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Enhanced affinity propagation clustering with a modified extreme learning machine for segmentation and classification of hyperspectral imaging

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2024年 9卷

作者： Daniel, V. Antony Asir Vijayalakshmi, K. Pawar, Priyanka Pramod Kumar, Deepak Bhuvanesh, A. Christilda, A. Josephine Department of Electronics and Communication Engineering Loyola Institute of Technology and Science Tamil Nadu Kanyakumari India Department of Computer Science and Engineering Ramco Institute of Technology Tamil Nadu Rajapalayam India Department of Information Technology University of the Cumberlands Williamsburg United States Department of Electrical and Electronics Engineering PSN College of Engineering Technology Tamil Nadu Tirunelveli India Research Scholar Sathyabama Institute of Science and Technology Tamil Nadu Chennai India

Hyperspectral Imaging (HSI) plays a crucial role in detecting, identifying, and classifying a wide range of natural resources, including minerals, geological phenomena like volcanic eruptions, and vegetation. Segmentation and classification of HSI play vital roles in extracting meaningful information and identifying different land cover or land use categories within the scene. One of the primary limitations associated with HSI is the scarcity of labeled samples. Obtaining annotated samples is a laborious and time-consuming process, posing a significant challenge in the field. This work presents an Enhanced Affinity Propagation Clustering (EAPC) and Modified Extreme Learning Machine (MELM) for segmentation and classification of HSI. Initially, the HSI images are pre-processed by the non-linear diffusion partial differential equation. Then, the segmentation process is performed by the EAPC and it is the combination of Affinity Propagation Clustering (APC) with Light Spectrum Algorithm (LSA). Finally, the classification is performed by the MELM and the experimentation is demonstrated on the Salinas dataset and achieved better accuracy and sensitivity of 97.3 % and 98.2 % respectively. © 2024 The Author(s)

关键词： Classification Enhanced affinity propagation clustering Hyperspectral imaging Modified extreme learning machine

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Issues and Challenges Related to Cloud-Based Traffic Management System: A Brief Survey 1st

Issues and Challenges Related to Cloud-Based Traffic Managem...

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1st International Conference on Intelligent and Cloud Computing, ICICC 2019

作者： Mahapatra, Sarita Rath, Krishna Chandra Pattnaik, Srikanta Department of Computer Science and Information Technology Siksha ‘O’ Anusandhan Deemed to Be University Bhubaneswar Odhisha and Research Scholar Utkal University Bhubaneswar India P.G. Department of Geography Utkal University Bhubaneswar Bhubaneswar India Department of Computer Science and Engineering Siksha ’O’ Anusandhan Deemed To Be University Bhubaneswar India

ISBN: (纸本)9789811559709

In the present scenario, increasing traffic volume is a prime concern worldwide. Many urban areas suffer from acute traffic congestion due to various reasons like insufficient road capacity, etc. Congestions adversely affect major sectors like economy, environment, and health. Smart solutions to this timely topical problem may be provided by using Intelligent Transportation Systems (ITS) technologies. ITS urban traffic management mostly deals with the collection and processing of a huge amount of geographically distributed information to manage distributed infrastructure and components of road traffic. Due to the distributed aspect of the problem, there is a need to develop a novel, scalable Cloud-based ITS platform. The Cloud-based ITS hosts the collection of software services which leads to the development of the Cloud-based Traffic Management System (CTMS). In this paper, we did a survey on distinct aspects of CTMS such as adaptive intersection control algorithm integrated with microscopic prediction mechanism and microscopic traffic simulation tool tightly integrated with the ITS-cloud. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Surveys

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Optimizing Energy Efficiency in Electric Vehicles Through Fuzzy Logic and Neural Network Algorithms

Optimizing Energy Efficiency in Electric Vehicles Through Fu...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： M Devi Siva Satya Sreedhar P S. Geetha Priya M. Amutha D. Kanagajothi R.Kalaivani Sri Research Scholar Bharath Institute of Higher Education and Research Chennai Tamil Nadu India Department of Information Technology Seshadri Rao Gudlavalleru Engineering College Gudlavalleru Andhra Pradesh India Department of computer science and Engineering R. M. D. Engineering College Kavaraipettai Chennai India Department of ECE Sona College of Technology Salem Tamil Nadu India Department of Mathematics Vel Tech Rangarajan Dr. Sagunthala R& D Institute of Science and Technology Avadi Chennai India Department of computer science Arunai Engineering College Tiruvannamalai Tamil Nadu India

The introduction of electric vehicles (EVs) has represented a significant step forward in the development of environmentally sustainable transportation. The study addresses the critical need to enhance energy management systems in electric vehicles (EVs) by overcoming the limitations of traditional algorithms. Existing systems, which rely on rule-based controllers and basic predictive models, have poor flexibility and precision when it comes to dynamically optimizing energy efficiency. To revolutionize energy optimization in EVs, the proposed approach uses a hybrid model that combines fuzzy logic and neural networks. It tackles limits through a comprehensive analysis, the design of a fuzzy logic-based controller, and the development of a neural network model trained on historical data. TensorFlow-implemented simulation results in Python validate the reliability of the model by closely matching predicted energy usage in a range of driving scenarios. Real-world experiments with an instrumented EV prototype highlight the proposed system's advantages even further, giving noteworthy results—92% energy efficiency, 25 mile range extension, and 95% predicted accuracy. These findings support the system's ability to greatly improve energy efficiency and range extension in electric vehicles, therefore transforming sustainable transportation.

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S-COAP: Semantic Enrichment of COAP for Resource Discovery

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SN computer science 2020年第2期1卷 1-7页

作者： Vandana, C.P. Chikkamannur, Ajeet A. Visvesvaraya Technological University Research Scholar Department of Information Science Engineering New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering R. L. Jalappa Institute of Technology Doddaballapur Bangalore India

Internet of Things (IoT) paradigm is progressing at an enormous rate creating an expansive ecosystem of interconnected smart devices which needs to be controlled and managed by end applications or users. So, the solution is to discover the best device matching the user requirement in timely manner. Ensuring efficient and context aware resource discovery is a challenge in seamless operation of IoT applications. Semantic Intelligence can be built for resource discovery. In this paper, we propose semantic enrichment of COAP protocol to discover the resources. Resources are semantically modelled, and COAP protocol CORE Link format is modified to incorporate semantic information. Also, we have proposed a framework which is aware of the social relationship between the devices to accommodate distributed topologies. We conducted the experiments in COOJA simulator on CONTIKI OS with COAP protocol semantically enriched. COAP CORE Link format and Resource Directory were semantically enriched, and its effectiveness was measured. © 2020, Springer Nature Singapore Pte Ltd.

关键词： COAP CORE Link IoT Ontology RD Semantic Social

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The Design of AI Algorithms for DG Setting

The Design of AI Algorithms for DG Setting

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： N Ganeshkumar Purushottam Das Laith Hussein Dharmesh Dhabliya Wesam Abed Al Hassan Alhaidry Muhsin Jweeg Ahmed Al-Ansari Ali SaadAlwan Department of Computer Science and Engineering GITAM School of Technology Gandhi Institute of Technology and Management (GITAM) (Deemed to be University) Karnataka India Department of CSE Graphic Era Hill University Research Scholar Graphic Era Deemed to be University Dehradun Uttarakhand Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Information Technology Department Vishwakarma Institute of Information Technology Pune India College of Technical Engineering National University of Science and Technology DhiQar Iraq Al-Farahidi university Baghdad Iraq Dental Department Al-kunooze University College Basra Iraq Business Administration Department Kut University College Al kut wasit Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Artificial intelligence (AI) is the bedrock of management systems and will continue to support human society both now and in the future. These systems’ decision-making processes must be transparent, especially in sensitive areas such as autonomous driving and healthcare, to build trust. Although methods exist for achieving algorithmic transparency, there are no well-established design techniques to systematically address descriptive issues when reviewing AI. In response, we introduce the Explainability in Design (EID) approach, which operationalizes the definition of AI into six fundamental concepts. EID was refined through iterative empirical studies, including applied learning and interviews with practitioners. An empirical study with AI system designers tested the utility of EID, demonstrating its effectiveness in reducing barriers to integrating explanatory functionality in AI systems and enhancing decision-making processes.

关键词： Decision making Medical services Iterative methods Artificial intelligence Interviews Autonomous vehicles

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Enhancing IoT Ecosystems through Effective Master Data Management

Enhancing IoT Ecosystems through Effective Master Data Manag...

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International Conference on Signal Processing and Integrated Networks (SPIN)

作者： Ronak Ravjibhai Pansara Balaram Yadav Kasula Piyush Kumar Gupta Tabrej Ahamad Khan Naved Alam Pawan Whig Master Data Specialist Tesla USA Dept. of Information Technology University of The Cumberlands Williamsburg KY USA Department of computer science and engineering Jamia hamdard New Delhi India Research Scholar Jamia Millia Islamia New Delhi India Whig Research Scientist Threws VIPS-TC New Delhi India

This study explores the quantitative impact of Master Data Management (MDM) implementation on Internet of Things (IoT) ecosystems. Employing a comparative analysis methodology, pre-and post-MDM integration metrics were assessed within the IoT environment. Results showcased a notable enhancement in data accuracy by 20%, attributed to minimized errors and improved consistency following MDM adoption. Operational efficiency exhibited a significant 30% reduction in data integration time across diverse IoT platforms, contributing to streamlined processes. Financially, operational costs decreased by 15%, primarily due to reduced data cleansing efforts post-MDM implementation. Decision-making processes benefited from a 25% faster response time to real-time analytics derived from IoT data insights. Additionally, the IoT ecosystem’s scalability increased by 40%, indicating amplified adaptability to accommodate higher data volumes without compromising performance. These quantitative findings highlight the substantial impact of MDM on fortifying the reliability, efficiency, and scalability of IoT ecosystems, providing empirical evidence for the transformative potential of MDM in optimizing IoT applications.

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Internet of things: present state of the art, applications, protocols and enabling technologies

Internet of things: present state of the art, applications, ...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Jayashree C. Pasalkar Kamaljeet Kaur NVLM Krishna Munagala N. Sharmila Banu Ali Hussein Malik Bader Alazzam Department of Information Technology AISSMS Institute of Information Technology Research Scholar in AISSMS COE Chandigarh University Mohali Department of Electrical Electronics and Communication Engineering GITAM Deemed University Visakhapatnam Andhra Pradesh School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Medical technical college Al-Farahidi University Baghdad Iraq Information Technology College Ajloun National University Jordan

The Internet of Things (IoT) has transformed into a dynamic paradigm that connects a wide range of systems and devices in many fields. The current status of IoT is thoroughly examined in this research study, including its applications, communication protocols, supporting technologies, including prospects for the future. Researchers methodically examine how the Internet of Things (IoT) has an influence on many businesses, and social issues, including daily life using Jupyter Notebook for data analysis. In the Jupyter Notebook environment, the study highlights the value of structured data pretreatment, exploratory data analysis, and statistical inference, including visual depiction of findings. The future of IoT research is in the integration of developing technologies, better interoperability, and increased security methods.

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The Various Techniques in Mmcn: An Overiview

The Various Techniques in Mmcn: An Overiview

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Manisha Aeri Kadim A. Jabbar Huthaifa Alani Myasar Mundher Adnan Dharmesh Dhabliya Imad Al-Din Hussein Al-Saidi Nabaa Kareem Taaban Department of CSE Graphic Era Hill University Research Scholar Graphic Era Deemed to be University Dehradun Uttarakhand College of Technical Engineering National University of Science and Technology Dhi Qar Iraq Al-Farahidi university Baghdad Iraq Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Vishwakarma Institute of Information Technology Pune India Computer Engineer Department Al-kunooze University College Basra Iraq Business Administration Department Al kut Kut University College wasit Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Molecular communications provide a fresh approach to deciphering complex biological systems. The topic of mobile molecular communication networks is examined in this article, with a focus on calcium signaling within a population of mobile cells. Taking inspiration from conventional communication networks, we investigate multi-modality behavior using a multimedia method. We initially utilize a random walk model to study the stochastic mobility of biological cells and the dynamic features of cellular networks. Next, a mathematical model is proposed to explain how calcium signals arise and propagate throughout mobile networks. We further provide a thematic framework and expand our study to a multimedia context. Using this approach, we study two forms of calcium signaling transmission: waveform transmission and concentration transmission. To further explain our findings, we use simulations to show the patterning of calcium signal waveforms. The results show that the surroundings have an impact on performance. Additionally, the simulation shows performance differences between the two transmission methods, confirming the features that our proposed multimedia theme has.

关键词： Cellular networks Calcium Sociology Molecular communication Stochastic processes Mathematical models Data models

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ADVANCED ATTACK MITIGATION IN IOT GATEWAY PROTOCOLS

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computers & Security 2025年 157卷

作者： K. Praveen Kumar Dr. N. Suresh Kumar Assistant Professor Department of Information Technology Kakatiya Institute of Technology and Science Warangal (KITSW) Telangana India Research Scholar Department of Computer Science and Engineering GITAM University Visakhapatnam Andhra Pradesh India Associate Professor Department of Computer Science Engineering GITAM Institute of Technology GITAM (Deemed to be University) Visakhapatnam India

With the increasing number of users on the internet, numerous cyberattacks are becoming more and more common. Proper detection of these attacks by Intrusion Detection Systems (IDS) is extremely important, particularly for IoT networks. Deep learning methods have proved to be very promising for enhancing IDS performance. This paper presents an end-to-end system for attack detection and prevention in IoT networks with the use of data augmentation, preprocessing, feature extraction, and deep machine learning algorithms. The class imbalance is resolved using the Enhanced Synthetic Minority Over-Sampling Technique (ESMOTE), and preprocessing operations normalize and clean the data for improved model performance. Feature extraction involves statistical features and Shannon entropy-based features, which are fused and sent through a feature selection process. A new 2D-LICM hyper-chaotic map combined with Walrus Optimization (2D-LICMHy-CM_WO) is used to enhance feature selection through enhanced search diversity, convergence rate, and eliminating redundancy. The Dense Convolutional Spatial Attention-based Enhanced Bi-GRU (DCSAtten_EBi-GRU) effectively extracts attack pattern dependencies for precise detection, and an Enhanced Double Deep Q-Learning Network (DoubleDQN) offers dynamic adaptive real-time countermeasures. Experimental findings prove that the proposed solution can obtain a 99.6% detection accuracy with an F1-score of 0.98 and outperforms current IDS models in false positive rate and detection time.

关键词： 2D-LICM hyper-chaotic map with Walrus Optimization (2D-LICMHy-CM_WO) Dense Convolutional Spatial Attention-based Enhanced Bi-GRU (DCSAtten_EBi-GRU) Double Deep Q-Learning Network (DoubleDQN) Enhanced Synthetic Minority Over-Sampling Technique (ESMOTE)

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Learning analytics using deep learning techniques for efficiently managing educational institutes

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Materials Today: Proceedings 2022年 51卷 2317-2320页

作者： Ravi Kishore Veluri Indrajit Patra Mohd Naved Veduri Veera Prasad Myla M. Arcinas Shehab Mohamed Beram Abhishek Raghuvanshi Aditya Engineering College(A) Surampalem India An Independent Researcher PhD from NIT Durgapur West Bengal India Department of Business Analytics Jagannath University Delhi-NCR India Associate Professor Behavioral Science Department De La Salle University Philippines Research Scholar Department of Computing and Information Systems Sunway University Malaysia Department of Computer Engineering Mahakal Institute of Technology Ujjain India

Increasing numbers of higher education institutions see themselves as service providers, catering primarily to the needs of its students. The improvement of student performance is a top priority for universities. It is critical to first assess the present situation of the students before designing a program to improve their performance. Higher education administrators face a significant problem in predicting a student's future success. The goal of this study is to learn what factors influence college students' decision on a major. It will be possible to forecast students' behavior, attitudes, and performance with the use of predictive tools and procedures. Predicting student performance ahead of time makes it possible to take proactive measures to raise achievement levels. To obtain a high education standard, several attempts have been made to forecast student performance. However the accuracy of these predictions falls short of the desired level of excellence. Machine learning approaches including Artificial Neural Network, Nave Bayes, and SVM are being studied. A University Data Set from UCI Machinery is used in the experimental investigation.

关键词： Learning analytics Deep learning Educational data mining Machine learning Student performance Classification Prediction

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