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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Dharmateja, M. Rama, Prabha K.P. Asha, N. Nithya, P. Lalitha, S. Manojkumar, P. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Arunai Engineering College Department of Computer Science and Engineering Tamil Nadu Tiruvannamalai India Bannari Amman Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Sathyamangalam India

ISBN: (纸本)9798350382952

Wireless Sensor Actuator Networks (WSANs) are sensitive to cyber threats due to present static encryption techniques. Adaptive security methods are required in WSAN settings to reduce evolving dangers and maintain data integrity. The paper introduces a novel method for encrypting WSAN data using AI and machine learning. Long Short-Term Memory (LSTM) networks process data, Genetic Algorithms (GAs) optimize encryption, and Isolation Forests detect threats in real-time. The proposed system achieved remarkable performance, with scores of 98% data integrity, 96% error repair, and 95% threat detection accuracy. Encryption rates reach their max at 200 Mbps, with optimal resource usage (30% CPU, 25% RAM) and a 5% false positive rate. The proposed AI-driven security architecture improves data integrity, encryption speed, and overall security efficacy in WSANs, surpassing existing methods. The paper overcomes the limitations of static encryption methods and proposes a proactive protection mechanism against developing cyber threats, making a substantial contribution to WSAN security improvement. © 2024 IEEE.

关键词： Cryptography

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Ranking and Un-ranking Calculations for Constant-Weight Gray code on Local Rank Modulation 18

Ranking and Un-ranking Calculations for Constant-Weight Gray...

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18th International Conference on Advanced Computing and Analytics, ACOMPA 2024

作者： Thao, Tran Ngan Huong, Tran Thi Thu Van Khu, Vu Vietnamese-German University Department of Computer Science Binh Duong Viet Nam National University of Singapore Department of Industrial Systems Engineering and Management Singapore

ISBN: (纸本)9798331542467

The local rank modulation scheme, a generalization of the rank modulation scheme, is proposed for accurate and effective data representation in flash memory. In this scheme, a sliding window traversing a sequence of real-valued variables yields a sequence of permutations. We focus on constant-weight Gray codes for the scheme. We represent two constructions for Gray codes of weights 2 and 3 proposed in [6] by grouping transition rules to create more easily calculable paths. From that we give calculations for the ranking and un-ranking on these Gray codes. Additionally, we show that the calculations are efficient by showing their running time in constant and logarithmic time for Gray codes of weights 2 and 3 respectively. © 2024 IEEE.

关键词： Flash memory

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A Review of Key Elements for Underwater Wireless Sensor Networks

A Review of Key Elements for Underwater Wireless Sensor Netw...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Al-Dulaimi, Aymen Mohammed Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Aliesawi, Salah A. Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Engineering Techniques Department of Communication Technical Engineering Al-Farahidi University Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq

ISBN: (纸本)9798350369106

Underwater Wireless Sensor Networks (UWSNs) are emerging and have huge prospects since they have various applications in oceanography, environmental monitoring, and underwater communication. These networks contain low-cost submerged sensors that are mostly connected by way of acoustic media. Sensors are relatively new to underwater research, and thanks to recent technological development, they can now function at different depths. In this article, the author explores what UWSNs must do to operate smoothly by exploring the basic features that they must meet for further development. Real-time Underwater communication remains an open problem due to underwater acoustic channels important for undersea data acquisition, undersea navigation, and undersea remote sensing. To mitigate such challenges and in light of the use of new technologies, researchers seek to improve the reliability and reliability of UWSNs. Finally, there is great potential with UWSNs for investigating and exploiting the underwater environment by improving scenarios of different domains in line with subaqueous data and communications. © 2024 IEEE.

关键词： Wireless sensor networks

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P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

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Intelligent Automation & Soft Computing 2023年第1期35卷 553-566页

作者： Ayman Altameem Ramesh Chandra Poonia Ankit Kumar Linesh Raja Abdul Khader Jilani Saudagar Department of Computer Science and Engineering College of Applied Studies and Community ServicesKing Saud UniversityRiyadh11533Saudi Arabia Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Computer Engineering and Applications GLA UniversityMathuraUPIndia Department of Computer Application Manipal University JaipurRajasthan303007India Information Systems Department Imam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Data clustering is crucial when it comes to data processing and *** new clustering method overcomes the challenge of evaluating and extracting data from big *** or categorical data can be *** clustering methods favor numerical data clustering and ignore categorical data *** recently,the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering ***,these algorithms could not use the concept of categorical data for *** that,suggestions for expanding traditional categorical data processing methods were *** addition to expansions,several new clustering methods and extensions have been proposed in recent *** is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster *** paper aims to modify the algo-rithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster *** parameterized ROCK algorithm is the name given to the modified algorithm(P-ROCK).The proposed modification makes the original algorithm moreflexible by using user-defined parameters.A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm.A comparison with the original ROCK algorithm is also *** results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%.The proposed P-ROCK algorithm has improved the runtime and is moreflexible and scalable.

关键词： ROCK K-means algorithm clustering approaches unsupervised learning K-histogram

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A Novel Energy Efficient Sensor Data Encryption in Fog Based IoT Networks

A Novel Energy Efficient Sensor Data Encryption in Fog Based...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Manicka Raja, M. Manoj Kumar, S. Kiruba, B. Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (纸本)9798350319088

Smart devices equipped with embedded systems, including CPUs, sensors, and communication hardware, utilize web connectivity to gather and relay data from their environment, forming a segment of the Internet of Things network. Sensors in an IoT network are resource-constrained devices, but traditional data security techniques use complicated security mechanisms with long processing and reaction times, which reduce the network's overall lifespan. As a consequence, we proposed an EELWEP (energy efficient light weight encryption process) system to keep the data acquired by each sensor node private. When using this strategy, the secret key is exchanged using the Diffie-Hellman method using the Pretty Good Privacy (PGP) program, and a large portion of the operations is carried out via the use of symmetric cryptography. The energy usage and calculation time on the sensor network are significantly reduced as a result of this method. The suggested system is simulated, and their results are analysed using a variety of parameters in comparison to existing benchmark schemes. Comparing the suggested technique to the current approaches reveals that it outperforms the alternatives in the vast majority of situations studied. © 2023 IEEE.

关键词： Internet of things

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Improved IChOA-Based Reinforcement Learning for Secrecy Rate Optimization in Smart Grid Communications

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computers, Materials & Continua 2024年第11期81卷 2819-2843页

作者： Mehrdad Shoeibi Mohammad Mehdi Sharifi Nevisi Sarvenaz Sadat Khatami Diego Martín Sepehr Soltani Sina Aghakhani The WPI Business School Worcester Polytechnic InstituteWorcesterMA 01609-2280USA Department of Computer Science Escuela de Ingeniería Informática de SegoviaUniversidad de ValladolidSegovia40005Spain Department of Data Science Engineering University of HoustonHoustonTX 77204USA Department of Industrial Engineering College of EngineeringUniversity of HoustonHoustonTX 77204USA Department of Industrial and Manufacturing Systems Engineering Iowa State UniversityAmesIA 50011USA

In the evolving landscape of the smart grid(SG),the integration of non-organic multiple access(NOMA)technology has emerged as a pivotal strategy for enhancing spectral efficiency and energy ***,the open nature of wireless channels in SG raises significant concerns regarding the confidentiality of critical control messages,especially when broadcasted from a neighborhood gateway(NG)to smart meters(SMs).This paper introduces a novel approach based on reinforcement learning(RL)to fortify the performance of *** by the need for efficient and effective training of the fully connected layers in the RL network,we employ an improved chimp optimization algorithm(IChOA)to update the parameters of the *** integrating the IChOA into the training process,the RL agent is expected to learn more robust policies faster and with better convergence properties compared to standard optimization *** can lead to improved performance in complex SG environments,where the agent must make decisions that enhance the security and efficiency of the *** compared the performance of our proposed method(IChOA-RL)with several state-of-the-art machine learning(ML)algorithms,including recurrent neural network(RNN),long short-term memory(LSTM),K-nearest neighbors(KNN),support vector machine(SVM),improved crow search algorithm(I-CSA),and grey wolf optimizer(GWO).Extensive simulations demonstrate the efficacy of our approach compared to the related works,showcasing significant improvements in secrecy capacity rates under various network *** proposed IChOA-RL exhibits superior performance compared to other algorithms in various aspects,including the scalability of the NOMA communication system,accuracy,coefficient of determination(R2),root mean square error(RMSE),and convergence *** our dataset,the IChOA-RL architecture achieved coefficient of determination of 95.77%and accuracy of 97.41%in validation *** was accompanied by the lowes

关键词： Smart grid communication secrecy rate optimization reinforcement learning improved chimp optimization algorithm

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A Model for Predicting Chronic Kidney Diseases Based on Medical Data Using Reinforcement Learning

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SN computer science 2024年第4期5卷 353页

作者： Nramban Kannan, Senthil Kumar Aseervatham, Joshi Moholkar, Kavita Palanimuthu, Mithun Marappan, Saranya Narendran, Muthusamy Sathar, Banu Sengan, Sudhakar Department of Computer Science & amp Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai 600123 India Department of Computer Science and Business Systems Engineering JSPM’S Rajarshi Shahu College of Engineering Savitribai Phule Pune University Pune 411033 India Department of Computer Science and Engineering St. Joseph’s Institute of Technology Tamil Nadu Chennai 600119 India Department of Information Technology Paavai Engineering College Tamil Nadu Namakkal 637018 India Department Information Technology KCG College of Technology Tamil Nadu Chennai 600097 India Department of Master of Computer Application T. J. Institute of Technology Chennai 600100 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli 627451 India

Kidney diseases (KD) are a global public health concern affecting millions. Early detection and prediction are crucial for effective treatment. Artificial intelligence (AI) techniques have been used in KDP to analyze past medical records, applying patients’ Electronic Medical Record (EHR) data. However, conventional statistical analysis methods conflict with fully comprehending the complexity of EHR data. AI algorithms have helped early KDP learn and identify complex data patterns. However, challenges include training heterogeneous historical data, protecting privacy and security, and developing monitoring system regulations. This study addresses the primary challenge of training heterogeneous datasets for real-world evaluation. Early detection and diagnosis of chronic kidney disease (CKD) is crucial for improved outcomes, reduced healthcare costs, and reliable treatment. Early treatments are crucial for CKD, as it often develops without apparent symptoms. Predictive models, particularly those using reinforcement learning (RL), can identify significant trends in complex healthcare information, which standard techniques may struggle with. The study makes KDP more accurate and reliable using RL methods on clinical data. This lets doctors find diseases earlier and treat them better by looking at static and changing health measurements. Machine learning (ML) algorithms can enhance the accuracy of AI systems over time, enhancing their effectiveness in detecting and diagnosing diseases. In the current investigation, the RL-ANN model is implemented for performing enforceable CKD by assessing the outcomes of multiple neural networks, which include FNN, RNN, and CNN, according to parameters such as accuracy, sensitivity, specificity, prediction error, prediction rate, and kidney failure rate (KFR). The recommended RL-ANN method has a lower failure rate of 70% based on the KFR data. Further, the proposed approach earned 95% in PR and 70% in analysis of errors. However, the RL

关键词： Alerting systems and diagnostic assistance Chronic kidney disease Machine learning Medical history RL-ANN

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FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalous Event Detection 38

FT-AED: Benchmark Dataset for Early Freeway Traffic Anomalou...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Coursey, Austin Ji, Junyi Quinones-Grueiro, Marcos Barbour, William Zhang, Yuhang Derr, Tyler Biswas, Gautam Work, Daniel B. Department of Civil and Environmental Engineering Vanderbilt University United States Institute for Software Integrated Systems United States Department of Computer Science United States

Early and accurate detection of anomalous events on the freeway, such as accidents, can improve emergency response and clearance. However, existing delays and mistakes from manual crash reporting records make it a difficult problem to solve. Current large-scale freeway traffic datasets are not designed for anomaly detection and ignore these challenges. In this paper, we introduce the first large-scale lane-level freeway traffic dataset for anomaly detection. Our dataset consists of a month of weekday radar detection sensor data collected in 4 lanes along an 18-mile stretch of Interstate 24 heading toward Nashville, TN, comprising over 3.7 million sensor measurements. We also collect official crash reports from the Tennessee department of Transportation Traffic Management Center and manually label all other potential anomalies in the dataset. To show the potential for our dataset to be used in future machine learning and traffic research, we benchmark numerous deep learning anomaly detection models on our dataset. We find that unsupervised graph neural network autoencoders are a promising solution for this problem and that ignoring spatial relationships leads to decreased performance. We demonstrate that our methods can reduce reporting delays by over 10 minutes on average while detecting 75% of crashes. Our dataset and all preprocessing code needed to get started are publicly released at https://***/ft-aed/to facilitate future research. © 2024 Neural information processing systems foundation. All rights reserved.

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Introduction to the Minitrack on Learning Analytics 2023

Proceedings of the Annual Hawaii International Conference on...

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Proceedings of the Annual Hawaii International Conference on System sciences 2023年 2023-January卷 1269-1270页

作者： Willermark, Sara Islind, Anna Sigridur Deeva, Galina Óskarsdóttir, Mari University West School of Business Economics and IT Sweden Reykjavik University Department of Computer Science Iceland KU Leuven Research Centre for Information Systems Engineering Belgium

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Oral Squamous Cell Carcinoma Diagnosis Using Spotted Hyena Optimizer Combined with Transfer Learning Approaches 15th

Oral Squamous Cell Carcinoma Diagnosis Using Spotted Hyena O...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Balakrishnan, Roshni M. Bharanidharan, N. Chakravarthy, S. R. Sannasi Kumar, V. Vinoth Patil, Swetha Rajaguru, Harikumar Department of Computer Science and Engineering Amrita School of Computing Bengaluru Amrita Vishwa Vidhyapeetham Bengaluru India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Electronics and Communication Engineering Bannari Amman Institute of Technology Sathyamangalam India Department of Computer Science and Engineering PES University EC Campus Bengaluru India

ISBN: (纸本)9783031810794

Histopathological oral images are one of the popular methodologies to diagnose oral squamous cell carcinoma. computerized diagnostic systems need to be developed in order to process huge number of histopathological oral images for each subject and helping the clinician in classifying the oral images as either normal class or oral squamous cell carcinoma class. This research work uses deep transfer learning approaches namely MobileNetV3, InceptionV2, and EfficientNetB3 for extracting the features from histopathological oral images. Spotted Hyena Optimizer is used as feature transform for improving classification accuracy. Two dense layers are used for classifying the transformed features. 2784 & 3632 number of histopathological oral images are considered in this study under the two classes as normal and oral squamous cell carcinoma respectively. With the proposed spotted Hyena Optimizer, the maximum accuracy attained is 93% for the features extracted through InceptionV2 model. Relatively, the highest accuracy attained is 89% if the optimizer is not used for the features extracted through MobileNetV3 model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Transfer learning

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