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8th International Symposium on Multidisciplinary studies and Innovative Technologies, ISMSIT 2024

作者： Al-Mahmood, Haider H. Alsaad, Saad N. Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq University of Mustansiriyah Department of Computer Science College of Science Baghdad Iraq

ISBN: (纸本)9798350354423

Lightweight encryption methods are introduced to overcome the challenges of high-power consumption and high memory usage in the traditional cryptography system. The primary goal of the lightweight encryption technique is to make it suitable for use in devices provided with lower power and small memories. The importance of lightweight cryptosystem is its ability to provide suitable security for data encryption without consuming high level of power and without need for big memories. As the devices with lower battery life and supplied low memory become widespread, encryption key management problems start to emerge. This may lead to a high possibility of an attack on the encryption key and break the encryption system. In this context, this paper proposes a novel hashing- based approach for key generation in lightweight cryptography. The approach involves generating significant hash digits from a seed input to the hashing system, and then using hash code slicing to select different characters and numbers that can be used as a seed for the key generating system. The practical results, which demonstrate that a small seed length can effectively generate a large key length, are a testament to the effectiveness of the proposed approach. These results can be used in the encryption process to reduce the initial total bits, thereby enhancing the efficiency of the system. © 2024 IEEE.

关键词： Cryptographic Algorithms Encryption Key Management Hashing Techniques IoT Security Lightweight Cryptography

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Deep Learning Image Classification Abiotic Plant Disease Detection Classification Using MobileNet 4

Deep Learning Image Classification Abiotic Plant Disease Det...

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4th International Conference on Technological Advancements in Computational sciences, ICTACS 2024

作者： Aggarwal, Ritu Aggarwal, Eshaan Jain, Anurag Aluvalu, Rajanikanth Maheswari, Uma Choudhury, Tanupriya Department of Computer Science and Engineering India School of Computer Science and Engineering Iilm University Haryana Gurugram India Symbiosis Institute of Technology Hyderabad Campus Hyderabad India Symbiosis International University Pune India Chaitanya Bharathi Institute of Technology Department of Computer Science and Engineering Hyderabad India School of Computer Sciences University of Petroleum and Energy Studies Upes Uttarakhand Dehradun248007 India

ISBN: (纸本)9798350387490

In the field of agriculture sector the key role of feature food which has a major role in the emergent population with their economy. In the food production for plant disease, it may cause significant loss for the era of diversity in species and the crop production rate in the plant disease detection and diagnosis the accurate detecting techniques could increase the superiority in food productivity and reduce the economic loss for the image categorization and object recognition. As per computed results and analysis of the various techniques, deep learning is as best approach to get the accuracy means and classify the images. In this proposed work to tune the parameters required to train load balancing overload forecasting load balancing overload forecasting the models using CNN. The model performance is evaluated by confusion matrix parameters such as accolade balancing overload forecasting accuracy, precision, Recall, sensitivity, specificity, f-measure, etc. In the plants, the stress will destroy the crop's growth, and yields are affected negatively due to the water supply's suboptimal quantity, soil erection, and abnormal temperature due to other physiological conditions and changes in biochemicals. In this paper, the current model Mobile, which is used for image detection and classification in disease, achieves an accuracy of 99.8%. © 2024 IEEE.

关键词： Climate change Crop disease production Drought Heat stress Plant responses Stress management

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DBCChecker: A Bigraph-Based Tool for Checking Security Properties of Container Compositions

DBCChecker: A Bigraph-Based Tool for Checking Security Prope...

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2023 Italian Conference on Cyber Security, ITASEC 2023

作者： Altarui, Andrea Miculan, Marino Paier, Matteo Department of Computer Science University of Milan Italy Department of Mathematics Computer Science and Physics University of Udine Italy Department of Environmental Sciences Informatics and Statistics University of Venice Italy IMT School for Advanced Studies Lucca Italy

Despite their widespread application in modern systems, container composition is often complex and error-prone. In this work, we present DBCChecker, a tool aiming to verify security properties of systems obtained by composition of containers. From the configuration of a container-based system and an abstract description of the interface behaviour of each container, the tool builds a formal model of the overall system, which can be verified in ProVerif (an automatic symbolic protocol verifier), to check that the overall system satisfies the required properties. The system can be described in a specification language capable to express at once the interfaces and connections of containers and the relevant behavioural aspects of their interfaces, called JSON Bigraph Format (JBF), and inspired by previous formal models, based on bigraphs, for containerized architectures. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Containers

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Balancing Fairness and Accuracy for Predictive Models in Criminal Justice Applications Using Multi-Objective Optimization Methods

Balancing Fairness and Accuracy for Predictive Models in Cri...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Qi, Xiaowen Ma, Yujungrong Nakamura, Kiminori Bhattacharyya, Shuvra S. University of Maryland Department of Electrical and Computer Engineering Institute for Advanced Computer Studies College ParkMD20740 United States University of Mary-land Department of Criminology and Criminal Justice College ParkMD20740 United States

ISBN: (纸本)9781665410205

In the field of predictive modeling for criminal justice applications, the dual challenges of ensuring fairness and maintaining interpretability are crucial. This paper addresses these challenges by introducing a new approach to optimizing decision trees using evolutionary algorithms (EAs). Our approach focuses on refining decision trees to achieve a balance between accuracy and algorithmic fairness, a task complicated by potential bias present in historical data. By leveraging the principles of multi-objective optimization, our model systematically trades off prediction accuracy and fair-ness. The evolutionary process characterized by selection, crossover, and mutation is tailored to fit the decision tree structure, ensuring that model development is not only accurate but also promoting measurable fairness. Experimental results demonstrate the effectiveness of our approach in providing interpretable and fair predictive models that can be considered for high-stakes applications in criminal justice. More broadly, this research makes a significant contribution to the field of explainable machine learning, providing a powerful framework for engineering systems that are transparent, fair, and adaptable to different data environments. © 2024 IEEE.

关键词： Forestry

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An Empirical Study Measuring the Performance Ratings and Evaluating Gender Based Satisfaction Level 1

An Empirical Study Measuring the Performance Ratings and Eva...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Prem Knowles, S. Basu, Sremmant Ashpin Pabi, D.J. Madanapalle Institute of Technology & Science Department of Management Studies Madanapalle India Madanapalle Institute of Technology & Science Department of Computer Science & Engg. Madanapalle India

ISBN: (纸本)9798350328042

Performance ratings, also known as performance appraisals or evaluations, are a systematic process used by organizations to assess and measure the job performance of their employees. The objectives of the study are framed with two main motives *** analyse the performance rating of the employees and *** know the gender based job satisfaction level in IBM. Necessary statistical tools have been applied. This study reveals that 84.6% of them are having high performance rating and 15.4 of them are having very high performance rating. It should be noted that no one opted for low and medium performance rating. The older approach of performance appraisal system really made the employee's sick, as the system compares the employees one another and made the conclusion of efficiency. But the new system is such an eminent approach as it measure the performance by evaluating the employees by their contribution to the department and achieving the organizational goal. There is no much variation in gender based satisfaction level. Here we have to note the strong recruitment and promotion policies of IBM. © 2023 IEEE.

关键词： Job satisfaction

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Optimized Energy Efficient Strategy for Data Reduction Between Edge Devices in Cloud-IoT

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computers, Materials & Continua 2022年第7期72卷 125-140页

作者： Dibyendu Mukherjee Shivnath Ghosh Souvik Pal D.Akila N.Z.Jhanjhi Mehedi Masud Mohammed A.AlZain Department of Computer Science and Engineering Brainware UniversityBarasat700125India Department of Computer Science and Engineering Global Institute of Management and TechnologyKrishnanagar741102India Department of Information Technology School of Computing SciencesVels Institute of ScienceTechnology&Advanced StudiesChennai600117India School of Computer Science and Engineering SCE Taylor’s UniversitySubang Jaya47500SelangorMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Numerous Internet of Things(IoT)systems produce massive volumes of information that must be handled and answered in a quite short *** growing energy usage related to the migration of data into the cloud is one of the biggest *** computation helps users unload the workload again from cloud near the source of the information that must be handled to save time,increase security,and reduce the congestion of ***,in this paper,Optimized Energy Efficient Strategy(OEES)has been proposed for extracting,distributing,evaluating the data on the edge *** the initial stage of OEES,before the transmission state,the data gathered from edge devices are supported by a fast error like reduction that is regarded as the largest energy user of an IoT *** initial stage is followed by the reconstructing and the processing *** processed data is transmitted to the nodes through controlled deep learning *** entire stage of data collection,transmission and data reduction between edge devices uses less *** experimental results indicate that the volume of data transferred decreases and does not impact the professional data performance and predictive *** consumption of 7.38 KJ and energy conservation of 55.57 kJ was found in the proposed OEES *** accuracy is 97.5 percent,data performance rate was 97.65 percent,and execution time is 14.49 ms.

关键词： Energy efficient internet of things transmission performance cloud computing edge devices

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Motivating an Agent-based Distributed AI Framework for Renewable Integration: Power Balancing

Motivating an Agent-based Distributed AI Framework for Renew...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Javaid, Saher Ioannou, Iacovos I. Pitsillides, Andreas Tan, Yasuo Japan Advanced Institute of Science and Technology School of Information Science Ishikawa Japan University of Cyprus Department of Computer Science Nicosia Cyprus

ISBN: (纸本)9798350324174

Green energy sources including wind turbines and solar generating systems play a significant role in power systems due to their low environmental impact. However, their generated power is highly variable, posing an unpredictable danger of power fluctuations. As a result, power fluctuations can affect the quality and stability of the power grid, hence making the power imbalance a formidable problem. Traditional control and management techniques have many limitations in measuring, transmitting, and controlling power data between power devices. To achieve power balancing in each time instance, artificial intelligence (AI) techniques are needed to realize truly real-time power control. This paper promotes the application of distributed AI framework for a power system with renewable power sources, loads, and storage devices to achieve power balancing. © 2023 IEEE.

关键词： Environmental impact

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Information-Centric IoT-Based Smart Farming with Dynamic Data Optimization

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computers, Materials & Continua 2023年第2期74卷 3865-3880页

作者： Souvik Pal Hannah VijayKumar D.Akila N.Z.Jhanjhi Omar A.Darwish Fathi Amsaad Department of Computer Science and Engineering Sister Nivedita UniversityKolkataIndia Post Doctoral Researcher Sambalpur UniversitySambalpurIndia Department of Computer Science Anna Adarsh College for WomenChennaiIndia Department of Information Technology Vels Institute of ScienceTechnology and Advanced StudiesChennaiIndia School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya47500SelangorMalaysia FITTaylor’s UniversitySubang Jaya47500SelangorMalaysia Department of Information Security and Applied Computing Eastern Michigan UniversityUSA School of Information Security and Applied Computing Eastern Michigan UniversityUSA

Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies,including big data,the cloud,and the Internet of Things(IoT).Many researchers try to integrate IoT-based smart farming on cloud platforms *** define various frameworks on smart farming and monitoring system and still lacks to define effective data management *** IoT-cloud systems involve massive structured and unstructured data,data optimization comes into the ***,this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization(ICISF-DDO),which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved ***,a conceptual framework of the proposed scheme and statistical design model has beenwell *** information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data *** simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%,reliability ratio of 98.63%,a coverage ratio of 99.67%,least sensor error rate of 8.96%,and efficient energy consumption ratio of 4.84%.

关键词： Smart farming data optimization information management internet of things cloud

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Machine Learning based Anomaly and Threat Detection System in Real Time Social Internet of Things using Dimensionality Reduction Techniques 2

Machine Learning based Anomaly and Threat Detection System i...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Jenifer, Anciline Piramu, Preethika S.K. Vels Institute of Science Technology & Advanced Studies Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798331505745

The Social Internet-of-Things (SIoT) is an innovative model developing in the struggle to safeguard privacy and increase trust inside the Internet-of-Things(IoT). The revolutionary notion of SIoT combines the IoT with social platforms, allowing inanimate items to develop relationships with one another. The fast growth of SIoT has transformed networked networks, enabling effortless interaction between devices. However, integrating these systems creates major security issues, including anomalies and attacks that may compromise system integrity. This study presents a machine learning(ML) methods like Isolation Forest (IF), One class support vector Machine(SVM) and Kernel density estimation(KDE) for in SIoT networks using the Principal component Analysis(PCA) and Correlation Analysis as dimensionality reduction techniques. The findings shows that the IF method provides an accuracy of 93% with the dimensionality reduction techniques applied on RT-IoT dataset. By utilizing powerful machine learning algorithms and contextual analytics, the framework improves detection accuracy and enables strong device collaboration in highly dynamic contexts.. © 2025 IEEE.

关键词： Support vector machines

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DataDriven Approaches for Early Detection and Prediction of Chronic Kidney Disease Using Machine Learning 23

DataDriven Approaches for Early Detection and Prediction of ...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Shibi Mathai K.S. Thirunavukkarasu Computer Science Vels Institute of Science Technology and Advanced Studies India

ISBN: (纸本)9798400709418

In recent years, the application of machine learning (ML) techniques for medical diagnostics has shown promising advancements. This study introduces a distinctive method for predicting chronic kidney disease (CKD) harnessing the prowess of ML. Our methodology encompasses an innovative data preprocessing approach, intricate feature engineering, and an amalgamation of ensemble techniques for model training. By evaluating our model on a dataset sourced from Kaggle, comprising 400 samples, we achieved an impressive accuracy of 98%, outperforming traditional methods. The findings underscore the potential of ML in revolutionizing CKD diagnostics, laying a foundation for further exploration in this domain.

关键词： Chronic Kidney Disease

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