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检索条件"机构=Department of Computer Science and Information and Communication Technology"

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Automated Detection of Alzheimer's Disease Using EfficientNet-B3 Architecture with Transfer Learning 25

Automated Detection of Alzheimer's Disease Using EfficientNe...

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25th International Arab Conference on information technology, ACIT 2024

作者： Aburomman, Ahmad Abuowaida, Suhaila Al-Momani, Adai Al Henawi, Esraa Elsoud, Esraa Abu Salah, Zaher Alzoubi, Haneen Chan, Huah Yong Khouj, Mohammed Zarqa University Department of Data Science and Ai Zarqa Jordan Al-Bayt University Department of Computer Science Ai Mafraq Jordan Zarqa University Department of Computer Science Zarqa Jordan The Hashemite University Department of Information Technology Zarqa Jordan Applied Science Private University Faculty of Information Technology Amman Jordan Universiti Sains Malaysia School of Computer Sciences Pulau Pinang Gelugor11800 Malaysia University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

In order to bring forward expansion in both the accuracy and the processing speed of the detection of Alzheimer's disease, an automated approach for Alzheimer's disease diagnosis with passable EfficientNet-B3 neural network and transfer learning techniques is provided in this work. After direct fitness on a large number of annotated brain images, the model of EfficientNet-B3 is further trained, in an effort to obtain worse depicted quality connected with Alzheimer's Disease. To hasten the process of training and to make the model fit for this specific task, transfer learning was employed. The results of these experiments prove the efficiency of our approach to the diagnostic Alzheimer's disease compared to the most advanced and regular deep learning methods. Thus, the model may also withstand the test amidst diverse clinical populations by performing well in dementia classification with precision and recall performance coupled with over 99% accuracy. These findings demonstrate how the EfficientNet-B3 architecture and transfer learning enhance generalization across. © 2024 IEEE.

关键词： Image annotation

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Fair and communication-Efficient Personalized Federated Learning 18th

Fair and Communication-Efficient Personalized Federated Lea...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Zheng, Yifu Zheng, Xu Wang, Tingqi Mu, Chong Zhakiyev, Nurkhat School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Department of Science and Innovation Astana IT University Astana Kazakhstan

ISBN: (纸本)9783031714665

Personalized Federated Learning (pFL) is among the most popular tasks in distributed deep learning, which compensates for mutual knowledge and enables device-specific model personalization. However, the effectiveness of pFL is severely impeded by challenges on fairness and significant communication overhead, where devices holding essential samples have to devote extensive resources for model training. To address these issues, we introduce the Fair and communication-Efficient Personalized Federated Learning (FCE-PFL) framework, which harmonizes performance and device fairness while maintaining communication costs. Based on the Dempster-Shafer Theory, FCE-PFL employs assistance and contribution metrics to quantify the auxiliary information a client receives and provides. Then FCE-PFL balances and adjusts the involvement of devices in each training iteration, so as to facilitate fair training by constraining the maximum resource consumption per device and reduce the overall communication overhead. Our framework has been proven to be superior to existing methods in accuracy through rigorous experiments on the CIFAR10 and CIFAR100 datasets, demonstrating its potential as a fair and efficient pFL solution. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Cloud Deployment Unveiled: A Security Model Focused on Industry

Cloud Deployment Unveiled: A Security Model Focused on Indus...

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2023 International Conference on Advances in Computation, communication and information technology, ICAICCIT 2023

作者： Yadav, Rakesh Kumar Natya, S. Mahesh, T.R. Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India

ISBN: (纸本)9798350344387

The Cloud Compute Adoption Architecture (CCAF), a unique architecture designed to improve safety of the business cloud installations, is introduced in this research article. Using router, authentication and authorization, and encryption methods embedded in Industrial File Sync/Share systems, CCAF aims to provide a multi-layered privacy strategy. Our paper describes the creation of this security A system, examines relevant literature, and explains the reasoning behind CCAF. Through testing and thorough analysis of the key technologies, the study assesses the effectiveness of the CCAF security layered approach. The intricate security system in place at CCAF proved to be an outstanding tool during penetration testing, detecting and eliminating 99.95% of worms including trojans, and even deactivating over 85% of them after 100 hours of continuous attack. Of particular note was the operating system's lightning-fast reaction time of less than 0.012 seconds per trojan and virus, which allowed for the early detection and mitigation of threats. Moreover, each try at SQL injection was successfully thwarted by the CCAF's extensive security defenses, guaranteeing strong security of data. The article highlights the usefulness of CCAF by exploring how its multi-layered security is seamlessly integrated into current regulations, services, and corporate processes. The research highlights how important CCAF's complex security is for Big Data applications running in the cloud, especially when it comes to scalability, speed, and veracity. The study's conclusions highlight CCAF's legitimacy and significant contributions to the field, demonstrating its applicability and effectiveness in protecting the cloud. © 2023 IEEE.

关键词： Cloud Compute Adoption Framework (CCAF) encryption Enterprise File Sync firewall identity management multilayered security approach penetration testing and SQL injection assessment security technologies Share solutions

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Exploring Spatio-Temporal Context with Recurrent Neural Networks for Medical Image Analysis

Exploring Spatio-Temporal Context with Recurrent Neural Netw...

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2024 International Conference on Optimization Computing and Wireless communication, ICOCWC 2024

作者： Bhuvana, J. Srivastav, Vaibhav Kumar, Sunil Department of Computer Science and Information Technology Bangalore India Computer Science & Engineering Vivekananda Global University Jaipur India College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India

ISBN: (纸本)9798350383348

The Recurrent Neural Networks (RNNs) software for scientific image analysis poses both an assignment and an opportunity to discover the spatiotemporal context to perceive significant patterns within scientific images. RNNs are capable of capturing lengthy-term dependencies in high-dimensional statistics, which has made them appropriate for use in clinical photograph analysis. additionally, they own a hierarchical architecture making it feasible to capture each low-level and excessive-level capabilities in a unmarried version. As such, RNNs can be used to extract spatiotemporal features from scientific images and contain the understanding of preceding frames with current frames to refine the know-how of a given frame. it allows RNNs to focus the eye of the version on applicable regions within the photo that denote a circumstance of hobby or to combine temporal statistics over a sequence of a couple of snapshots related to a particular affected person. This paper will discover the unique strategies and opportunities for utilizing RNNs for clinical photo evaluation and discuss a number of the demanding situations related to this approach. © 2024 IEEE.

关键词： associated hierarchical incorporate information possibilities

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Optimizing Big Data Algorithms on Cloud for Automated Disease Diagnosis in Healthcare System 2

Optimizing Big Data Algorithms on Cloud for Automated Diseas...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rai, Vijendra Sharma, Sumit Kumar Gupta, Richa Kumar, Rakesh Sethi, Tarun Information Technology Abes Institute of Technology UP Ghaziabad India Ajay Kumar Garg Engineering College Department of Information Technology UP Ghaziabad India Raj Kumar Goel Institute of Technology Electronics & Communication Engineering UP Ghaziabad India Gla University Department of Computer Engineering and Applications UP Mathura India Department of Computer Applications Iamr Duhai UP Ghaziabad India

ISBN: (纸本)9798350371055

Massive data Algorithms have the potential to revolutionize healthcare with the aid of making scientific diagnoses quicker., greater correct., and greater value efficient. Cloud-primarily based platforms are increasingly more getting used to save and procedure huge portions of scientific facts., and this allows optimized and automatic selection-making. by way of incorporating massive facts Algorithms., healthcare professionals can analyze massive volumes of scientific facts and discover diseases in a more correct and timely manner. computerized sickness diagnosis can reduce misdiagnosis and the need for useless treatments., main to higher high-quality of healthcare and reduced clinical fees. Consequently., its miles essential to optimize massive facts Algorithms on cloud for automatic ailment diagnosis in healthcare structures. Specific techniques hired include data modelling., statistics compression algorithms., and machine getting to know techniques. Facts modelling let in efficient summarization of huge volumes of information., and information compression techniques permit efficient garage., retrieval., and evaluation. Machine studying strategies such as clustering techniques and category algorithms can further beautify the accuracy of prognosis. All of these optimization strategies in the end offer healthcare expert with accelerated accuracy and progressed efficiency of prognosis. The current surge of crypto currencies and initial Coin offerings (ICOs) has created an abundance of big data.. This paper investigates the feasibility of optimizing massive information algorithms at the cloud for computerized disorder prognosis in a healthcare gadget. First., this paper explores the capacity advantages of cloud computing., along with accelerated computational power., scalability., and fee-performance at the cloud. Outcomes advocate that cloud-based totally optimization can growth the accuracy of ailment diagnoses by means of imparting extra information for extra corre

关键词： Analysis Healthcare Incorporating Predictions Selection

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Powering Next-Generation Wireless communication With Cooperative Strategies

Powering Next-Generation Wireless Communication With Coopera...

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

作者： Nair, Amrutha V Panduranga Rao, M.V. Agrawal, Trapty Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka 562112 India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798350357769

Recent research has been focused on RF energy transmission and recovery as a potential strategy to greatly increase the lifespan of resource wireless networks. This method has the ability to remotely supply items with vitality that is comparatively steady and prolonged;as a result, it introduced a new methodological approach known as WPC, that had created numerous new research avenues with broad uses. In these, the creation and evaluation of effective joint plans has recently garnered a lot of study attention. The collaboration continuum communicating initiatives for behavioral connectivity networks, the collaborating crosstalk techniques forward into mobile broadband network security, and the explaining policies and procedures for wap data transmission are all highlighted in this article's general introduction of working in multiple approaches. Again once we wrap up this piece, we also highlight a number of intriguing study paths in this field. © 2023 IEEE.

关键词： Cognitive Radio Networks Collaborative Strategies Cooperative Jamming and Network Topologies Energy Harvesting Relay Selection Secure communications Spectrum Sharing Wireless-Powered communications Energy Transmission

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Translational Deep Learning-Based Diagnostics: Enhancing Pathology With AI

Translational Deep Learning-Based Diagnostics: Enhancing Pat...

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2023 International Conference on Advances in Computation, communication and information technology, ICAICCIT 2023

作者： Ashwini, B. Sinha, Deepak Kumar Chaudhary, Chetan Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering & Technology Uttar Pradesh India

ISBN: (纸本)9798350344387

Transformative innovation in healthcare is led by the constantly changing field of deep learning techniques, especially designed for cellular imaging in diagnostic pathology. This paper provides a thorough examination of several deep learning approaches in the field of pathology, highlighting new uses and tackling important issues that are essential to the field's progress. The successful deployment of AI in clinical settings depends on the seamless integration of these apps into processes in digital pathology. This all-inclusive diagnostic method not only simplifies processes but also provides pathologists with cutting-edge instruments that accelerate work, improve diagnosis accuracy, and lower mistakes. The US has recently approved a limited number of manufacturers' digital pathology products for diagnostic usage, which offers a strong basis for the creation of useful AI solutions. Researchers, physicians, business executives, government agencies, and patient advocacy organizations working together will make it much easier for these cutting-edge technologies to spread and become widely used by medical professionals. These technologies usher in a new age in healthcare, distinguished by their exceptional performance, faster processing rates, cost effectiveness, increased accuracy, and strict safety regulations. AI and computational pathology will continue to develop in harmony, opening the door to a new age of improved healthcare delivery. © 2023 IEEE.

关键词： Artificial Intelligence Cellular Imaging Deep Learning Diagnostic Imaging and Machine Intelligence Diagnostics Digital Pathology Healthcare Pathology

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An Approach for Efficient Experts Selection in Team Formation Problems 2nd

An Approach for Efficient Experts Selection in Team Format...

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2nd International Conference on Advanced communication and Intelligent Systems, ICACIS 2023

作者： Shingade, Sandip Niyogi, Rajdeep Department of Computer Science and Engineering Indian Institute of Technology Roorkee Roorkee India Department of Computer Engineering and Information Technology Veermata Jijabai Technological Institute Mumbai India

ISBN: (纸本)9783031451232

This paper addresses the team formation problem for task-oriented projects, where experts are selected based on performance. The tasks are assigned priorities based on the experts’ performance, and subsets of experts are optimized to perform the priority tasks. The proposed system is designed to handle uncertainties, such as when experts leave after being assigned a task. An efficient replacement expert is found, and an optimized subset of expert teams is identified. The proposed method is evaluated using task and expert datasets. It demonstrates robustness in creating teams that can complete tasks efficiently, even when there are unexpected changes in team composition. Overall, our approach successfully designs algorithms for team formation problems with robustness properties, leveraging a given set of experts, and the results show the effectiveness of our approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Expert Division Task Team Formation Uncertainty

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Complexity Results and Algorithms for Preferential Argumentative Reasoning in ASPIC+ 21

Complexity Results and Algorithms for Preferential Argumenta...

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21st International Conference on Principles of Knowledge Representation and Reasoning, KR 2024

作者： Lehtonen, Tuomo Odekerken, Daphne Wallner, Johannes P. Järvisalo, Matti Department of Computer Science Aalto University Finland Department of Information and Computing Sciences Utrecht University Netherlands National Police Lab AI Netherlands Police Netherlands Institute of Software Technology Graz University of Technology Austria Department of Computer Science University of Helsinki Finland

ISBN: (纸本)9781956792058

We provide complexity results and algorithms for reasoning in the central structured argumentation formalism of ASPIC+. Considering ASPIC+ accommodated with preferences under the last-link principle, the results are made possible by rephrasing several argumentation semantics—admissible, complete, stable, preferred and grounded—in terms of defeasible elements of an ASPIC+ theory for both democratic and elitist last-link lifting. Via the rephrasing, we establish that acceptance is polynomial-time computable under grounded semantics, and complete for either NP, coNP, or ΠP2 depending on the reasoning mode and semantics. We also detail answer set programming encodings for deciding acceptance for the NP/coNP-complete reasoning tasks and empirically show that it scales significantly better than first translating ASPIC+ reasoning tasks to abstract argumentation. Finally, we show that, in contrast to the last-link principle, it is NP-hard to compute the grounded extension under the weakest-link principle. © 2024 Proceedings of the International Conference on Knowledge Representation and Reasoning. All rights reserved.

关键词： Knowledge representation

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Service-Centric Architectures in Cloud Security: An In-Depth Analysis

Service-Centric Architectures in Cloud Security: An In-Depth...

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2023 International Conference on Advances in Computation, communication and information technology, ICAICCIT 2023

作者： Natya, S. Vivek, V. Yadav, Dhananjay Kumar Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Faculty of Engineering and Technology Department of Computer Science Engineering Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India

ISBN: (纸本)9798350344387

Due to its affordable and high-quality services, cloud computing has lately attracted a lot of interest. Due to the on- demand, pay-per-use characteristics that cloud services provide, which encourage companies to outsource parts of their operations with better service delivery and more value, they have become an essential component of both businesses et people's everyday lives during the last ten years. In the next years, a continued migration towards cloud-based services is predicted by the current market trend, which started in 2019. Despite the many benefits that the cloud computing paradigm offers to both enterprises and consumers, security issues are expected to be the biggest cloud computing challenges in 2020. Security problems are a result of a number of variables, including the streaming nature of these solutions and the technology that support cloud computing, which includes hybridization and multitenancy. This research examines security concerns in service-driven cloud computing in order to shed light on the situation of the industry at the moment. This paper's main contribution is an analysis of cloud security over the last ten years and the presentation of a thorough taxonomy of security concerns throughout the three-layer architecture, which includes Infrastructure-as-a-Service (IaaS), Platform-as- (PaaS), and Software-as-a-Service (SaaS). © 2023 IEEE.

关键词： Cloud computing Hypervisor Infrastructure-as-a-Service (IaaS) Multitenancy Platform-as-a-Service (PaaS) Security issues Service-driven cloud computing Software-as-a-Service (SaaS) and Denial of Service (DoS) Virtualization

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