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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Hannah Jessie Rani, R. Goyal, Manish Kumar Mishra, Awakash Faculty of Engineering and Technology Department of Electrical and Elceltronics Engineering Bangalore India Vivekananda Global University Department of Computer Science & Application Jaipur India Maharishi University of Information Technology Engineering Department Uttar Pradesh India

ISBN: (纸本)9798350357769

The complex operation of contemporary power systems is closely linked to the widespread integration of computer networks, or cyber networks. These applications fall into two general categories: direct and indirect interdependencies, which are the result of how they communicate with power networks. Past studies explored the area of direct interdependencies in cyber-power networks and presented a state mapping-based model that was carefully designed to assess its impact on power system dependability. The investigation of indirect relationships between cyber and electricity networks is the new emphasis of this article. A wide range of applications involving indirect interdependencies in modern power systems are examined, laying the foundation for the release of a model that is based on state updating. This model is carefully crafted to provide a numerical evaluation of cyber-power networks' dependability in the face of indirect interdependencies. Two separate models are used to maximize data communication inside the cyber network and minimize load limitation in the power network, respectively, in order to maximize the functioning of these integrated networks. In order to demonstrate the efficacy of the suggested solution model, the research focuses on a high-voltage converter equipped with both protection and monitoring devices. By taking a comprehensive approach, the research makes a substantial contribution to a deeper understanding of the complex interactions between cyber and electricity networks, providing priceless insights for enhancing the dependability of these interconnected systems. © 2023 IEEE.

关键词： Cyber-Power Interdependencies Dependability Indices and Optimization Framework Indirect Relationships Monitoring Systems P- Table Protection Systems Reliability Assessment Smart Grid State Updating

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Coordinate-Oriented Clustered Chain Based Routing Protocol (COCCBRP) for Wireless Sensor Network 3

Coordinate-Oriented Clustered Chain Based Routing Protocol (...

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3rd International Conference on Technological Advancements in Computational sciences, ICTACS 2023

作者： Deep, Kamal Rani, Sonia Jain, Payal Yaduvanshi, Ankit Dronacharya Group of Institutions Department of Electronics and Communication Engineering Greater Noida India School of Computing Science & Engineering Galgotias University Greater Noida India Dpg Institute of Technology & Management Department of Computer Science & Application Gurugram India

ISBN: (纸本)9798350342338

Wireless sensor networks employ a variety of techniques such as data recycling, power management, and energy-aware routing protocols to reduce power consumption. Clustering is a well-known approach for extending the life of a network. It is an approach to divide the network into small groups and high-powered nodes are elected as Cluster Heads (CHs)for each group. This paper presents Coordinate-Oriented Clustered Chain Based Routing Protocol (COCCBRP), where the cluster is created along with X and Y-coordinates into equal size. Three phases make up the COCCBRP protocol's operation: chain construction, chain head selection, and head rotation mechanism. According to the distance and energy level, the criteria value of each node is determined and the node with the highest criteria value is chosen as the Chain Head. The performance of implemented work is compared with the existing CCBRP protocol. According to the findings of the simulation, the purposed strategy COCCBRP increases lifetime and reduces energy consumption as compared to the CCBRP protocol. © 2023 IEEE.

关键词： CH (Cluster Head) Clusters chain Sensor nodes Sink node WSN

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Graph Based Keywords Extraction from Crime Data

Graph Based Keywords Extraction from Crime Data

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International Conference on Computational Intelligence and Communication Networks (CICN)

作者： Priyanka Das Anuvab Munshi Annwesha Naha Adrija Dey Sriparno Chakraborty Dept. of Computer Application and Science Institute of Engineering & Management Kolkata University of Engineering and Management Kolkata

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

It takes a lot of work to gather a large amount of data to help in criminal investigation and prevention of crimes. The most effective methods for reporting crimes are insufficient for both civilians and law enforcement. The police and witness narrative summaries are frequently deemed to be too lengthy to read. Hence, there exists many research works that represents summary of those descriptive reports. But to make it easier, there should be certain phrases or words so that Police investigators can quickly understand criminal scenarios saving them from having to read the entire report. This can be accomplished with the use of criminal information extraction. Here, the proposed study uses a graph-based methodology and natural language processing techniques to extract crime-related keywords from in-depth police and witness narrative descriptions. Initially, the suggested work selected nouns, verbs, and adjectives from the crime reports and evaluated their importance scores. Based on the words' importance scores, a graph partitioning technique has been applied; following repeated runs of the algorithm, the words are classified into clusters. Each cluster in this instance, together with the words that reside within it, represent a different aspect of crime. These clustered words are considered as the keywords for representing the crime.

关键词： Law enforcement Prevention and mitigation Clustering algorithms Information retrieval Natural language processing Partitioning algorithms Classification algorithms Data mining Communication networks Computational intelligence

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An Energy Efficiency Enhanced through Duty Cycle based on Clusters in Wireless Body Area Network

An Energy Efficiency Enhanced through Duty Cycle based on Cl...

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Power Electronics & IoT applications in Renewable Energy and its Control (PARC), 2020 International Conference on

作者： Anita Rani Rakesh Kumar Anant Ram Computer Science of Engineering and Application GLA University Mathura India Computer Science Govt. Polytechnique college Jalun India

Wireless body area network (WBANs) plays an important role in monitoring and helping the patient to maintain the health surveillance system. In this research paper, a variable duty cycle-based clustering algorithm is designed to increase the energy efficiency, by varying the awake and sleep period according to the criticality of the sense data that need to be transmitted. In the proposed approach, the network consists of two phases as LEACH Protocol. first steady phase and second setup phase. Firstly, applying the variable duty cycle and distance centroid has a maximum energy of alive node is considered as initial cluster heads (CHs). The successive CHs are recognized based on residual energy based on the threshold distance from the current cluster head (CH). After the formation of the clusters, the Cluster head aggregates the emergency data through alive nodes in Variable duty cycle values and sends it to the base station. In this algorithm no need to transmit the data in normal conditions of the patient (i.e.) they are dead nodes This technique provides more energy efficiency in the network. The performance evaluation of the proposed protocol Energy Efficient Duty Cycle-Based Cluster Approach (EEDCCA) is done using MATLAB tool and results are analyzed. The simulation result validates that the proposed technique increases the energy efficiency of the network and also Enhancement of Lifetime of the whole WBAN network.

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Profiling Industrial 3D Parts with Complex Shapes Using 2D Imagery 15

Profiling Industrial 3D Parts with Complex Shapes Using 2D I...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mahato, Nivedan Kaushik, Harshita Swadimath, Uma C. Arka Jain University Department of Mechnical Engineering Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India School of Mangement - Pg Department of Management Karnataka Bangalore India

ISBN: (纸本)9798350370249

Commercial 3-D element profiling via second imagery is a modern technique to acquire the 3-D geometric characteristics of quite complicated shapes frequently encountered in industrial additives. This approach makes use of a set of pix taken from multiple angles to derive the corresponding 3-D profile of the component with the use of photogrammetry strategies. The resulting three-D statistics can be similarly characterized to evaluate the part's dimensional accuracy, floor texture, and different attributes. The novel technique described in this work augments conventional measuring methods and opens new possibilities for automated bodily asset evaluation, reverse engineering, and automated inspection. The method defined has potential programs in an extensive type of fields in which correct geometrical characterization of complex parts is important. © 2024 IEEE.

关键词： Characterization Commercial complex Complicated geometrical Photogrammetry Potential

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Traffic Prediction Analysis (TPA) Using Machine Learning Methodologies 6

Traffic Prediction Analysis (TPA) Using Machine Learning Met...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Solanki, Mohit Rawat, Vandana Pandey, Monika Singh, Neelam Aswal, Shobha Graphic Era Deemed to Be University Department of Computer Application Dehradun India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India School of Computing Graphic Era University Dehradun India

ISBN: (纸本)9798350304480

A traffic prediction system might help drivers in making better travel choices. Minimizing carbon emissions, reducing traffic congestion, and improving the effectiveness of traffic management to deliver such traffic flow data is the main aim of traffic flow prediction. Prediction of traffic is essential in a system of intelligent transportation. Effective traffic forecasting can help with route design, vehicle dispatching, and reducing traffic congestion. Due to its incredible capacity to predict traffic patterns, a branch of machine learning which is also called 'deep learning,'has recently attracted a lot of attention. In this study, a comparative evaluation has been done to predict traffic flow based on deep learning. To prepare the stacked auto encoder model, basic traffic flow features are included. The goal of this study is to conduct a thorough screening for deep earning -based traffic forecast algorithms from multiple angles. This study recommends a deep learning method to predict urban traffic that combines data from twitter messages with traffic and weather data. The predictive model uses a deep Bi-directional long- short-term memory (LSTM) Stacked Auto Encoder (SAE) architecture for multi-step traffic flow prediction that was trained from tweets, traffic, and weather datasets. To support study in this area, it also gathers and arranges frequently available public datasets. © 2023 IEEE.

关键词： Long-short Tem Memory (LSTM Stacked Auto Encoder (SAE etc

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Intelligent Machine Learning Method for Detecting Suspicious Encoded Messages 2

Intelligent Machine Learning Method for Detecting Suspicious...

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2nd International Conference on Sustainable Computing and Data Communication Systems, ICSCDS 2023

作者： Hussain, Syed Pakkir Mohideen, S. B.S Abdur Rahman Crescent Institute of Science & Technology Computer Science and Engineering Department Tamil Nadu Chennai India B.S Abdur Rahman Crescent Institute of Science & Technology Computer Application Department Tamil Nadu Chennai India

ISBN: (纸本)9781665491990

With the use of an Advanced Machine Learning (AML) approach for suspicious coded message detection such as cryptanalysis SR4S or any messaging system, a framework to prevent worldwide crime, including terrorist attacks, bomb explosions, drone attacks, and all large and minor assaults. Terrorists communicate with their teammates in different parts of the world and give instructions with resources to attack the government or the public. To maintain secrecy, they use encrypted messages. The proposed framework at the server site will detect encrypted messages and decrypt them using Rail fence Cipher techniques. There is currently no proper solution to prevent a large crime across the globe. Suspicious Message Detection (SMD) is employed when a server receives an encoded message;the proposed system decrypts the message and accelerates the process of predicting the type of crime from microblogs before it is committed by criminals. The security sector will be less tense as a result of the dissemination of information regarding criminals. © 2023 IEEE.

关键词： Cryptography

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Automated Testing for Service-Oriented Architecture: Leveraging Large Language Models for Enhanced Service Composition

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IEEE Access 2025年 13卷 89627-89640页

作者： Altin, Mahsun Mutlu, Behcet Kilinc, Deniz Cakir, Altan Istanbul Technical University Department of Computer Engineering Istanbul34467 Turkey Wingie Enuygun Group Istanbul34750 Turkey University of Bakirçay Department of Computer Engineering Izmir35665 Turkey Istanbul Technical University Artificial Intelligence Data Science Research and Application Center Istanbul34467 Turkey ITU Ari Teknokent Parton Big Data Analytics and Consulting Istanbul34467 Turkey Adin.Ai WilmingtonDE19806 United States

This article explores the application of Large Language Models (LLMs), including proprietary models such as OpenAI's ChatGPT 4o and ChatGPT 4o-mini, Anthropic's Claude 3.5 Sonnet and Claude 3.7 Sonnet, and Google's Gemini 1.5 Pro, Gemini 2.0 Flash, and Gemini 2.0 Flash-Lite, as well as open-source alternatives including Qwen2.5-14B-Instruct-1M, and commercially accessed models such as DeepSeek R1 and DeepSeek V3, which were tested via APIs despite having open-source variants, to automate validation and verification in application Programming Interface (API) testing within a Service-Oriented Architecture (SOA). Our system compares internal responses from the Enuygun Web Server against third-party API outputs in both JSON and XML formats, validating critical parameters such as flight prices, baggage allowances, and seat availability. We generated 100 diverse test scenarios across varying complexities (1-4 flight results) by randomly altering request and response parameters. Experimental results show that Google Gemini 2.0 Flash achieved high accuracy (up to 99.98%) with the lowest completion time (85.34 seconds), while Qwen2.5-14B-Instruct-1M exhibited limited capability in processing complex formats. Models such as OpenAI's ChatGPT and Anthropic's Claude Sonnet models also demonstrated strong performance in single-flight validation scenarios, making them suitable for low-latency, high-precision tasks. Our findings indicate that some open-source models can offer promising cost-effective alternatives, though performance significantly varies. This integration of LLMs reduced manual workload, improved test scalability, and enabled real-time validation across large-scale datasets. As LLM technologies mature, we anticipate further advances in automation, accuracy, and efficiency in software validation systems. © 2013 IEEE.

关键词： Service oriented architecture (SOA)

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Disease Detection in C4 Plants using Machine Learning Approaches 2

Disease Detection in C4 Plants using Machine Learning Approa...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Singh, Souravdeep Sharma, Geeta Kumar, Ram Kumar, Adesh School of Computer Application Lovely Professional University Punjab Phagwara144411 India School of Computing Science and Engineering VIT Bhopal University Bhopal India School of Agriculture Lovely Professional University Phagwara India

ISBN: (纸本)9798331519056

Disease detection in crops is essential to maintaining food security and agricultural productivity. Plant disease identification is essential to maintaining crop health and increasing agricultural productivity. with the advancement of computational techniques like Machine learning is used for extracting insights from data, enabling automation, and making predictions in various farming domains. Recent assessments reveal that infections that develop on the leaves of plants are causing significant losses for 56% of the agriculture sector. Monitoring the disease's growth and increasing agricultural production is essential. We must first identify the condition early on and take action to stop it from propagating. Therefore, we could potentially able to resolve this challenge by implementing some algorithms for identifying diseases on leaflets. In this study, we investigate the efficacy of Convolutional Neural Networks (CNNs) for detecting diseases in two major crops: maize and sugarcane which lie under C4 plants. Utilizing a dataset located from Kaggle, comprising a diverse range of images depicting various disease symptoms, we trained and evaluated CNN models for both crops. The accuracy of disease detection achieved remarkable results, with 98% accuracy for maize and 100% accuracy for sugarcane. Our comparative analysis highlights the efficacy of CNNs in accurately identifying diseases in these crops, showcasing their potential for aiding farmers in timely disease management and enhancing agricultural yield. © 2024 IEEE.

关键词： C4 Plants Leaf diseases Machine Learning Maize Sugarcane

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Cash Flow Forecasting Based on Wavelet Transform and Neural Networks 8

Cash Flow Forecasting Based on Wavelet Transform and Neural ...

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8th International Conference on computer science and engineering, UBMK 2023

作者： Soylen, Zumra Mammadova, Farida Fasounaki, Mandana Ozmen, Ayse Irem Ince, Gokhan Istanbul Technical University Department of Computer Engineering İstanbul Turkey Mdp Group R&d Department İstanbul Turkey Istanbul Technical University Department of Computer Engineering & Ai and Data Science Application and Research Center İstanbul Turkey

ISBN: (纸本)9798350340815

Cash flow forecasting is a critical task for businesses and financial institutions to ensure effective financial planning and decision-making. However, limited data availability poses a significant challenge when developing accurate and robust cash flow prediction models. In this paper, we investigate the performance of various forecasting methods and propose an approach based on wavelet transform for improving the forecasting accuracy. We demonstrate the effectiveness of the proposed approach with the best combination of wavelet functions and methods for forecasting future values in a univariate time series. We investigate the impact of wavelet transform on forecasting techniques based on open-source datasets. Our methodology includes data collection, preprocessing, feature engineering, model selection, and experimentation using different performance evaluation metrics. © 2023 IEEE.

关键词： Forecasting

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