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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Prakash Jayant, Khel Kumar Singh, Abhishek Thapliyal, Nitin Sharma, Prashant Goyal, Sunita Raj Kumar Goel Institute of Technology Computer Sciences & Engineering UP Ghaziabad India Shiva Institute of Management Studies Department of Computer Science UP Ghaziabad India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Gla University Department of Civil Engineering UP Mathura India Abes Engineering College Department of Applied Science & Humanities UP Ghaziabad India

ISBN: (纸本)9798350371055

Contemporary networks are complex structures that require superior algorithms to maintain their security. This paper explores the usage of superior algorithms to cozy networks, focusing on the improvement and deployment of efficient and comfortable algorithms for networked infrastructure. It examines current algorithms used to cozy networks, together with authentication and encryption algorithms, as well as a spread of rising technologies, which include artificial intelligence (AI) and gadget learning (ML). it'll study techniques to optimize the safety of the network through the improvement of smart protection architectures, as well as the demanding situations and opportunities posed by real-international implementations paper will offer a evaluation of various safety algorithms utilized in networks, and speak strategies for efficient deployment and integration in current networks. It'll define demanding situations related to the deployment of advanced algorithms on networks and provide a top level view of the modern nation of safety solutions and their implications for networked infrastructure. Sooner or later, it's going to offer guidelines for destiny studies to further optimize safety for allotted infrastructure. Networked Infrastructure security is a fundamental part of any contemporary gadget. To make certain that such systems are thoroughly covered from malicious actors, superior algorithms are frequently employed to optimize the overall safety of the community. These algorithms are used to identify and detect malicious activities, as well as offer anomaly detection competencies. Moreover, they frequently leverage synthetic intelligence techniques along with device gaining knowledge of and herbal language processing to carry out extra effective and accurate protection analysis. The usage of those algorithms can ensure complete security coverage, as well as improve reaction time to threats and vulnerabilities. Furthermore, it is able to provide an extra degree

关键词： algorithms deployment maximize networked protection

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Hyperledger Blockchain and Lightweight Bcrypt Symmetric Key Encryption to Boost Cloud Computing Security Effectiveness

Hyperledger Blockchain and Lightweight Bcrypt Symmetric Key ...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Banushri, A. Karthika, R.A. Vels Institute of Science Technology & Advanced Studies Dept. of Computer Science & Engineering Pallavaram Chennai India SRM Institute of Science & Technology Dept. of Computational Intelligence Kattangulathur Chennai India

ISBN: (纸本)9798350333244

One of the key business models of contemporary information technology is cloud computing. It offers users a range of services (hardware, software) with little user engagement and at a reasonable price. Utilising a cloud environment raises several challenges, chief among them being security and privacy. This study explores the issues and proposes a Lightweight Bcrypt Symmetric Key (LBSK) data encryption and decryption technique with key rotation. A variety of algorithms and Blockchain technologies are used to protect the information. Encryption is one of the most fascinating features of data security technology. An Aggregated Authority Certificate Provider (AACP) is recommended with blockchain authentication for secured cloud data audit, which lessens the burden on the data owner. The suggested AACP continuously runs on a cloud server and tracks user requests. For the non-trusted provider, the Hyperledger blockchain proxy re-encrypts data to increase protection and privacy. The Blockchain algorithm was used to encrypt the data from beginning to end, and it was then saved in the cloud. Without storing the data locally, a security degree, public verification, and performance factors must be taken into consideration. Compared to current methods, the proposed strategy is intended to deliver a better result. The Blockchain technique was used to encrypt the data from beginning to end, and it was then saved on the cloud. Public verification and performance considerations raise the security level for remote data verification without storing the data locally. In comparison to current methods, the proposed method offers a better result. © 2023 IEEE.

关键词： Blockchain

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Smart Job Scheduling Model for Cloud Computing Network Application

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SN computer science 2024年第1期5卷 1-12页

作者： Onyema, Edeh Michael Gude, Venkataramaiah Bhatt, Ashutosh Aggarwal, Ambika Kumar, Sunil Benson-Emenike, Mercy Eberechi Nwobodo, Lois Onyejere Department of Vocational and Technical Education Faculty of Education Alex Ekwueme Federal University Ndufu-Alike Abakaliki Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India GP Technologies LLC Texas United States Department of Computer Science and Engineering Shivalik College of Engineering Dehradun India School of Computer Science University of Petroleum and Energy Studies Dehradun India Department of Computer Science Federal University of Technology Owerri Nigeria Department of Computer Engineering Enugu State University of Science and Technology Enugu Nigeria

Task scheduling is one of the significant factors for heterogeneous type of elements in multi-cloud computing environment. It is to delegate activities to most adequate resources to raise the performance with respect to some dynamic parameters. The suggested model of scheduling was designed to run applications of cloud computing which applied in three steps (classifying, execution, minimization, and rating) and is assumed to be completion time, average waiting and turnaround time, and render duration as output parameters. The tasks’ execution time in cloud computing applications was created in the task scheduling model by exponential and normal distribution. The task ranking depends on the shortest strategy for First Job and the results are compared with other “Largest Processing Time” and “First Come First Serve” ranking method. Scheduling model proposed provides significant output than per performance parameters specified. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing Directed acyclic graph (DAG) Smart scheduling

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NPRportrait 1.0:A three-level benchmark for non-photorealistic rendering of portraits

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Computational Visual Media 2022年第3期8卷 445-465页

作者： Paul L.Rosin Yu-Kun Lai David Mould Ran Yi Itamar Berger Lars Doyle Seungyong Lee Chuan Li Yong-Jin Liu Amir Semmo Ariel Shamir Minjung Son Holger Winnemöller School of Computer Science and Informatics Cardiff UniversityCardiffUK School of Computer Science Carleton UniversityOttawaCanada Department of Computer Science and Technology Tsinghua UniversityBeijingChina Reichman University(the Interdisciplinary Center) HerzliyaIsrael Department of Computer Science and Engineering Pohang University of Science and TechnologyPohangRepublic of Korea Lambda Labs Inc.San FranciscoUSA Hasso Plattner Institute University of PotsdamPotsdamGermany Multimedia Processing Laboratory Samsung Advanced Institute of TechnologySuwonRepublic of Korea Adobe Systems Inc.San JoseUSA

Recently,there has been an upsurge of activity in image-based non-photorealistic rendering(NPR),and in particular portrait image stylisation,due to the advent of neural style transfer(NST).However,the state of performance evaluation in this field is poor,especially compared to the norms in the computer vision and machine learning ***,the task of evaluating image stylisation is thus far not well defined,since it involves subjective,perceptual,and aesthetic *** make progress towards a solution,this paper proposes a new structured,threelevel,benchmark dataset for the evaluation of stylised portrait *** criteria were used for its construction,and its consistency was validated by user ***,a new methodology has been developed for evaluating portrait stylisation algorithms,which makes use of the different benchmark levels as well as annotations provided by user studies regarding the characteristics of the *** perform evaluation for a wide variety of image stylisation methods(both portrait-specific and general purpose,and also both traditional NPR approaches and NST)using the new benchmark dataset.

关键词： non-photorealistic rendering(NPR) image stylization style transfer portrait evaluation benchmark

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Optimal Deep Learning Driven Intrusion Detection in SDN-Enabled IoT Environment

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computers, Materials & Continua 2023年第3期74卷 6587-6604页

作者： Mohammed Maray Haya Mesfer Alshahrani Khalid A.Alissa Najm Alotaibi Abdulbaset Gaddah AliMeree Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia SAUDI ARAMCO Cybersecurity Chair Networks and Communications DepartmentCollege of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Warwick UniversityCovetryUK Department of Computer Science Faculty of Computers and Information TechnologyFuture University in Egypt New Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

In recent years,wireless networks are widely used in different *** phenomenon has increased the number of Internet of Things(IoT)devices and their *** IoT has numerous advantages,the commonly-used IoT devices are exposed to cyber-attacks *** scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic *** Software-Defined Networking(SDN)technique and the Machine Learning(ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT *** Intrusion Detection System(IDS)models can be employed to secure the SDN-enabled IoT environment in this *** current study devises a Harmony Search algorithmbased Feature Selection with Optimal Convolutional Autoencoder(HSAFSOCAE)for intrusion detection in the SDN-enabled IoT *** presented HSAFS-OCAE method follows a three-stage process in which the Harmony Search Algorithm-based FS(HSAFS)technique is exploited at first for feature ***,the CAE method is leveraged to recognize and classify intrusions in the SDN-enabled IoT ***,the Artificial Fish SwarmAlgorithm(AFSA)is used to fine-tune the *** process improves the outcomes of the intrusion detection process executed by the CAE algorithm and shows the work’s *** proposed HSAFSOCAE technique was experimentally validated under different aspects,and the comparative analysis results established the supremacy of the proposed model.

关键词： Internet of things SDN controller feature selection hyperparameter tuning autoencoder

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Refining Pancreatic Cancer Detection and Classification through a Hybrid Approach: Stacked Deep Learning Integrated with the Sparrow Search Algorithm for Medical Image Analysis 2

Refining Pancreatic Cancer Detection and Classification thro...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Arunachalam, P. Devi, M. Kalpana Jose, Naduvathezhath Nessariose Vivekanandan, G. Sharma, Rinku Mahesh Yadav, Ganesh Kumar Department of Biomedical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology MCA Department Sreenivasa Institute of Technology and Management Studies Chittoor Architect Denken Solutions Department of CSE Sri Sairam Institute of Technology Chennai Department of Computer Science and Engineering SVKM Institute of technology Dhule Department of Computer Science and Engineering JSS Academy of Technical Education

ISBN: (纸本)9798350383867

In the endeavour to improve the precision and efficiency of diagnosing and classifying pancreatic cancer, there is a new idea that fuses deep learning and the Sparrow Search Algorithm with a stacked structure. That is why we consider this approach as revolutionary in terms of utilizing medical image analysis for the diagnosis in the field of pancreatic oncology with more accurate results. Transforming using stacked deep learning allows the systemto recursively learn its own complex feature in medical images, which is a basis for the identification of cancer. SBA or Sparrow Search Algorithm has been developed based on this social sparrow model and is a separate optimization technique which helps in optimizing the method of classification in this case. The foremost aim of this particular paper is to incorporate these two strategies and use the endproduct to improve the diagnostics of pancreatic cancer and also theclassification of the same. The cyclic process is applied together with the stacked deep learning process as well as the Sparrow Search Algorithm to make the systems' continuous optimization for sensitivity and specificity in detecting pancreatic malignancies. It is well to remember that this is not just a pure technological innovationthat holds promise of changing the landscape of pancreatic diagnosisand therapy. This approach of diagnosis and treatment also suggests early diagnosis, develop individual management plan and likely to yield higher survival rates for such patients. In addition, it is also worth considering at the potential that has been provided in enhancement of all sorts of branches of medicine linked to image analysis, along with the potential that has been developed when the best of technologies is paired with the best of ideas in computation. In conclusion, this paper captures the notion of intentionality and exigency in medical procedures. This union of stacked deep learningwith the Sparrow Search Algorithm unveils a new epoch in the medic

关键词： Deep learning

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SNCA:Semi-Supervised Node Classification for Evolving Large Attributed Graphs

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Big Data Mining and Analytics 2024年第3期7卷 794-808页

作者： Faima Abbasi Muhammad Muzammal Qiang Qu Farhan Riaz Jawad Ashraf Luxembourg Institute of Science and Technology and University of Luxembourg Esch-Sur-Alzette L-4362Luxembourg Department of Computer and Information Sciences Northumbria UniversityNewcastle NE18STUK Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055China School of Computer Science University of LincolnLincoln LN67TSUK Faculty of Computing Engineering and MediaDe Montfort UniversityLeicesterLE19BHUK

Attributed graphs have an additional sign vector for each ***,edge signs represent like or dislike relationship between the node *** has applications in domains,such as recommender systems,personalised search,***,limited availability of edge sign information in attributed networks requires inferring the underlying graph embeddings to fill-in the knowledge *** inference is performed by way of node classification which aims to deduce the node characteristics based on the topological structure of the graph and signed interactions between the *** study of attributed networks is challenging due to noise,sparsity,and class imbalance *** this work,we consider node centrality in conjunction with edge signs to contemplate the node classification problem in attributed *** propose Semi-supervised Node Classification in Attributed graphs(SNCA).SNCA is robust to underlying network noise,and has in-built class imbalance handling *** perform an extensive experimental study on real-world datasets to showcase the efficiency,scalability,robustness,and pertinence of the *** performance results demonstrate the suitability of the solution for large attributed graphs in real-world settings.

关键词： attributed networks node classification recommender systems

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Review of AI-Driven Machine Learning Solutions for Modern Agriculture

Review of AI-Driven Machine Learning Solutions for Modern Ag...

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Smart Technologies for Sustainable Development Goals (ICSTSDG), International Conference on

作者： K. Suganthavalli C. Meenakshi Department of Computer Applications Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Tamil Nadu India Department of Computer Applications Vels Institute of Science Technology & Advanced Studies(VISTAS) Chennai Tamil Nadu India

ISBN: (数字)9798331516284

ISBN: (纸本)9798331516291

Artificial intelligence (AI) has been more prevalent in the agricultural sector in recent years. Sector challenges, there are many obstacles to overcome to increase the crop production namely reduced soil preparation, pest invasion Insufficient data leading to wrong result prediction hence the poor output, and wide breach between farmers and evolving technologies. The proposal of applying AI techniques in agriculture is to ensure its flexibility, precision, reliability, and goodness. The study summarizes the many AI-based approaches to weed, disease, crop, and soil management solutions. Several approaches are used to increase the yield, and the benefits and drawbacks of the application are examined. To produce high yields of fruit, plants must have a perfect balance between vegetative development (such as root, stem, and leaf growth), reproductive growth (such as flower and fruit growth), NNs, and GA. This allows for the best nutrient concentration (NC) set points. One way to forecast plant growth is to look at the ratio of stem diameter to total leaf length.

关键词： Technological innovation Reviews Computational modeling Crops Soil Predictive models Reliability Sustainable development Monitoring Diseases

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An Effective Diagnosis of Alzheimer's Disease with the Use of Deep Learning based CNN Model 7

An Effective Diagnosis of Alzheimer's Disease with the Use o...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Sharmili, K.C. Suja, G.P. Pandian, E. Walid, Md. Abul Ala Arunachalam, Sripriya Babu, G.Charles Computer Science Stella Maris College 17 Cathedral Road Chennai India Computer Science Muslim Arts College Thiruvithancode629174 India Sri Muthukumaran Institute of Technology Mangadu Master of Computer Application Chennai69 India Department of Computer Science and Engineering Khulna9203 Bangladesh Computer Applications-PG School of Computing Sciences VELS Institute of Science Technology & Advanced Studies - VISTAS Velan Nagar P.V. Vaithiyalingam Road Pallavaram Chennai600 117 India GRIET Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350397253

In recent years, Alzheimer's disease has become a major health concern. Over 45 million individuals worldwide suffer from this illness. Alzheimer's disease is a neurodegenerative illness of unidentified etiology and process that causes cognitive deterioration, and it mostly affects the elderly. Dementia, the gradual and irreversible loss of brain cells, is the leading cause of Alzheimer's disease. Those with this illness lost the ability to think, read, and do other basic tasks. A machine learning system may aid in this situation by making accurate predictions about the onset of illness. Dementia diagnosis in a broad patient population is the major objective. This research presents the findings and analysis of many Machine Learning models for identifying dementia. To alleviate this obstacle and help in the diagnosis of AD will create a deep-learning architecture that uses stacked auto-encoders and a softmax output layer. The proposed technique can analyze numerous classes in a single setting, unlike earlier procedures, and only needs a small number of labeled training samples and basic domain expertise to get started. In the present studies, a substantial improvement in performance when it came to classifying all diagnostic subgroups. Using the proposed technique can run these time series through a Convolutional Neural Network (CNN) and ResNet50 model to extract the distinguishing patterns across stages. With an F1-score of 0.99 and an accuracy of 99.91 percent, the CNN-based technique outperformed the standard feature-based classifiers by a significant margin. © 2023 IEEE.

关键词： Convolutional neural networks

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FASALKA: Offloaded Privacy Classification for Blockchain Smart Contracts 6

FASALKA: Offloaded Privacy Classification for Blockchain Sma...

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6th International Conference on Blockchain Computing and Applications, BCCA 2024

作者： Devgun, Tannishtha Kumar, Gulshan Conti, Mauro School of Computer Science and Engineering Lovely Professional University Punjab India Department of Mathematics University of Padua Padua Italy International School of Advanced Studies University of Camerino Italy

ISBN: (纸本)9798350351538

Privacy problems in blockchain smart contracts arise from the inherent transparency of the technology. While blockchain ensures data integrity, it also exposes sensitive information to all participants. This lack of privacy can be a concern in industries requiring confidentiality, like finance or healthcare, prompting the need for privacy solutions in smart contracts. The existing smart contracts face the problems of over-utilization or under-utilization of privacy parameters due to the unavailability of online or offline privacy classification;smart contracts fall behind in solving privacy issues. In this paper, we introduce the first privacy classification framework for smart contracts. We call our framework oFfloaded privAcy-classified Smart contrAct for bLocKchAins (FASALKA). To be specific, FASALKA runs a novel privacy-ensured smart contract that includes a novel hybrid learning mechanism. This hybrid learning mechanism combines the potential of federated learning and reinforcement learning. We deploy Ethereum on Azure and run a set of experiments to measure the performance of our proposed FASALKA. We compare a general Ethereum framework with an Ethereum framework using our proposed FASALKA. We observe that the Ethereum framework shows 1.1% more latency than the general Ethereum;however, our proposed framework has a similar throughput of 21 TPS. Besides, FASALKA has the 100% accuracy of privacy classification, which is not present in general Ethereum. Thus, our proposed FASALKA is efficient and beneficial for the privacy-ensured blockchains. © 2024 IEEE.

关键词： Smart contract

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