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arXiv 2024年

作者： Tsirigotis, Aris Sarantoglou, George Deligiannidis, Stavros Sánchez, Erica Gutierrez, Ana Bogris, Adonis Capmany, Jose Mesaritakis, Charis Dept. Information and Communication Systems Engineering University of the Aegean Palama 2 str Samos Karlovasi83200 Greece Dept. Informatics and Computer Engineering University of West Attica Ag. Spiridonos Egaleo12243 Greece iPronics Programmable Photonics Valencia Spain Photonics Research Labs Universitat Politècnica de València Camino de Vera Valencia46022 Spain

In this work, we present and experimentally validate a passive photonic-integrated neuromorphic accelerator that uses a hardware-friendly optical spectrum slicing technique through a reconfigurable silicon photonic mesh. The proposed scheme acts as an analogue convolutional engine, enabling information preprocessing in the optical domain, dimensionality reduction and extraction of spatio-temporal features. Numerical results demonstrate that utilizing only 7 passive photonic nodes, critical modules of a digital convolutional neural network can be replaced. As a result, a 98.6% accuracy on the MNIST dataset was achieved, with a power consumption reduction of at least 26% compared to digital CNNs. Experimental results confirm these findings, achieving 97.7% accuracy with only 3 passive nodes. Copyright © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Video key concept extraction using Convolution Neural network

Video key concept extraction using Convolution Neural Networ...

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AI in Cybersecurity (ICAIC), International Conference on

作者： Tanvir H Sardar Ruhul Amin Hazarika Bishwajeet Pandey Guru Prasad M S Sk Mahmudul Hassan Radhakrishna Dodmane Hardik Gohel Dept. of Computer Science & Engg School of Technology (GST) GITAM University Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru India Department of Intelligent System and Cyber Security Astana IT University Kazakhstan Dept. of Computer Science & Engg. Graphic Era (Deemed to be University) Dehradun India School of Computer Science and Engineering VIT-AP India Department of Computer Science & Engg. NMAM Institute of Technology Nitte India Department of CSE University of Houston Victoria USA

Objectives: This work aims to develop an automated video summarising methodology and timestamping that uses natural language processing (NLP) tools to extract significant video ***: The methodology comprises extracting the audio from the video, splitting it into chunks by the size of the pauses, and transcribing the audio using Google's speech recognition. The transcribed text is tokenised to create a summary, sentence and word frequencies are calculated, and the most relevant sentences are selected. The summary quality is assessed using ROUGE criteria, and the most important keywords are extracted from the transcript using ***: Our proposed method successfully extracts key points from video lectures and creates text summaries. Timestamping these key points provides valuable context and facilitates navigation within the lecture. Our method combines video-to-text conversion and text summarisation with timestamping key concepts, offering a novel approach to video lecture analysis. Existing video analysis methods focus on keyword extraction or summarisation, while our method offers a more comprehensive approach. Our timestamped key points provide a unique feature compared to other methods. Our method enhances existing video reports by (i) providing concise summaries of key concepts and (ii) enabling quick access to specific information through timestamps. (iii) Facilitating information retrieval through a searchable index. Further research directions: (i) Improve the accuracy of the multi-stage processing pipeline. (ii) Develop techniques to handle diverse accents and pronunciations. (iii) Explore applications of the proposed method to other video genres and ***/Improvements: This approach is practical in giving accurate video summaries, saving viewers time and effort when comprehending the main concepts presented in a video.

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A Low-overhead PUF-based Secure Scan Design

A Low-overhead PUF-based Secure Scan Design

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IEEE International Symposium on Quality Electronic Design

作者： Wei Zhou Aijiao Cui Cassi Chen Gang Qu School of Electronic and Information Engineering Harbin Institute of Technology (Shenzhen) Guangdong China University of California Berkeley USA Dept. of Electrical and Computer Engineering and Institute of Systems Research Univ. of Maryland College Park USA

Scan-based side-channel attacks have become a severe threat to the security of cryptographic chips and locking mechanisms are one of the most effective methods against these attacks. However, securing the test key that locks the scan but must be shared among test engineers arises as a new challenge. In this paper, we solve this challenge by adopting the physical unclonable function (PUF) design to generate test keys that are unique for each chip. A one-time programming structure (OTPS) is used when the PUF response is first generated to improve its reliability. The security of the PUF response is achieved by obfuscation such that it can be retrieved only when a specific validation test vector presents. We implement the proposed secure scan design by reusing the original scan chain to reduce overhead. We demonstrate that the proposed secure scan design can protect the crypto chips against all existing scan-based side-channel attacks while incurring negligibly low overhead.

关键词： Side-channel attacks Resists Programming Physical unclonable function Reliability engineering Silicon Registers

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BLOCK-SECURE: AI-Based Blockchain Enabled Secure Framework for IoMT Applications

BLOCK-SECURE: AI-Based Blockchain Enabled Secure Framework f...

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International Conference on e-health networking, Applications and Services (HealthCom)

作者： Barkha Panchal Jitendra Bhatia Malaram Kumhar Sudeep Tanwar Ashit Kumar Dutta Joel J.P.C. Rodrigues Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Dept. of Comp. Science and Information Systems College of Applied Sciences AllMaarefa University Kingdom of Saudi Arabia Amazonas State University Manaus - AM Brazil

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350555

The Internet of Medical Things (IoMT) revolution-izes healthcare by integrating medical devices and systems with the internet. However, the vast amounts of sensitive medical data in IoMT networks pose significant security and privacy concerns. Traditional security measures often fall short in identifying the malicious data attacks within the IoMT ecosystems. This paper introduces an AI-based non-malicious data classification scheme based on blockchain. We applied and evaluated the machine learning (ML) classifiers, such as support vector machine (SVM), random forest (RF), and K-Nearest neighbor (KNN). We evaluated the proposed framework based on various performance metrics that includes accuracy, precision, recall, and F1 score. The accuracy using SVM obtained 75.3%, RF is 75.9%, and K-NN is 84.3%. The results shows that KNN performs better than other models hy the factor of 9%.

关键词： Support vector machines Radio frequency Performance evaluation Accuracy Biological system modeling Nearest neighbor methods Blockchains Sensors security Random forests

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Using AI and Natural Language Processing to Enhance Consumer Banking Decision-Making

Using AI and Natural Language Processing to Enhance Consumer...

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E-mobility, Power Control and Smart systems (ICEMPS), International Conference on

作者： Sheetal Temara Sagar Varma Samanthapudi Piyush Rohella Ketan Gupta Ashokkumar S. R Dept of Computer & Information Sciences University of the Cumberlands Williamsburg KY USA Applied Data Science The Polis Center at Indiana University Purdue University Indianapolis Indianapolis Indiana USA Payments & Commerce GoDaddy Edmonds Washington USA Dept. of Information Technology University of The Cumberlands Williamsburg KY USA Department of Computer and Communication Engineering Centre for Blockchain and Cyber security Sri Eshwar College of Engineering Coimbatore Tamil Nadu INDIA

ISBN: (数字)9798350394399

ISBN: (纸本)9798350394405

This technical summary outlines the use of Artificial Intelligence (AI) and Natural Language Processing (NLP) to beautify patron banking decision-making. AI-based banking systems can autonomously identify patterns in consumer information and generate insights from a number of sources, including but not restricted to purchaser 360 profiles, 1/3-party statistics, and transactional statistics. With the assistance of AI and NLP, this gadget can answer customer questions relating to their banking wishes and offer personalized recommendations. AI-based monetary decision-making systems also permit banks to offer computerized customer service with information on clients' needs, respond speedily to purchaser requests, and offer quick acclaim for loans and other banking merchandise. Moreover, AI and NLP enhance patron engagement via chatbot-like solutions and purchaser sentiment analysis. The extraordinary AI and NLP-based services being presented by using banks are mentioned in this technical abstract. In conclusion, AI and NLP technology are giving banks the capacity to meet the desires of their customers and improve typical purchaser pleasure.

关键词： Sentiment analysis IEEE merchandise Customer services Decision making Power control Customer satisfaction Process control

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AI in Agriculture: Yield Optimization and Food security

AI in Agriculture: Yield Optimization and Food Security

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Lavanya Chavan Rishikesh Kumbhalkar Poorva Agrawal Nitin Rakesh R. Parvathi Mohammad Shahid Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Dept. of Smart Computing School of Computer Science Engineering and Information Systems Vellore Institute of Technology (VIT) Vellore India Noida Institute of Engineering and Technology Greater Noida India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Artificial Intelligence (AI) is remarkably automating the agricultural methods. In this process, AI is helping to optimize yield, and improving food security in a world with growing population and environmental concern(s). AI technology integration in agriculture is the focus of this paper which includes predictive analytics, machine learning and IoT. These applications led to precision farming and resource management which lead to efficient use of resources. The discussion on the benefits of AI in increasing productivity and sustainability is based on the summarization of the latest literature and empirical studies. Yet, these technologies bring along their own issues such as initial capital investment to implement the systems in emergence economies as well as the need for the rapid upgrading of the infrastructure and human capital. This research demonstrates that AI has the potential to change agriculture radically, while at the same time acknowledging the pending problems that hinder full-fledged implementation.

关键词： Training Food security Agriculture Resource management Artificial intelligence Sustainable development Socioeconomics Optimization Farming Investment

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Weapon detection system for surveillance and security

Weapon detection system for surveillance and security

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Shehzad Khalid Abdullah Waqar Hoor Ul Ain Tahir Onome Christopher Edo Imokhai Theophilus Tenebe Dept. of Computer Engineering Bahria University Islamabad Pakistan Department of Information Systems Auburn University at Montgomery Alabama USA Mineta Transportation Institute San Jose State University California United States

Weapon detection is a critical and serious topic in terms of public security and safety, however, it's a difficult and time-consuming operation. Due to the increase in demand for security, safety, and personal property protection, the requirement for deploying video surveillance systems capable of recognizing and interpreting scenes and anomalous occurrences plays an important role in intelligence monitoring. In certain regions of the globe, mass shootings and gun violence are on the increase. Timely detection of the presence of a gun is critical to prevent loss of life and property. Several object detection models are available, which struggle to recognize firearms due to their unique size and form, as well as the varied colors of the background. In this study, we proposed a state-of-the-art system based on a deep learning model YOLO V5 for weapon detection that will be sufficiently resilient in terms of affine, rotation, occlusion, and size. We evaluated the performance of our system on a publicly available dataset and achieved the F1-score of 95.43%. For the detection and segmentation of firearms, we implemented Instance segmentation or Pixel level segmentation which employed Mask-RCNN. The system achieved the detection accuracy (DC) of 90.66% and 88.74% Mean intersection over union (mIoU). The purposed methodology combined different data augmentation and preprocessing methods to improve the accuracy of the proposed weapon detection system.

关键词： Deep learning Measurement Technological innovation Weapons Object detection Video surveillance Real-time systems

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An Artificial Neural network Model to Forecast Grain Crops in North-Kazakstan Agricultural Experimental Station

An Artificial Neural Network Model to Forecast Grain Crops i...

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Communications, information, Electronic and Energy systems (CIEES), International Conference on

作者： Aigul Mimenbayeva Maylen Omirtay Rozamgul Niyazova Gulmira Bekmagambetova Raya Suleimenova Ainur Tursumbayeva Dept. of Computational and Data Sciences Astana IT University Astana Kazakstan Specialty Smart Technologies Astana IT University Astana Kazakstan Dept. of Artificial Intelligence Technologies L. Gumilyov Eurasian National University Astana Kazakhstan Dept. of Computer Science S. Seifullin Kazakh Agrotechnical Research University Astana Kazakstan School of Engineering and Information Technology Eurasian Technological University Almaty Kazakstan Dept. of Information Systems S. Seifullin Kazakh Agrotechnical Research University Astana Kazakstan

The paper analyzes and predicts the yield of grain crops of LLP “North-Kazakhstan Agricultural Experimental Station” for the last 30 years. The class of artificial neural networks-multilayer perceptron (MLP) was used as a research tool. The process of training of a model of a neural network consisting of 30 networks was performed on the basis of 70% training and 30% control samples. Based on the analysis of residuals histogram and scatter plot of the target and output function, the best neural network models MLP-1-7-1 (p=0.92), MLP-1-8-1 (p=0.97), MLP-1-5-1 (p=0.82) were selected. Next, using the best MLP 1-8-1 network, the model was tested the given data. Using time series projection, a graph was illustrated and a table of predicted grain yield values for the coming years was tabulated. The absolute error showed the high accuracy of the obtained best MLP 1-8-1 network for forecasting the amount of grain crop yield. The obtained model of artificial network can be applied in research and monitoring of agricultural production development.

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Intelligent Constellation Generation based on Autoencoder Communication System

Intelligent Constellation Generation based on Autoencoder Co...

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computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Kaisei Matsumoto Takao Toma Shiho Oshiro Tomohisa Wada Computer Science and Intelligent Systems Program University of the Ryukyus Okinawa Japan Magna Design Net Inc Okinawa Japan Information Technology Center University of the Ryukyus Okinawa Japan Dept. of Engineering Area of Computer Science and Intelligent Systems University of the Ryukyus Okinawa Japan

This paper discusses intelligent constellation generation based on autoencoder communication system. In previous studies, the amplitude was set to fluctuate between r=0.0 and 1.0. However, when checking the generated constellation, distortion was confirmed instead of the conventional symbol arrangement. Therefore, in this paper, it compares the case where the amplitude is constant, the case where the average amplitude within a Minibatch is 1, and the case where the average amplitude is 1 for Interval time. The communication standard used in this research is IEEE 802.11a, assuming wireless Local Area network (LAN) specifications. The IEEE 802.11a standard has an Fast Fourier Transform (FFT) length of 64, a subcarrier number of 52, and Quadrature Phase Shift Keying (QPSK) and 16 Quadrature Amplitude Modulation (QAM), modulation methods. A guard interval of 800 ns is added and the symbol length is 4000 ns. First, a simulation was performed under the condition that the amplitude was kept constant. QPSK with 4 symbols, constant amplitude model is rounded more than previous research result. 16QAM with 16 symbols is arranged regularly like lined up on a line. Second, the simulation was performed under the condition that the average amplitude within the minibatch was set to 1. QPSK with 4 symbols, appears to rotate clockwise. 16QAM with 16 symbols has a more uniform symbol placement than previous research result. Third, a simulation was performed under the condition that the average amplitude within Interval time was set to 1. QPSK with 4 symbols, is the closest to square among QPSK output results so far. The direction is slightly tilted, but if it can be rotated a little more, it may be possible to reproduce the same symbol arrangement as before. 16QAM with 16 symbols, the symbol arrangement is biased as a whole. However, it can be seen that are arranged in line on the line, perhaps due to regularity. As future work, in addition to the conditions set this time, it will exa

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Real-Time IoT Cybersecurity using Machine Learning-based AI Threat Detection System to Train Generative Robots

Real-Time IoT Cybersecurity using Machine Learning-based AI ...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： P Srilakshmi Koushik Reddy Chaganti Talachendri Suryam Sweety Julia I D. Chaithanya Jami Kavitha Department of CSE G Pulla Reddy Engineering College Dept. of Computer and Information Systems University of the Cumberlands Department of CSE (DATA SCIENCE) Vignan Institute of Technology and Science Department of AI&ML Malla Reddy University Department of CSE CVR College of Engineering Department of CSE Sanketika Vidya Parishad Engineering College

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

Existing security protocols encounter major cybersecurity difficulties because the online devices' growing popularity continues to spread across networks. These security protocols are ineffective because they do not properly handle current cyber threats. The main goal of this study involves developing enhanced IoT cybersecurity through the development of a threat detection system which brings together adversarial training and deep learning models (CNN-LSTM) and Federated Learning (FL). The system enables distributed Internet of Things devices to work on security model development through Federated Learning while maintaining total privacy of their information. security procedures controlled by generative artificial intelligence robots alongside real-time attack protection functions decrease security response durations. Through its Federated CNN-LSTM model the system upholds a 1.2% false positive rate alongside a 98.3% accuracy evaluation and 160 milliseconds of exact threat tracking time. The designed system sustains a minimal occurrence of incorrect alarm activations. The developed system provides real-time security for the Internet of Things framework because it enables adaptive protection systems while preserving user privacy in current IoT settings.

关键词： Training Privacy Protocols Federated learning Real-time systems Threat assessment Internet of Things computer security Protection Robots

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