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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： B Uppalaiah Anjum Ara Ahmad A. Annie Lotus Abdul Hameed Al-Shammari Sharmeen Izzat Hassan Dhaaraani R Department of Mathematics Hyderabad Institute of Technology and Management Hyderabad India Department of Statistics Rizvi College of Arts Science & Commerce Mumbai Maharashtra India Department of Mathematics Sona College of Technology Salem India Uruk University Baghdad Iraq Department of Computer Engineering College of Engineering Knowledge University Erbil Iraq Department of AI & DS K. Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

Time series Forecasting plays a major role mainly in areas including finance, health, and meteorology. Although, classical forecast methods, including ARIMA models and feedforward neural networks, fail at capturing long-term information and non-linear interactions in temporal systems. This paper explores the application of LSTM networks in improving forecasting accuracy while overcoming the limitations of traditional methods. The contribution of this work is in applying of LSTM models into complex mathematical frameworks capable to work with time-series data with minimal efforts required in feature engineering. A plan of the proposed framework is as follows: LSTM model training on the given time-series data and comparison with ARIMA and feedforward neural networks that leads to the comparison of results based on performance metrics like MAE, RMSE and MAPE. Overall, out of all analysed methods, LSTM model provides the lowest values of mean absolute error equal to 8.12, root mean squared error equal to 10.23 and mean absolute percentage error equal to 5.29 % which proves how good it was in dealing with data which contains relations with a more non-linear nature and experiencing long dependencies. Such results suggest that LSTMs are significantly superior to traditional approaches as a new method of time-series analysis and forecasting for complex systems. It is important to discuss the advantages of LSTM based on the aspect of the capability of being scaled and trainable when it comes to time-series forecasting and its demanding computation resource and training data.

关键词： Training Recurrent neural networks Time series analysis Weather forecasting Training data Mathematical models Data models Feedforward neural networks Forecasting Long short term memory

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Brain Tumor Detection using Deep Features

Brain Tumor Detection using Deep Features

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Evolutionary Algorithms and Soft Computing Techniques (EASCT), International Conference on

作者： Rahul Nijhawan Rajendra Singh Bisht Mukesh Pandey Sanyam Kumar Neha Mendirtta Poojita Sagar Awasthi Computer Science Engineering Thapar Institute of Engg. & Technology Patiala India Department of CSE Graphic Era Hill University Bhimtal India School of Computer Science University of Petroleum & Energy Studies Dehradun India Medical Knowledge Group New York City USA Chandigarh University Punjab India Computer Science Engineering University of Petroleum & Energy Studies Dehradun India

An excessive proliferation of aberrant cells in the brain, which may result in a brain tumor. Moreover, it is very difficult for doctors to detect brain tumors at an early stage. However, with the help of MRI images, they can now monitor the condition. Due to the complexity of the disease, it is very difficult for doctors to determine its exact causes. With the help of brain tumor images, we can now monitor the condition and identify the possible cause of the disease. In this paper, we have proposed a deep learning technique for brain tumor detection. We employed a pre-trained VGG-16 architecture for detection of brain tumour. Our proposed approach outperformed the state-of-the-art algorithms with an accuracy of 86.6%. We compared the results by computing different statistical measures such as AUC, precision, recall, and F1 score.

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A Conditional Perspective on the Logic of Iterated Belief Contraction

arXiv

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arXiv 2022年

作者： Sauerwald, Kai Kern-Isberner, Gabriele Beierle, Christoph FernUniversität in Hagen Faculty of Mathematics and Computer Science Knowledge-Based Systems Hagen58084 Germany TU Dortmund University Department of Computer Science Information Engineering Dortmund44221 Germany

Classical AGM belief change theory provides only limited information on how to deal with iterative belief revision, leading to the question of how appropriate iteration principles look alike. The contributions to the interconnection between iterative belief revision and conditional beliefs lead to fundamental insights on that matter. Besides of clarifying to what amount iterated revision should respect conditional beliefs, these investigations identified several principles for iterated revision that are today widely accepted by the belief change community and beyond. Specifically, due to the seminal work by Darwiche and Pearl, it is standard, that principles for iterated revision and change of conditional beliefs are considered as two sides of the same coin. However, for iterated contraction the situation is different, there are only a small amount of iteration principles present in the literature. Moreover, for none of these iterated principles for contraction, there is a known equivalent formulation in terms of change of conditional beliefs. In this article, we consider iteration principles for contraction, with the goal of identifying properties for contractions that respect conditional beliefs. Therefore, we investigate and evaluate four groups of iteration principles for contraction which consider the dynamics of conditional beliefs. For all these principles, we provide semantic characterization theorems and provide formulations by postulates which highlight how the change of beliefs and of conditional beliefs is constrained, whenever that is possible. The first group is similar to the syntactic Darwiche-Pearl postulates. As a second group, we consider semantic postulates for iteration of contraction by Chopra, Ghose, Meyer and Wong, and by Konieczny and Pino Pérez, respectively, and we provide novel syntactic counterparts. Third, we propose a contraction analogue of the independence condition by Jin and Thielscher. For the fourth group we consider natural and

关键词： Iterative methods

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TJCCT: A Two-timescale Approach for UAV-assisted Mobile Edge Computing

arXiv

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arXiv 2024年

作者： Sun, Zemin Sun, Geng Wu, Qingqing He, Long Liang, Shuang Pan, Hongyang Niyato, Dusit Yuen, Chau Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China The School of Information Science and Technology Northeast Normal University Changchun130024 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Electrical and Electronics Engineering Nanyang Technological University Singapore639798 Singapore The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is emerging as a promising paradigm to provide aerial-terrestrial computing services in close proximity to mobile devices (MDs). However, meeting the demands of computation-intensive and delay-sensitive tasks for MDs poses several challenges, including the demand-supply contradiction between MDs and MEC servers, the demand-supply heterogeneity between MDs and MEC servers, the trajectory control requirements on energy efficiency and timeliness, and the different time-scale dynamics of the network. To address these issues, we first present a hierarchical architecture by incorporating terrestrial-aerial computing capabilities and leveraging UAV flexibility. Furthermore, we formulate a joint computing resource allocation, computation offloading, and trajectory control problem to maximize the system utility. Since the problem is a non-convex and NP-hard mixed integer nonlinear programming (MINLP), we propose a two-timescale joint computing resource allocation, computation offloading, and trajectory control (TJCCT) approach for solving the problem. In the short timescale, we propose a price-incentive model for on-demand computing resource allocation and a matching mechanism-based method for computation offloading. In the long timescale, we propose a convex optimization-based method for UAV trajectory control. Besides, we theoretically prove the stability, optimality, and polynomial complexity of TJCCT. Extended simulation results demonstrate that the proposed TJCCT outperforms the comparative algorithms in terms of the system utility, average processing rate, average completion delay, and average completion ratio. Copyright © 2024, The Authors. All rights reserved.

关键词： Computation offloading

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Cloud Forensics Analysis Framework for Secure and Efficient Data Retrieval

Cloud Forensics Analysis Framework for Secure and Efficient ...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Revathi V Bk Aishwarya Manju Tripathi Navdeep Singh Haider Alabdely Ashwani Kumar Department of Applied Sciences New Horizon College of Engineering Bangalore Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Science & Engineering IES College of Technology Bhopal M. P. India Lovely Professional University Phagwara The Islamic university Najaf Iraq Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This study’s Cloud Forensics Analysis Framework seeks secure and effective data recovery methods. The framework’s five strategies make digital evidence retrieval methodical and versatile. These include adaptive evidence collecting, secure data retrieval, machine learning-based anomaly detection, intelligent automation, and context-aware metadata analysis. Each approach targets a distinct forensic process step. This makes cloud forensics investigations seem difficult. The customizable Evidence gathering Algorithm adapts to cloud characteristics to collect evidence. This allows contextually aware and flexible digital evidence retrieval. The secure Data Retrieval Techniques Algorithm protects data privacy and chain of custody with advanced encryption and validation. The Machine Learning-Based Anomaly Detection Algorithm checks protected data for anomalies before sending it, making the system safer. Investigators may focus on more complex cases by simplifying and sorting basic forensic activities with the Intelligent Automation Algorithm. The Context-Aware Metadata study Algorithm weights and prioritizes cloud metadata. This completes metadata analysis. Compared to other approaches, the framework has greater Precision, Recall, F1 Score, Processing Time, Resource Utilization, and Compatibility Score. Charts convey system operation in a simple manner. This powerful and adaptable Cloud Forensics Analysis Framework improves cloud forensics by providing a thorough and effective solution to recover and safeguard data. The Cloud Forensics, This paper discusses context-aware metadata analysis, data retrieval, intelligent automation, machine learning-based anomaly detection, performance metrics, secure data retrieval techniques, support vector machines, and visual representation.

关键词： Intelligent automation Support vector machines Visualization Technological innovation Machine learning algorithms Forensics Systems operation

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Real-time Settings and Their Transformation through Distributed Intelligence Communication Technologies

Real-time Settings and Their Transformation through Distribu...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Manjunatha M Hari Krishna Vijay Kumar Rai Navdeep Singh Ashwani Kumar Mohammed I. Habelalmateen Department of Mechanical Engineering New Horizon College of Engineering Bangalore Institute of Aeronautical Engineering Dundigal Hyderabad India Department of Computer Science & Engineering IES College of Technology Bhopal M. P India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh The Islamic university Najaf Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

Modern real-time settings and dispersed intelligence communication technologies require new solutions to improve efficiency, flexibility, and scalability. This research introduces dynamic edge-cloud orchestration for real-time intelligence. The approach uses the Dynamic Task Allocation Algorithm (DTAA), the Predictive Resource Scaling Algorithm (PRSA), and the Adaptive Communication Routing Algorithm. DTAA distributes work between edge devices and the cloud based on priority, processing time, and resources. PRSA estimates future resource needs, making edge and cloud scaling easier. ACRA adjusts communication routes based on latency, job priority, and network circumstances. We use numbers to compare the new strategy to six popular ones. These include SDA, TFA, RBI, FBA, and PRT. The proposed option outperforms the others in latency, scalability, security, reliability, freedom, and resource utilization. Due to its comprehensive framework for smart task assignment, resource management, and communication enhancement, DECO-RTI is a major advance in real-time settings. The approach can adapt to changing situations and automatically scale up resources, making it ahead of the curve for distributed intelligent communication systems.

关键词： Heuristic algorithms Scalability computer network reliability Prediction algorithms Routing Dynamic scheduling Real-time systems

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Assessing the Societal Impacts of Emerging Distributed Intelligence Technologies

Assessing the Societal Impacts of Emerging Distributed Intel...

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Irfan Khan V Alekhya B Rajalakshmi Sorabh Lakhanpal Mohammed Ayad Alkhafaji K. Santhi Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Institute of Aeronautical Engineering Dundigal Hyderabad Department of Computer Science New Horizon College of Engineering Bangalore Lovely Professional University Phagwara College of Technical Engineering National University of Science and Technology Dhi Qar Iraq Dept. of IT St. Martin’s Engineering College Secunderabad Telangana India

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

The Integrated Impact Assessment Framework (IIAF) is established in this study to examine how new distributed intelligence technologies (DIT) influence society. The framework comprises three powerful algorithms: SNDA, EIS, and EIAM. These illustrate DIT’s various benefits. SNDA examines the complex social web, identifying hubs, community dynamics, and future trends. EIS predicts economic repercussions and provides GDP, expenditures, and job losses. computer faults and privacy are important ethical considerations for EIAM. These strategies paint a comprehensive picture of the effect. Fake data suggests the new strategy outperforms six others. The recommended strategy scores higher on significance, economic indicators, and ethics. The IIAF’s DIT impact evaluation is more complete, educated, and responsible than other techniques. This work contributes to our understanding of the problem by proposing a flexible and resilient paradigm for dealing with the societal consequences of diverse technologies. The IIAF may demonstrate to politicians the immediate impacts of DIT adoption and discuss morality and society. The framework’s holistic approach helps us understand how technology and society interact and evolve.

关键词： Ethics Technological innovation Privacy Economic indicators Reviews Heuristic algorithms Prediction algorithms

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Spatial Modulation Techniques for Improved ISAC Throughputs

Spatial Modulation Techniques for Improved ISAC Throughputs

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Emerging Trends in Industry 4.0 (ETI 4.0)

作者： Sanjay Kumar Suman B Rajalakshmi Irfan Khan V Alekhya Sorabh Lakhanpal Abduljabbar A. Ali St. Martin’s Engineering College Secunderabad Telangana Bharat Department of Computer Science New Horizon College of Engineering Bangalore Knowledge Park II Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh Institute of Aeronautical Engineering Hyderabad Dundigal Lovely Professional University Phagwara College of Dentist National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

This study introduces “Adaptive Spatial Modulation for Enhanced ISAC Throughputs (ASM-ISAC)” to speed up Intelligent Space-Air-Ground Communication (ISAC). An adaptive spatial modulation technique, CSD for cooperative spatial diversity, and EESM-ISAC for energy-efficient spatial modulation are used. To boost communication throughput and reduce bit error rates, ASM-ISAC adjusts modulation and spatial patterns in real time. Cooperation between close ISAC nodes improves performance in CSD. EESM-ISAC develops energy-efficient communication with restricted resources. This research evaluates ASM-ISAC against earlier approaches like “Conventional Modulation Scheme for ISAC (CMS-ISAC)”. The test evaluates frequency efficiency, error performance, latency, complexity, energy efficiency, and security. ASM-ISAC outperforms CMS-ISAC in several aspects, suggesting it might increase ISAC throughput.

关键词： Technological innovation Frequency modulation Spatial diversity Modulation Throughput Energy efficiency Real-time systems

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Multidimensional Impacts of Generative AI and an In-Depth Analysis of LLMs with Their Expanding Horizons in Technology and Society

Multidimensional Impacts of Generative AI and an In-Depth An...

引用

Emerging Trends in Industry 4.0 (ETI 4.0)

作者： K Ramu Ginni Nijhawan G Lalitha Praveen V Asha Ahmed J Fakher Dept. of CSE Velammal Institute of Technology Chennai TN India Lovely Professional University Phagwara Institute of Aeronautical Engineering Dundigal Hyderabad Lloyd Institute of Engineering & Technology Knowledge Park II Greater Noida Uttar Pradesh Department of Computer Applications New Horizon College of Engineering Bangalore Department of Forensic Sciences College of Science National University of Science and Technology Dhi Qar Iraq

ISBN: (数字)9798350373783

ISBN: (纸本)9798350373790

Generative AI and large language models (LLMs) have transformed technology and society, as this paper details. Generative AI advanced AI greatly. Because they can read and write, LLMs, a form of Generative AI, have altered how humans and robots communicate. This study focuses on how these technologies influence schooling, healthcare, finance, and morals. Recent studies and case studies will help us understand how LLM is applied in many sectors. Our key concerns with these activities are efficiency, efficacy, and morality. The technique also polls and interviews professionals to learn about LLM usage and issues. LLM is quicker and more precise in translating languages, creating content, and analyzing data. Sadly, prejudice, privacy, and abuse issues persisted. LLMs are versatile enough to be utilized in odd fields like individualized learning and mental health treatment, according to the research. LLMs and creative AI can advance technology and society. Despite their benefits, they pose moral and practical issues that must be addressed. This study seeks a compromise between the two LLM methods. A more moral, research-focused use might make them more beneficial and less hazardous.

关键词： Surveys Performance evaluation Ethics Privacy Generative AI Reviews Large language models

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Multi-site MRI classification using Weighted federated learning based on Mixture of Experts domain adaptation

Multi-site MRI classification using Weighted federated learn...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Bai, Tian Zhang, Yingfang Wang, Yuzhao Qin, Yanguo Zhang, Fa Jilin University College of Computer Science and Technology Changchun China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China The Second Hospital of Jilin University The Department of Orthopaedics Changchun China Beijing Institute of Technology School of Medical Technology Beijing China

ISBN: (纸本)9781665468190

Deep learning often requires large amounts of data from different institutions. Federated learning, as a distributed training framework, enables multiple participants to collaboratively train models without collecting data together and hence protecting data privacy, but the datasets from different institutions usually bring the problem of domain shift, which affects the performance of the model. When addressing domain shift, previous works often use a single global model to share parameters. Therefore, we propose a novel method to train multiple public models with different structures under the federated framework to improve the reliability and robustness of the public models. And each participant keeps its own domain-tuned private model, the private model does not share parameters with other participants. We use Mixture of Experts (MoE) domain adaptation to dynamically combine different public models and private model, which utilizes the similarity between different datasets to update the parameters of the public models. We apply the proposed method to the multi-site Magnetic resonance imaging (MRI) end-to-end classification, and the experiments demonstrate its effectiveness. © 2022 IEEE.

关键词： Magnetic resonance imaging

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