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IEEE International Conference on Signal Processing, Computing and Control (ISPCC)

作者： Bubun Kumar Mohanta Kalyandurg Rafeeq Ahmed Abhiraj Malhotra Godwin Premi M.S R Suchithra Baishakhi Debnath Department of CSE Siksha 'O' Anusandhan (Deemed to be University) Bhubaneswar Odisha India Department of ECE Presidency University Bangalore Karnataka India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of ECE Sathyabama Institute of Science and Technology Chennai TamilNadu India Department of Computer Applications (DCA) Presidency College Bengaluru India School of Mangement - UG Management JAIN (Deemed to be University) Bangalore Karnataka

ISBN: (数字)9798331538934

ISBN: (纸本)9798331538941

This paper presents a framework of deploying Internet of Things (IoT), industrial Internet of Things (IIoT) applications such that QoS is taken into account and resource consumption and latency are optimized. The architecture is built on containers and makes intensive use of data analysis in order to forecast workload trends and to dynamically allocate services to fog nodes. One can make ideal judgments about service placement as function of delay, energy, and pricing. The framework achieves this by the dynamic relocation of services to forestall system failure when fog nodes fail or performance falls. Through the use of real world Internet of Things datasets, we have demonstrated that the strategy outperforms standard placement strategies with respect to response and resource efficiency. Thus the approach has been provided which can benefit a number of intelligent applications, such as, city planning, healthcare, and manufacturing. The research also contributes to the development of fog calculating for Internet of Things using Internet of Things ecosystems, as well as, supporting intelligent and scalable solutions for applications of next generation.

关键词： Data analysis Urban planning Signal processing algorithms Quality of service Containers Delays Optimization Standards Industrial Internet of Things Edge computing

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Resilience test case automation for LTE femtocell networks 5

Resilience test case automation for LTE femtocell networks

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5th International Conference on Intelligent Computing and Control Systems, ICICCS 2021

作者： Sudarsanam, P. Anand, R. Banerjee, Sumanta Hemanth, R. BMS Institute of Technology and Management Bangalore Department of Master of Computer Applications Bengaluru India CMR Institute of Technology Bengaluru Department of Information Science and Engineering Bengaluru India Nokia Solutions and Network SQUAD LEAD Bengaluru India BMS Institute of Technology and Management Bangalore Department of MCA Bengaluru India

ISBN: (纸本)9781665412728

The automated test case generation includes the manual test cases, which are converted to automated test cases through executable test scripts. It is more essential to bring out an effective resilience testing for network gateways. The automation testing plays a major role in performing time constrained and skillful work. Moreover, the network gateways will not take part in onetime investment, even when the technology or environment evolves, the test scripts are continuously patched based on the market demands. Thus, the automation testing is remaining as a challenging task to define and test automatically in a cost-effective mode. The developed device is called as femtocell, which helps to effectively densify the network and deliver a great customer experience in small indoor environments. It is checked to meet the real world scenarios under resilience test cases. It is performed by various manual test cases. Moreover, this project automates the manual test cases. © 2021 IEEE.

关键词： Automation

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Intelligent Intrusion Detection Using Pattern-Matching Aware Replicated Neural Networks

Intelligent Intrusion Detection Using Pattern-Matching Aware...

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IEEE International Conference on Signal Processing, Computing and Control (ISPCC)

作者： Kalyandurg Rafeeq Ahmed Rahul S Poornapushpakala Veena S Badiger Sunitha B K Sushree B. B. Priyadarshini Department of ECE Presidency University Bangalore Karnataka India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of ECE Sathyabama Institute of Science and Technology Chennai TamilNadu India Department of Computer Applications (DCA) Presidency College Bengaluru India School of Mangement - UG Management JAIN Deemed to be University Bangalore Karnataka Department of CS&IT Siksha 'O' Anusandhan (Deemed to be University) Bhubaneswar Odisha India

ISBN: (数字)9798331538934

ISBN: (纸本)9798331538941

This study presents the Pattern Matching aware Replicated Neural Network-based Intrusion Detection System (PM-RNN-IDS), an approach to WSN detection of breaches that is both quick and accurate. Because of their limited resources, sensor nodes in WSNs provide particular obstacles that need sophisticated algorithms for detecting malicious behaviour. Starting with data preliminary processing, the approach moves on to improving K-Nearest Neighbour (KNN) computation, which uses a bagged approach to deal with values that are absent, and firefly algorithm to remove redundant information. By following these procedures, you may be confident that your dataset is strong and complete. Afterwards, the most significant properties are extracted using feature choosing using Modified Particle Swarm Optimization (PSO), which improves detecting accuracy. Implemented within the system of intrusion detection is a Replicator Neural Network (RNN), a three-hidden-layer multiple-layer perceptron that has been fine-tuned for precise finding of anomalies by means of sophisticated matchmaking. Accurate identification of infiltration patterns is supported by the RNN's ability to reduce reconstructions errors, which allows it to accurately detect anomalies. The suggested PM-RNN-IDS outperforms the Tree-CNN approach for intrusion detection, with an overall accuracy of around 92%.

关键词： Wireless sensor networks Accuracy Neural networks Intrusion detection Process control Nearest neighbor methods Signal processing Feature extraction Threat assessment Testing

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AI-Enhanced Cybersecurity for Intelligent Systems (IS) Towards Criminals

AI-Enhanced Cybersecurity for Intelligent Systems (IS) Towar...

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2024 International Conference on Communication, Computing and Energy Efficient Technologies, I3CEET 2024

作者： Kumaran, U. Padmanaban, S. Kumar, Rakesh Al-Fatlawy, Ramy Riad Kaur, Manpreet Konatam, Sukanya Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Cse Bengaluru India Karpagam Institute of Technology Department of Science and Humanities Coimbatore641105 India Gla University Department of Computer Engineering and Applications Mathura India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Jhanjeri Punjab Mohali140307 India The University of Texas AustinTX United States

ISBN: (纸本)9798331541583

One of the strongest examples for this scenario is artificial intelligence (AI) in which AI interacts between individuals and their environment. Artificial intelligence powering smart homes is also still young, but it has huge promise in the eyes of experts. In this article, we will be looking at a very straightforward yet an elegant AI-powered home automation solution. The focus of this research is to develop the AI-based home lighting system that appears to operate just like normal;when you're still out in your cars at night. We used Arduino hardware for the physical building of AI based smart house and coded its software to make it real-world compatible. Over 5,500 over result vectors and nine inputs were subseqentially used to establish a unique dataset that mimic actual evening light patterns in the real world as well as presented an appearance of presence within home. Ta. In plain English: AI can, yes indeed understand and control your home night light. Comparison of Performance: The proposed system is evaluated against common machine learning classification techniques like multi perceptually layered neurons, the Gaussian naive Bayes method, linear discriminant analysis methods and vector machines. This is an encouraging realization that AI can indeed make the home safer and bolster defenses against break-ins.. © 2024 IEEE.

关键词： Intelligent systems

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Improving K-Means Effectiveness and Efficiency with Initialization Estimates of Cluster Centroids 2

Improving K-Means Effectiveness and Efficiency with Initiali...

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2nd International Conference on Smart Electronics and Communication, ICOSEC 2021

作者： Ojha, Rajesh Kumar Srivastava, Sandeep Goyal, Mohit Kumar, Lalan Kumar, Amit Prasad, Chitturi Sri Sri University Faculty of Science Odisha Cuttack753001 India Galgotias University School of Computer Science Engineering UP Greater Noida201306 India GL Bajaj Institute of Technology Management Department of MCA UP Greater Noida201306 India Graphic Era Hill University School of Computing Uttarakhand Dehradun248002 India Kiet Group of Institutions Department of Computer Applications UP Ghaziabad201206 India Koneru Lakshmaiah Education Foundation Department of CSE Department of CSE AP 522502 India

ISBN: (纸本)9781665433686

K-Means is known both for its usefulness in finding clusters of related data as well as its fragility with respect to initialization choices. This paper introduces a 95% more effective and 50% more efficient initialization methods, that could eliminate the need for multiple executions of K-Means to find high quality clustering. To initialize the centroids, it selects a multiple, m, of K real data points, computes (mK)2 distances and keeps only the K maximum( minimum( distance ) ) points. A consequence of this technique enables O(lnK) binary search to find the optimal K on 'linearly' separable clusters. The effectiveness claim applies both to separable and intertwined clusters although the efficiency is lost on intertwined clusters. © 2021 IEEE.

关键词： Support vector machines

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Multi-Layered Ontology Models for Dynamic Knowledge Representation in Social Network Analysis

Multi-Layered Ontology Models for Dynamic Knowledge Represen...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： Y Manohar Reddy Vijilius Helena Raj Saloni Bansal H Pal Thethi Ravi Kalra Mohammed Khalid Institute of Aeronautical Engineering Hyderabad Department of Applied Sciences New Horizon College of Engineering Bangalore Department of Computer Engineering and Applications GLA University Mathura Lovely Professional University Phagwara Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh College of Technical Engineering National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

In the rapidly evolving landscape of social networks, understanding the multifaceted nature of human interactions and the dissemination of information is pivotal. This paper introduces a novel approach to dynamic knowledge representation through the development of Multi-Layered Ontology Models tailored for Social Network Analysis (SNA). The core of this research lies in architecting a robust, scalable, and semantically rich framework that leverages the principles of data mining and web technologies to dissect and interpret the complex structure of social networks. The ontology layers are intricately designed to represent various dimensions of social interactions, including temporal dynamics, user behavior, and network evolution. Each layer is interlinked yet distinct, providing a granular view of the social fabric and enabling precise inference and prediction of patterns. The methodology adopts an innovative blend of semantic web technologies for ontology construction, coupled with advanced data mining techniques to efficiently process and analyze large volumes of social data. The result is a dynamic model that adapts to the ever-changing social context, offering valuable insights into trends, community structures, and influence dissemination. The implications of this research are vast, impacting areas ranging from targeted marketing to public opinion analysis. This paper sets a new precedent for interdisciplinary research in SNA, opening avenues for further exploration and refinement.

关键词： Semantic Web Adaptation models Analytical models Social networking (online) Simulation Ontologies Predictive models

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Strategies for Machine Learning Applied to Noisy HEP Datasets: Modular Solid State Detectors from SuperCDMS

arXiv

引用

arXiv 2024年

作者： Cushman, P.B. Fritts, M.C. Chambers, A.D. Roy, A. Li, T. School of Physics and Astronomy University of Minnesota MinneapolisMN55455 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States Department of Computer Science and Engineering University of Minnesota MinneapolisMN55455 United States

Background reduction in the SuperCDMS dark matter experiment depends on removing surface events within individual detectors by identifying the location of each incident particle interaction. Position reconstruction is achieved by combining pulse shape information over multiple phonon channels, a task well-suited to machine learning techniques. Data from an Am-241 scan of a SuperCDMS SNOLAB detector was used to study a selection of statistical approaches, including linear regression, artificial neural networks, and symbolic regression. Our results showed that simpler linear regression models were better able than artificial neural networks to generalize on such a noisy and minimal data set, but there are indications that certain architectures and training configurations can counter overfitting tendencies. This study will be repeated on a more complete SuperCDMS data set (in progress) to explore the interplay between data quality and the application of neural networks. © 2024, CC BY.

关键词： Neural networks

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Unified View of Damage leaves Planimetry & Analysis Using Digital Images Processing Techniques

arXiv

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arXiv 2023年

作者： Kumar, Pijush Kanti Munjal, DeepKiran Rani, Sunita Voumik, Liton Chandra Dutta, Anurag Ramamoorthy, A. Department of Information Technology Government College of Engineering & Textile Technology Calcutta Serampore India Department of Computer Applications G. L. Bajaj Institute of Technology and Management Uttar Pradesh Greater Noida India Department of Engineering Administration Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Department of Economics Noakhali Science and Technology University Noakhali Bangladesh Department of Computer Science & Engineering Government College of Engineering & Textile Technology Calcutta Serampore India Department of Mathematics Velammal Engineering College Anna University Tamil Nadu Chennai India

The detection of leaf diseases in plants generally involves visual observation of patterns appearing on the leaf surface. However, there are many diseases that are distinguished based on very subtle changes in these visually observable patterns. This paper attempts to identify plant leaf diseases using image processing techniques. The focus of this study is on the detection of citrus leaf canker disease. Canker is a bacterial infection of leaves. Symptoms of citrus cankers include brown spots on the leaves, often with a watery or oily appearance. The spots (called lesions in botany) are usually yellow. It is surrounded by a halo of the leaves and is found on both the top and bottom of the leaf. This paper describes various methods that have been used to detect citrus leaf canker disease. The methods used are histogram comparison and k-means clustering. Using these methods, citrus canker development was detected based on histograms generated based on leaf patterns. The results thus obtained can be used, after consultation with experts in the field of agriculture, to identify suitable treatments for the processes used. © 2023, CC BY.

关键词： Graphic methods

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Reliability-Redundancy Optimization of an Overspeed Protection System of a Gas Turbine by Modified Wild Horse Optimizer

Reliability-Redundancy Optimization of an Overspeed Protecti...

引用

Disruptive Technologies (ICDT), International Conference on

作者： Shshank Chaube Sangeeta Pant B.P. Joshi Anuj Kumar Manoj K. Singh Ketan Kotecha Department of Physical Sciences Jaypee University Anoopshar Uttar Pradesh India Department of Applied Sciences Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Lavale Pune Maharashtra India Department of Mathematics Graphic Era Hill University Bhimtal India School of Computer Science Engg. & Applications D. Y. Patil International University (DYPIU) Akurdi Pune India School of Computer Science Engineering and Technology Bennett University Techzone II Greater Noida Uttar Pradesh India Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Lavale Pune Maharashtra India

This article addresses the persistent challenges involved in the complex system reliability redundancy optimization of the overspeed protection system of a gas turbine (OPSGT), which is an essential complex system for ensuring the turbine's safety. The optimal functionality of this complex reliability-redundancy is achieved by means of a novel nature-inspired computational approach named the Modified Wild Horse Optimization Algorithm (MWHO), inspired by the behavior of wild horses. The MWHO algorithm efficiently delivers optimal solutions to this complex NP-hard problem, showcasing its potential in solving various complex system reliability optimization problems. This efficiency is easily verified through extensive numerical simulations, demonstrating that the outcomes produced by MWHO exhibit outstanding performance when compared to various existing metaheuristics. Hence, the MWHO not only introduces a fresh perspective in tackling associated reliability-redundancy optimization challenges but also establishes itself as a competitive and efficient algorithm in the complex realm of system reliability optimization.

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Design and Analysis of a Robust S-Box for Lightweight Cryptography in IoT applications Using the PRESENT Algorithm

Design and Analysis of a Robust S-Box for Lightweight Crypto...

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IEEE International Conference on Signal Processing, Computing and Control (ISPCC)

作者： Kartik N Umakanth S Ajit Ku. Mohapatra K Shoukath Ali Praney Madan Manisha Department of Computer Applications (DCA) Presidency College Bengaluru India School of Mangement - UG Management JAIN Deemed to be University Bangalore Karnataka Department of CS&IT Siksha 'O' Anusandhan (Deemed to be University) Bhubaneswar Odisha India Department of ECE Presidency University Bangalore Karnataka India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of ECE Sathyabama Institute of Science and Technology Chennai TamilNadu India

ISBN: (数字)9798331538934

ISBN: (纸本)9798331538941

Information sharing groups prioritize security. IoT gadgets enable smooth data interchange and communications across smart gadgets, ushering in a new age of connectivity. Since the majority of Internet of Things devices are restricted in assets, implementing good safety precautions is challenging. Lighter Cryptography (LWC) approaches to IoT applications are essential, as the research shows. This project aims to create and test inexpensive encryption techniques and methods for IoT gadgets' limited memory, computing capacity, and electrical power. The paper starts with a detailed investigation of cryptography protocols and potential relevance to IoT contexts, stressing privacy and cost-effectiveness trade-offs. Next, the study designs and optimizes lightweight cryptographic basics for optimal both efficiency and safety. The technique of PRESENT is examined extensively. The groundbreaking 2007 extremely portable PRESENT block cipher should use a new substitution box (S-box). Two steps make up the recommended S-box design: static generation and adaptive optimisation. Stochastic randomized and pseudo-number sequence build the static S-box. The fixed S-box is subsequently optimized into an evolving S-box using gradient-based algorithms to improve data. protection. The results of the study in MATLAB 2021 indicate increased performance over the PRESENT cipher across numerous criteria. Competitive assessments employing renowned ciphers have shown the efficacy of the suggested cryptographic approach. Among multiple the suggested method surpasses current algorithms by 98% in latency, energy consumption, power, software efficiency and execution time, indicating its potential as a cryptographic design breakthrough.

关键词： Chaos Ciphers Software algorithms Signal processing algorithms Prediction algorithms Safety Cryptography Internet of Things S Box Resilience

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