction and Management in the State of Telangana, India, Using Stacking Ensemble Approach 4

A Comprehensive Approach to Groundwater Quality Prediction a...

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4th International Conference on Emerging Systems and Intelligent computing, ESIC 2024

作者： Senapati, Chinmayee Patra, Kumar Janardan Swain, Satyaprakash Jayasingh, Suvendra Kumar Senapati, Swagatika Ray, Niranjan Kumar Siksha 'o' Anusandhan Deemed to Be University Institute of Technical Education and Research Department of Civil Engineering Bhubaneswar India Odisha University of Technology and Research Department of Computer Science and Engineering Bhubaneswar India Institute of Management of Information Technology Cuttack Bput Department of Computer Science and Engineering Odisha India Institute of Management of Information Technology Cuttack Bput Department of Computer Science and Engineering Odisha India Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350349856

Groundwater, a vital resource for various purposes, faces increasing threats to its quality due to factors like overuse, pollution and climate change. In the semi-arid climate of the state of Telangana, India, exacerbated by intensive agricultural practices, groundwater quality is a significant concern. This study presents a machine learning-based approach for predicting and managing groundwater quality in Telangana, utilizing a stacking ensemble method. Our proposed approach combines decision trees (DT) with meta-models such as logistic regression, K-nearest neighbors (K-NN), Bayesian ridge regression (BRR) and random forest (RF). Trained on a comprehensive dataset from the Telangana Open Data portal (2018-2021), the stacking ensemble demonstrates superior performance, achieving an accuracy of 96.07%, surpassing the best individual model (DT) at 92.68%. It not only enhances predictive capabilities but also provides insights into the spatial distribution of groundwater quality, identifying areas vulnerable to contamination. These findings offer valuable information for policymakers, environmental scientists and water resource managers, guiding informed decision-making for sustainable water resource management in Telangana. This research contributes to the ongoing discourse on environmental conservation and the imperative for effective groundwater management. © 2024 IEEE.

关键词： Decision trees

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Secure Data Sharing with Confidentiality,Integrity and Access Control in Cloud Environment

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computer Systems science & engineering 2022年第2期40卷 779-793页

作者： V.Rajkumar M.Prakash V.Vennila Department of Computer Science and Engineering Krishnasamy College of Engineering and Technology(Affiliated to Anna UniversityChennai)Cuddalore607109India Department of Computer Science and Engineering School of ComputingSRM Institute of Science&TechnologyKattankulathur603203India Department of Computer Science and Engineering K.S.R.College of Engineering(Affiliated to Anna UniversityChennai)Tiruchengode637215India

Cloud storage is an incipient technology in today’s *** of security in cloud environment is one of the primary challenges faced these *** scenario poses new security issues and it forms the crux of the current *** current study proposes Secure Interactional Proof System(SIPS)to address this *** methodology has a few key essential components listed herewith to strengthen the security such as authentication,confidentiality,access control,integrity and the group of components such as AVK Scheme(Access List,Verifier and Key Generator).It is challenging for every user to prove their identity to the verifier who maintains the access *** is conducted by following Gulliou-Quisquater protocol which determines the security level of the user in multi-step authentication ***,RSA algorithm performs the key generation process while the proposed methodology provides data integrity as well as confidentiality using asymmetric *** methodological operations such as time consumption have been used as performance evaluators in the proposed SIPS *** proposed solution provides a secure system for firm data sharing in cloud environment with confidentiality,authentication and access *** Timed Petri(STPN)Net evaluation tool was used to verify and prove the formal analysis of SIPS *** evidence established the effectiveness of the proposed methodology in secure data sharing in cloud environment.

关键词： Secure interactional proof system access control multi-step authentication Gulliou-Quisquater protocol

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Deep Learning for Violence Detection in Surveillance: The Role of Transfer Learning and Pre-Trained Models 24

Deep Learning for Violence Detection in Surveillance: The Ro...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Ilyas, Anum Obaid, Surayya Bawany, Narmeen Zakaria Center for Computing Research Jinah University for Women Department of Computer Science and Software Engineering Karachi Pakistan

ISBN: (纸本)9798350384307

In recent years, the use of CCTV footage for proactive crime prevention has surged, particularly in public places like airports, train stations, and malls. However, the efficacy of these surveillance systems becomes questionable for their substantial reliance on human resources which may lead to erroneous or delayed responses. This research takes a comprehensive approach for violence detection which was previously limited to binary classification, by categorizing violence into four distinct classes: abuse, arson, assault, and fight. It employs transfer learning combined with computer vision techniques for violence detection and classification in video footages. The study compares the performance of four pre-trained neural networks, namely DenseNet121, VGG16, MobileNet, and Xception. The dataset created for this research is compiled from three datasets available on Kaggle. The results reveal that the Xception model performed comparatively better achieving the highest AUC score of 98.39%, while the VGG16 model attained the lowest AUC score of 96%. In addition to the AUC score, precision, recall, and fl-score are employed as performance metrics. Transfer learning with convolutional neural networks (CNN) significantly reduced computational requirements and time. Automating the detection and categorization of violent behavior through the employed approach has the potential to reduce the risk of fatalities and injuries in public areas. It also improves the speed and accuracy of threat detection, enabling swift preventive actions by authorities. © 2023 IEEE.

关键词： computer Vision Deep Learning Pre-trained Models Transfer Learning Violence Categorization

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Classifying Grape Leaf Disease using an Optimized YOLOv7 Deep Learning Model 4

Classifying Grape Leaf Disease using an Optimized YOLOv7 Dee...

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4th International Conference on Soft computing for Security Applications, ICSCSA 2024

作者： Abraham, John T Hussain, Mohammad Manzoor Srividhya, R. Linda, P Edith Karthik, S. Nareshkumar, R. Bharata Mata College Department of Computer Science Kochi India B v Raju Institute of Technology Department of Computer Science and Engineering Telangana Medak India Dr G R Damodaran College of Science Department of Computer Science Coimbatore India Kristu Jayanti College Department of Computer Science Bengaluru India SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Networking and Communications Tamilnadu Kattankulathur India

ISBN: (纸本)9798331515720

The prevalence of grape leaf diseases has had a profound influence on the worldwide grape industry, resulting in considerable financial setbacks. For effective disease management, it is essential to classify the disease quickly and accurately. This research presents an enhanced YOLOv7 deep-learning model for accurately classifying grape leaf diseases. The dataset is obtained from Kaggle and consists of four categories: black rot, leaf blight, black measles, and healthy. The Adam optimizer is utilized to optimize hyperparameters in order to enhance the performance of the model. The refined YOLOv7 model achieved a remarkable accuracy of 99.24% on the test set, showcasing its potential for detecting grape leaf diseases at an early stage. The model in precision agriculture can aid in the management of grape production, categorization of leaf diseases, and monitoring of operations. © 2024 IEEE.

关键词： Contrastive Learning

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Comparative Analysis of Traditional Instruction and POGIL: A Student-Centered Learning Approach in Civil engineering

Comparative Analysis of Traditional Instruction and POGIL: A...

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2024 ASEE Annual Conference and Exposition

作者： Malliga, P. Dinesh Kumar, K.S.A. Gangathulasi, Janardhanan Velu, Shanmuganeethi Arivalagan, S. National Institute of Technical Teachers Training and Research Chennai India Department of Computer Science and Engineering India

Students in the twenty-first century need to think creatively, collaborate creatively with others, and use innovation in their daily lives. This means that students have to equip themselves to develop creative solutions to the many problems and challenges that they may face. Still many educational institutions are following the traditional teacher - centered approach predominantly which is inappropriate to develop the twenty-first century skills among the students. The conventional method of teaching, which emphasises memorization and standardised testing through lectures, rote learning, and memorization, may impede the development of critical thinking, problem solving, and creative thinking skills that are essential in everyday life. In addition, the conventional teaching methods can be monotonous and inflexible, which can cause students to lose interest and motivation in their studies. To make students more attentive in class, students centered approach need to be implemented. Variety of instructional strategies are in practice to engage the students in learning, to enhance student learning, and also to provide opportunity for students to reflect on their learning. One such student-centered instructional strategy is Process Oriented Guided Inquiry Learning (POGIL). This active learning strategy aims to engage students in the learning process through guided inquiry. This paper describes implementation of process-oriented guided inquiry learning in an engineering classroom in one of our civil engineering subject for cognitive lesson. The POGIL strategy divided into five phases namely Exploration, Concept Invention, Application, Reflection and Extension. In this student-centered approach, the teacher's job is to facilitate learning by encouraging students to apply what they've learned to solve more difficult problems or scenarios that are connected to the subject, which helps them to gain deeper comprehension of the material. This allows students to actively comprehe

关键词： Students

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Temporal Difference-Aware Graph Convolutional Reinforcement Learning for Multi-Intersection Traffic Signal Control

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IEEE Transactions on Intelligent Transportation Systems 2024年第1期25卷 327-337页

作者： Lin, Wei-Yu Song, Yun-Zhu Ruan, Bo-Kai Shuai, Hong-Han Shen, Chih-Ya Wang, Li-Chun Li, Yung-Hui National Yang Ming Chiao Tung University Department of Electrical and Computer Engineering Hsinchu30010 Taiwan National Tsing Hua University Department of Computer Science Hsinchu300 Taiwan Hon Hai Research Institute Ai Research Center New Taipei City11492 Taiwan

Traffic light control plays a crucial role in intelligent transportation systems. This paper introduces Temporal Difference-Aware Graph Convolutional Reinforcement Learning (TeDA-GCRL), a decentralized RL-based method for efficient multi-intersection traffic signal control. Specifically, we put forward a new graph architecture using each lane as a node for considering intersection relations. Additionally, we propose two new rewards by considering temporal information, namely Temporal-Aware Pressure on Incoming Lanes (TAPIL) and Temporal-Aware Action Consistency (TAAC), which enhance learning efficiency and time-interval sensitivity. Experimental results on five datasets show the superiority of TeDA-GCRL over state-of-the-art methods by at least 9.5% in average travel time. © 2000-2011 IEEE.

关键词： Reinforcement learning

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All-photonic artificial-neural-network processor via nonlinear optics

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Physical Review applied 2024年第1期22卷 014009-014009页

作者： Jasvith Raj Basani Mikkel Heuck Dirk R. Englund Stefan Krastanov Department of Electrical and Electronics Engineering Department of Electrical and Computer Engineering Institute for Research in Electronics and Applied Physics and Department of Electrical Engineering and Computer Science Department of Electrical and Photonics Engineering College of Information and Computer Sciences

Optics and photonics have recently captured interest as a platform to accelerate linear matrix processing, otherwise a bottleneck in traditional digital electronics. In this paper we propose an all-photonic computational accelerator wherein information is encoded in the amplitudes of frequency modes stored in a single ring resonator. Interaction among these modes is enabled by nonlinear optical processes. Both the matrix multiplication and elementwise activation functions on these modes (the artificial neurons) are performed through coherent processes, enabling the direct representation of negative and complex numbers without having to pass through digital electronics, a common limitation in today’s photonic architectures. This design also has a drastically lower hardware footprint compared with today’s electronic and optical accelerators, as the entirety of the matrix multiplication happens in a single multimode resonator on chip. Our architecture is unique in providing a completely unitary, reversible mode of computation, enabling on-chip analog Hamiltonian-echo backpropagation for gradient descent and other self-learning tasks. Moreover, the computational speed increases with the power of the pumps to arbitrarily high rates, as long as the circuitry can sustain the higher optical power. Lastly, the design presented here is a less demanding version of a future room-temperature quantum computational device. Therefore, while this architecture is already viable today, direct reinvestments in it would be enabling its evolution into quantum computational hardware.

关键词： Machine learning Neuromorphic computing Nonlinear optics Photonics Artificial neural networks Deep learning Neural network simulations Optical computing

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