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6th International Conference on Intelligent Communication, Control and Devices, ICICCD 2024

作者： Supriya Shukla, Ashutosh Manchanda, Mahesh Department of AI and ML COER University Roorkee247667 India Electrical Cluster School of Engineering UPES Dehradun India Department of Computer Science and Engineering GEHU University Dehradun India

ISBN: (纸本)9789819783281

India’s reliance on fossil fuels to meet its energy generation demands has a negative impact on both the environment and public health. In the present energy transition, photovoltaic (PV) technologies have become more widely accepted for commercial use, achieved significant progress in technology, and have a major role to play in reducing the harmful environmental effects of fossil fuel-based power generation. Soiling research on solar PV has notably increased in recent years due to its critical role in reducing the performance of solar PV cells. Therefore, to guarantee the amount of power yield, efficient monitoring of the soiling is crucial. It is difficult to manually analyse soiling severity on the panel images due to the non-uniformity of the type and amount of soiling faults. Deep learning models have been successfully applied to the analysis of solar panel images and soiling categorization;nevertheless, this approach may require a significant amount of training time and may lead to gradient descent. To tackle these training issues, we propose a comparative study of two pre-trained neural network architectures, namely, MobileNetV3small and RestNet50, on a publicly available dataset of 875 images of six solar PV fault classes, namely, clean, dust, bird-drop, electrical damage, physical damage, and snow-covered. The experimental results show that MobileNetV3small achieved a better performance of 98%. The results show that MobileNetV3small obtained a promising result of 11% higher classification accuracy than ResNet50, with an accuracy of 86.6%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electric losses

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MMInstruct: a high-quality multi-modal instruction tuning dataset with extensive diversity

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science China(Information sciences) 2024年第12期67卷 36-51页

作者： Yangzhou LIU Yue CAO Zhangwei GAO Weiyun WANG Zhe CHEN Wenhai WANG Hao TIAN Lewei LU Xizhou ZHU Tong LU Yu QIAO Jifeng Dai School of Computer Science Nanjing University School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai AI Laboratory School of Computer Science Fudan University Department of Information Engineering The Chinese University of Hong Kong SenseTime Research Department of Electronic Engineering Tsinghua University

Despite the effectiveness of vision-language supervised fine-tuning in enhancing the performance of vision large language models(VLLMs), existing visual instruction tuning datasets include the following limitations.(1) Instruction annotation quality: despite existing VLLMs exhibiting strong performance,instructions generated by those advanced VLLMs may still suffer from inaccuracies, such as hallucinations.(2) Instructions and image diversity: the limited range of instruction types and the lack of diversity in image data may impact the model's ability to generate diversified and closer to real-world scenarios outputs. To address these challenges, we construct a high-quality, diverse visual instruction tuning dataset MMInstruct,which consists of 973k instructions from 24 domains. There are four instruction types: judgment, multiplechoice, long visual question answering, and short visual question answering. To construct MMInstruct, we propose an instruction generation data engine that leverages GPT-4V, GPT-3.5, and manual correction. Our instruction generation engine enables semi-automatic, low-cost, and multi-domain instruction generation at 1/6 the cost of manual construction. Through extensive experiment validation and ablation experiments,we demonstrate that MMInstruct could significantly improve the performance of VLLMs, e.g., the model fine-tuning on MMInstruct achieves new state-of-the-art performance on 10 out of 12 benchmarks. The code and data shall be available at https://***/yuecao0119/MMInstruct.

关键词： instruction tuning multi-modal multi-domain dataset vision large language model

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Systematic Security Guideline Framework through Intelligently Automated Vulnerability Analysis

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computers, Materials & Continua 2024年第3期78卷 3867-3889页

作者： Dahyeon Kim Namgi Kim Junho Ahn Computer Information Technology Korea National University of TransportationChungju27469Korea Department of AI Computer Science and Engineering Kyonggi UniversitySuwon16227Korea

This research aims to propose a practical framework designed for the automatic analysis of a product’s comprehensive functionality and security vulnerabilities,generating applicable guidelines based on real-world *** existing analysis of software security vulnerabilities often focuses on specific features or *** partial and arbitrary analysis of the security vulnerabilities makes it challenging to comprehend the overall security vulnerabilities of the *** key novelty lies in overcoming the constraints of partial *** proposed framework utilizes data from various sources to create a comprehensive functionality profile,facilitating the derivation of real-world security *** guidelines are dynamically generated by associating functional security vulnerabilities with the latest Common Vulnerabilities and Exposure(CVE)and Common Vulnerability Scoring System(CVSS)scores,resulting in automated guidelines tailored to each *** guidelines are not only practical but also applicable in real-world software,allowing for prioritized security *** proposed framework is applied to virtual private network(VPN)software,wherein a validated Level 2 data flow diagram is generated using the Spoofing,Tampering,Repudiation,Information Disclosure,Denial of Service,and Elevation of privilege(STRIDE)technique with references to various papers and examples from related *** analysis resulted in the identification of a total of 121 *** successful implementation and validation demonstrate the framework’s efficacy in generating customized guidelines for entire systems,subsystems,and selected modules.

关键词： Framework automation vulnerability analysis security guidelines

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Context-Aware Summarization for PDF Documents using Large Language Models 4

Context-Aware Summarization for PDF Documents using Large La...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Ramprasad, Akshara Sivakumar, P. School of Computer Science and Engineering Vellore Institute of Technology B. Tech Computer Science with specialization in AI & ML Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798350385793

In an era characterized by the overflow of textual information, the demand for effective text summarization techniques has become increasingly evident. This research study presents a novel solution to address this demand: the development of a PDF Summarizer Generative ai Platform for context-aware text and document summarization. The developed platform empowers users to generate informative summaries from lengthy textual documents. It features an interface powered by Gradio for enabling user-friendly accessibility. Equipped with essential components such as Openai's language models for text comprehension, Chroma vector enables document embedding and conversational retrieval chains for contextual understanding, where the platform offers a comprehensive solution. Users can effortlessly upload documents in various formats, including PDFs, and interactively engage with the system to obtain concise summaries tailored to their specific requirements. © 2024 IEEE.

关键词： Chroma Database (ChromaDB) Gradio LangChain Large Language Models (LLM) Openai

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Drone Detection and Tracking using Deep Convolutional Neural Networks from Real-time CCTV Footage

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IEIE Transactions on Smart Processing and Computing 2024年第4期13卷 313-321页

作者： Allmamun, Md Akter, Fahima Talukdar, Muhammad Borhan Uddin Chakraborty, Sovon Uddin, Jia Department of Computer Science and Engineering European University of Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science and Engineering University of Liberal Arts Bangladesh Dhaka Bangladesh AI and Big Data Department Woosong University Daejeon Korea Republic of

Drones are flying objects that may be controlled remotely or programmed to do a wide range of tasks, including aerial photography, videography, surveys, crop and animal monitoring, search and rescue missions, package delivery, and military operations. Unrestrained use, however, can pose a significant threat to safety, privacy, and security through eavesdropping, flying close to prohibited locations, interfering with public events, and delivering illicit items. Hence, real-time drone detection and tracking are indispensable and appropriate measures. This study developed real-time drone detection and tracking using the most efficient deep-learning approaches. The models were fine-tuned first to suit the required purpose and yield the desired outcome. The performance of the developed system was better than that of earlier endeavors in terms of accuracy and loss. Of the seven fined-tuned models, the Xception model constantly rendered the maximum accuracy with negligible loss. The model outperformed other state-of-the-art architectures, exhibiting an accuracy and loss of 99.18% and 3.83, respectively. Copyrights © 2024 The Institute of Electronics and Information Engineers.

关键词： Aerial photography

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Guest Editorial: New Developments in Explainable and Interpretable Artificial Intelligence

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第4期5卷 1427-1428页

作者： Subbalakshmi, K. P. Suba Samek, Wojciech Hu, Xia Ben Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ07030 United States Fraunhofer HHI Einsteinufer AI Department Berlin10587 Germany ML & Communications Berlin10587 Germany Rice University Computer Science HoustonTX77005 United States

This special issue brings together seven articles that address different aspects of explainable and interpretable artificial intelligence (ai). Over the years, machine learning (ml) and ai models have posted strong performance across several tasks. This has sparked interest in deploying these methods in critical applications like health and finance. However, to be deployable in the field, ml and ai models must be trustworthy. Explainable and interpretable ai are two areas of research that have become increasingly important to ensure trustworthiness and hence deployability of advanced ai and ml methods. Interpretable ai are models that obey some domain-specific constraints so that they are better understandable by humans. In essence, they are not black-box models. On the other hand, explainable ai refers to models and methods that are typically used to explain another black-box model. © 2020 IEEE.

关键词： Artificial intelligence

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Analysis of Various Motion Detection and Facial Recognition Based Home Security Systems 2nd

Analysis of Various Motion Detection and Facial Recognition ...

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2nd International Conference on Deep sciences for Computing and Communications, IconDeepCom 2023

作者： Karthikeyan, P. Nidhi, Shashank Vishnu Kumar, S.R. Karunakaran, V. Velliangiri, S. Department of Computer Science and Information Engineering National Chung Cheng University Chiayi Taiwan Department of Computer Science and Engineering-AI and ML Jain Deemed to be University Bangalore India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur Chennai India

ISBN: (纸本)9783031689079

A combination of ai and IoT makes a great home security system. IoT collects data from the surroundings, while ai takes the data and makes predictions accordingly. In this case, these predictions help in creating alerts. CCTV cameras were traditionally used for home security systems, with attendees required to monitor the footage. Because of technological advancements, this process can be completed with the least amount of human intervention. Other work has been done in the field of home security systems that use facial recognition and motion detection. Those systems don’t exhibit good accuracy in detecting multiple human faces. This work focuses on a detailed survey of different facial recognition techniques along with motion detection techniques as per the requirements of a home security system. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Motion tracking

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A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

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computers, Materials & Continua 2024年第3期78卷 3089-3127页

作者： Navaneetha Krishnan Muthunambu Senthil Prabakaran Balasubramanian Prabhu Kavin Kishore Senthil Siruvangur Kavitha Chinnadurai Jehad Ali Department of Computer Applications Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Data Science and Business Systems College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyJeppiaar NagarRajiv Gandhi SalaiChennai600119India Department of AI Convergence Network Ajou UniversitySuwon16499Korea

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the ***,this development has expanded the potential targets that hackers might *** adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or *** identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious *** research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks(RNN)integrated with Long Short-Term Memory(LSTM)*** proposed model can identify various types of cyberattacks,including conventional and distinctive *** networks,a specific kind of feedforward neural networks,possess an intrinsic memory *** Neural Networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended *** such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual *** are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection *** model utilises Recurrent Neural Networks,specifically exploiting LSTM *** proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

关键词： Cybersecurity intrusion detection machine learning leveraging long short-term memory(LLSTM) CICIDS2019 dataset innovative cyberattacks

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Agricultural repercussions caused by the pandemic period

Agricultural repercussions caused by the pandemic period

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Sheeja Kumari, V. Seema Dev Aksatha, D. Michael, G. Vennira Selvi, G. Manikandan, T. Gururama Senthilvel, P. Department of Computational Intelligence – Institute of AI & ML SIMATS School of Engineering SIMATS University Chennai India Department of Computer Science Sri Krishna Arts and Science College Coimbatore India Department of Computer Science and Engineering SIMATS School of Engineering SIMATS University Chennai India

ISBN: (纸本)9781032669663

COVID-19 has affected India’s agriculture system. However, the latest quarterly GDP predictions post-COVID scenario show the resilience of the Indian agricultural sector, which is expected to be the sole sector to expand 4% for fiscal years. In this context, the major purpose is to assemble a summary of early evidence on COVID-19‘s influence on India’s agriculture industry, including production, marketing, and consumption, and then devise pragmatic measures to recover and thrive after the pandemic. The epidemic has disrupted production and marketing through labour and logistical restrictions, while the negative income shock has restricted access to markets and increased food commodity costs, changing consumption patterns, the poll found. India’s agriculture business was devastated by the outbreak. The crisis has allowed the state to launch many programmes and make important changes. After COVID-19, coordinated effort is needed to break the global stalemate. This review study shows how the COVID-19 epidemic has damaged global. © 2024, CRC Press/Balkema. All rights reserved.

关键词： COVID-19

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A novel WO-ANT: whale-ant optimization algorithm for detection of Parkinson’s disease

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1873-1881页

作者： Singh, Neha Sinha, Sapna Singh, Laxman Amity Institute of Information Technology Amity University Uttar Pradesh Noida India Department of Computer Science and Engineering (AI-ML) KIET Group of Institutions U.P Ghaziabad India

Parkinson’s disease (PD) is a neurological condition that impacts the quality of life for millions of people all over the world. A prompt and precise diagnosis of PD is absolutely necessary for the successful treatment and management of the condition. For the detection of PD, numerous methods exist in the literature, including speech-based methods, gait-based methods, and handwriting-based methods. Compared to the other two methods, the speech-based method is regarded as the most effective and competitive. The Whale-Ant Optimization Algorithm (WO-ANT) is a novel swarm optimization strategy that is presented in this paper as a potential method for the detection of PD via feature selection. To help in early detection, the proposed method attempts to enhance classification accuracy of PD detection by selecting an optimum subset of features from a wide feature space. In this paper, authors evaluate the classification efficacy of WO-ANT for PD using a speech-based method. MATLAB is used to implement the proposed method on the PD dataset obtained from ***. The efficacy of the algorithm is evaluated based on the optimal subset of features, accuracy, and computational cost. WO-ANT accurately reduced the number of features from 23 to 4 giving an accuracy of 90.29%. According to the numerical results, the algorithm performed well in terms of accuracy and selection of the optimal number of features. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Ant colony optimization algorithm Feature selection Parkinson’s disease Whale optimization algorithm Whale-ant optimization algorithm

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