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ACM Journal on Autonomous Transportation Systems 2024年第1期1卷 1-8页

作者： Sakib Mahmud Khan M Sabbir Salek Vareva Harris Gurcan Comert Eric A. Morris Mashrur Chowdhury Intelligent Transportation Systems Lead MITRE Corporation McLean VA Ph.D. Candidate Glenn Department of Civil Engineering Clemson University Clemson SC Vice President for Corporate Engagement O Analytics Incorporated Fairmont WV Associate Professor Computer Science Physics and Engineering Department Benedict College Columbia SC USA Professor Nieri Department of Construction Development and Planning Clemson University Clemson SC USA Professor Glenn Department of Civil Engineering Clemson University Clemson SC USA

The traditional build-and-expand approach is not a viable solution to keep roadway traffic rolling safely, so technological solutions, such as autonomous vehicles (AVs), are favored. AVs have considerable potential to increase the carrying capacity of roads, ameliorate the chore of driving, improve safety, provide mobility for those who cannot drive, and help the environment. However, they also raise concerns over whether they are socially responsible, taking into account issues such as fairness, equity, and transparency. Regulatory bodies have focused on AV safety, cybersecurity, privacy, and legal liability issues, but have failed to adequately address social responsibility. Thus, existing AV developers do not have to embed social responsibility factors in their proprietary technology. Adverse bias may therefore occur in the development and deployment of AV technology. For instance, an artificial intelligence-based pedestrian detection application used in an AV may, in limited lighting conditions, be biased to detect pedestrians who belong to a particular racial demographic more efficiently compared to pedestrians from other racial demographics. Also, AV technologies tend to be costly, with a unique hardware and software setup which may be beyond the reach of lower-income people. In addition, data generated by AVs about their users may be misused by third parties such as corporations, criminals, or even foreign governments. AVs promise to dramatically impact labor markets, as many jobs that involve driving will be made redundant. We argue that the academic institutions, industry firms, and government agencies overseeing AV development and deployment must act proactively to ensure that AVs serve all and do not increase the digital divide in our society.

关键词： Autonomous vehicles Social responsibility Fairness Equity Transparency

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Resilient Framework for Next-Generation Heterogeneous Wireless Networks in Disaster Management

Resilient Framework for Next-Generation Heterogeneous Wirele...

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Sustainable Expert Systems (ICSES), International Conference on

作者： Ajisha Mathias J. Relin Francis Raj S R Raja R. Santhana Krishnan S. Balamurugan G.Vinoth Rajkumar Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Associate professor Master of Computer Applications Hindustan Institute of Technology and Science Chennai India Associate Professor Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Mathematics Velammal College of Engineering and Technology Madurai India Department of Electronics and Communication Engineering J.P. College of Engineering Tenkasi India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

The growing occurrence and concentration of natural and artificial disasters necessitate the development of robust communication systems capable of maintaining reliable and efficient connectivity under adverse conditions. Heterogeneous wireless networks (HSNs) have emerged as a promising solution, offering the flexibility to integrate various wireless technologies and adapt to changing environments. However, the inherent complexity and dynamic nature of HSNs present significant challenges in ensuring resilience and reliability, particularly in disaster scenarios where network infrastructure may be severely compromised. This research investigates a Resilient Framework for Next Generation Heterogeneous Wireless Networks in Disaster Management, focusing on enhancing communication reliability and efficiency during critical scenarios. The study introduces Network Coding Enhanced COPE (NC-COPE), a novel algorithm integrating network coding principles into the COPE framework. Simulation analysis compares NC-COPE with existing algorithms: Optimized Link State Routing (OLSR), Coding-Aware Routing (CAR), and Dynamic Source Routing (DSR) using metrics such as Packet Delivery Ratio (PDR), Routing Overhead, and Resilience to Node Failures. NC-COPE demonstrates superior performance in maintaining high PDR, reducing routing overhead, and enhancing network resilience under node failures. The findings underscore NC-COPE's potential to mitigate communication challenges in disaster management scenarios, highlighting its role in future wireless network deployments.

关键词： Wireless networks Heuristic algorithms Disasters Focusing Disaster management Network coding Routing Automobiles Next generation networking Resilience

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An investigation on feature and text extraction from images using image recognition in Android

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Materials Today: Proceedings 2022年 51卷 798-802页

作者： Brijeshkumar Y. Panchal Gaurang Chauhan Sandipkumar R. Panchal Urvashi M. Chaudhari Department of Computer Science and Engineering DEPSTAR CHARUSAT Charotar University of Science and Technology (CHARUSAT) CHARUSAT campus Changa 388421 India ITsouls Vadodara India Associate Professor & I/C Principal Knowledge Institute of Technology & Engineering Gujarat India Department of Computer Engineering Sardar Patel College of Engineering Bakrol-Anand Gujarat India

In today’s era, there is a huge rise in demand for the data and information available in a printed medium. To resume time, cost, and energy, we have been developing numerous technology. One of them, convert JPG, JPEG, or PDF softcopy into text, this process calls Text extraction. Identification of characters and detection of words and texts is again an upfront challenging task. This paper is focused on an investigation of feature and text extraction from images using image recognition in Android. In this era of Research and Technology, Image processing has become one of the most critical fields. Using a number of researchers' papers, we are going to study appropriate methods or algorithm, their accuracy, a key feature of each paper. All research papers going to study in the context of Android technology.

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Employing a Deep Neural Network to Identify and Categorize Leaf Diseases in Tomato Plant in Their Early Stages

Employing a Deep Neural Network to Identify and Categorize L...

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International Conference on Advances in Information Technology (ICAIT)

作者： Chandradeep Bhatt Parveen Kumar Saini Vikash Sawan Sanjay Sharma Teekam Singh Taranath N L Department of CSE Graphic Era Hill University Dehradun Uttarakhand India Department of CSE Chandigarh University Gharuan Mohali Punjab Department of Computer Engineering & Applications GLA University Mathura Uttar Pradesh Computer Application Department SGRR Univeristy Dehradun India Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Associate Professor School of CSE Presidency University Rajanukunte Bengaluru India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Tomato production makes a good contribution to the world economy as an element of agriculture. Tomato plants are vulnerable to different types of diseases that might create a negative impact on the production of the Tomato yield. Identification of this type of vulnerable disease at its early stage is important. This research work proposed a 9-layer Deep Neural Network to identify the leaf disease in the tomato plant. Deep learning technology of neural network approach was used in this method to classify the different leaf diseases in the tomato plant. The performance accuracy was obtained at 97.32% through the proposed method.

关键词： Accuracy Artificial neural networks Production Transforms Pressing Agriculture Real-time systems Safety Diseases Testing

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Intelligent Analysis of Big Data in the Information Flow

Intelligent Analysis of Big Data in the Information Flow

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Information Computing and Processing (ICP), Seminar on

作者： Petr V. Chetyrbok Marina A. Shostak Candidate of Science Engineering and Associate Professor at the Department of Computer Studies and Information Technologies V.I. Vernadsky Crimean Federal University Simferopol Russia Graduate student at the Faculty of Tourism and Service Sochi State University Sochi Russia

The article considers the use of AI methods for creation of a knowledge base in the field of recreation using a national interactive map of local cultural trends. The map contains thousands of brands classified into 9...

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AI-Based Cybersecurity Frameworks for 7G-Enabled Virtual Therapy Platforms

Cyber Security and Applications

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Cyber Security and Applications 2025年

作者： A. Jayanthiladevi Jayapandian Natarajan KP Arjun L. Godlin Atlas M. Arvindhan Daniel Arockiam Professor Department of Computer Engineering Marwadi University Rajkot Gujarat India Associate Professor Department of Computer Science and Engineering School of Engineering and Technology Christ University Kengeri Campus Bangalore Karnataka India Department of Computer Science and Engineering GITAM University Bengaluru Campus Karnataka India Associate Professor Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai Tamil Nadu Assistant Professor Amity School of Engineering & Technology Computer Science and Engineering Gwalior Madhya Pradesh India Associate Professor CSE Amity University Madhya Pradesh Gwalior 474005 India

The modern technology network plays an important role. Artificial intelligence to provide a higher solution for network security. As 7G networks emerge, virtual therapy platforms have begun to take center stage, offering fast, real-time interaction and high-definition immersive experiences. While the convergence of security in health care and mobility is in many ways even more integrated into our lives, it is fraught with cybersecurity risks. This article proposes an AI-based cybersecurity framework for a 7 G-based virtual therapy platform. The framework employs advanced machine learning (ML) algorithms, predictive analytics, and adaptive threat intelligence to protect the confidentiality, integrity, and availability of data. The article covers the core building blocks like anomaly detection, secure 7 G-based virtual therapy platform authentication protocols, and privacy-preserving techniques. AI-GN gives an overview of attack mitigation using AI with a comparative analysis of existing 5G/6G frameworks and solutions and their unique aspects needed for 7G environments. This article mainly focuses on threat detection using a deep learning method, also it aims to provide a continuous authentication process. The proposed model response time is decreased by nearly 30 percent, at the same time detection rate is increased to 98.5 percent in a 7G network.

关键词： AI-based cyber security framework Adaptive threat intelligence Anomaly detection Predictive analytics Threat detection

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IoT based Power and Water Theft Detection in Urban Cities

IoT based Power and Water Theft Detection in Urban Cities

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： Praful V. Nandankar G.N.R. Prasad M. Lakshminarayana Mr. Doddabasappa N G. Satish Ramya Maranan Assistant Professor Department of Electrical Engineering Government College of Nagpur Maharashtra India Sr. Asst. Professor Department of Master of Computer Application Chaitanya Bharathi Institute of Technology (A) Hyderabad India Assistant Professor Department of Medical Electronics Engineering M S Ramaiah Institute of Technology Karnataka India Assistant Professor School of Electrical and Electronic Engineering REVA University Bengaluru Karnataka India Associate Professor Department of Electrical and Electronics Engineering Sree Dattha Institute of Engineering and Science Sheriguda Ibrahimpatnam Hyderabad Telangana India Assistant Professor Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai India

Many people in metropolitan areas have experienced problems with energy and water shortages. Some people used modern technologies and advancements to theft water and power illegally. Because the planet absolutely needs both water and power. Currently, the Internet of Things (IoT), smart meters, smart pipelines, and smart homes are being heavily integrated to replace traditional systems for detecting theft activities. This can be digitizing both the meter readings and the data that has been gathered. Wireless technology may be used to collect and analyze the data, which helps to reduce the need for physical labor. In this project, we suggested creating an IoT-based system for water and power theft detection. The proposed project aims to design a system to monitor the amount of power and water used by the people and indicates if water and electricity theft activities happen. This research study offers a user interface system to help people to monitor whether anyone is stealing their water or power. This study employs a hardware like voltage sensors, current sensors, flow sensors, and additionally, the Arduino IoT cloud to enable remote control and monitor the water and power usage.

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A Novel Forward Filter Feature Selection Algorithm Based on Maximum Dual Interaction and Maximum Feature Relevance(MDIMFR) for Machine Learning

A Novel Forward Filter Feature Selection Algorithm Based on ...

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International Conference on Advanced computer Control, ICACC

作者： M A Anitha K K Sherly Assistant Professor of Computer Science College of Engineering Cherthala Kerala India Associate Professor of Information Technology Rajagiri School of Engineering & Technology Kerala India

ISBN: (纸本)9781665439206

In the last few decades, Feature selection is one of the most challenging and open problem to researchers. The rapid progress in computational techniques causes the generation and recording of data in huge size. Though there exists various feature ranking methods, the processing of data is still a challenging task due to its computational complexity. The filter method has many advantages over the wrapper method. The filter methods are classifier independent and have better computational efficiency. Here, a subset of features is selected based on a certain goal function. Most of these goal functions employs the principle of information theory. Most of the algorithms in earlier studies addressed two factors, that is, maximization of relevancy and minimization of redundancy without considering the interaction among the features. This paper developed a new forward filter feature selection algorithm based on mutual information known as Maximum Dual Interaction and Maximum Feature Relevance(MDIMFR). This method considers all the three factors: relevance, redundancy, and feature interaction. This method is experimented on three datasets and compares the performance with existing methods. The results show that MDIMFR outperforms the existing competitive feature selection methods of recent studies: mRMR, JMIM and CMIM. MDIMFR also achieves good stability in average classification accuracy for a certain number of features, say k and above. Hence, these k features can be considered as an optimal feature set.

关键词： Machine learning algorithms Redundancy Filtering algorithms Feature extraction Information filters Minimization Filtering theory

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Design, Modelling, and Analysis of Novel Solar PV System using MATLAB

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Materials Today: Proceedings 2022年 51卷 756-763页

作者： Ayan Banik Anurag Shrivastava Ravindra Manohar Potdar Sanjiv Kumar Jain Santosh Gopal Nagpure Mukesh Soni National Institute of Technical Teachers' Training & Research Kolkata India Principal and Professor ECE Lakshmi Narain College of Technology and Science Indore India Associate Professor Department of E&TC Engineering Bhilai Institute of Technology Chhattisgarh India Assistant Professor Department of Electrical Engineering Medi-Caps University Indore India Assistant Professor Department of E&TC Engineering D. Y. Patil School of Engineering Academy Pune India Research Scholar Department of Computer Science and Engineering Jagran Lakecity University Bhopal India

In order to meet the world's future energy demand, alternative renewable green energy supplies must be widely promoted. Among all DERs, the most prevalent is Solar PV for a number of important reasons. In this study, the authors analyze several elements of the Single-Phase Solar Photovoltaic Rooftop System with MATLAB. In order to analyze the characteristics of the output current, a new grid synchronization methodology has been devised. For maximum power tracking, the INC control technique has been adopted. The output of the solar DC power system has been synchronized with the grid through hysteresis current loop control. Extensive testing verifies the practicality of the proposed power grid model and promising outcomes and results.

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Unsupervised Single Valued Neutrosophic Sets Approach for Cloud Clustering

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Procedia computer science 2025年 258卷 3580-3589页

作者： Haewon Byeon Pramod Kumar Ihtiram Raza Khan Faisal Yousef Alghayadh Maher Ali Rusho Mukesh Soni Inje University Medical Big Data Research Center Gimhae 50834 Republic of Korea Associate professor Ganga Institute of Technology and Management Jhajjar - Bahadurgarh Rd Kablana Bhadana Haryana 124104 Computer science department Jamia Hamdard Delhi Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Lockheed Martin Engineering Management University of Colorado Boulder Boulder Colorado Dr. D.Y. Patil Vidyapeeth Pune Dr. D.Y. Patil School of Science & Technology Tathawade Pune

This work presents an innovative approach to integrating restricted rationality and strategic manipulation into multi-attribute decision-making in product style design, resulting in more precise and objective outcomes. The suggested approach employs cloud model clustering and unsupervised Single-Valued Neutrosophic Sets (SVNS). SVNS is derived by constructing pairwise comparison ratio square matrices between baseline attributes and prospective alternatives, following the scientific clustering of decision experts. SSVNC denotes the degrees of real, false, and questionable membership. The relative weights of the standard attributes are established by analysing the evaluation outcomes of different solutions for each common trait. Cloud model clustering is employed to exclude conflicting and unreasonable experts, hence attaining precise weights for design judgements. This model incorporates multi-granularity linguistic factors. The integration of the significance of generic attributes and human assessment yields a composite score for each prospective response. The feasibility and efficacy of the proposed strategy are demonstrated through a case study in automotive design aesthetics. The results emphasise the method’s capability to safeguard against nefarious strategic manipulation and effectively address complex and confusing multi-attribute decision-making challenges in vehicle stylistic design.

关键词： Multi-attribute decision-making Bounded rationality Strategy manipulation unsupervised Single-Valued Neutrosophic Sets Cloud model clustering

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