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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Abdel-Nabi, Heba Ali, Mostafa Z. Awajan, Arafat Alazrai, Rami Daoud, Mohammad I. Suganthan, Ponnuthurai N. Reynolds, Robert Princess Sumaya University for Technology Department of Computer Science Amman Jordan Jordan University of Science & Technology Faculty of Computer & Information Technology Irbid Jordan Mutah University Computer Science Department Karak Jordan German Jordanian University Department of Computer Engineering Amman Jordan School of Electrical & Electronic Engineering Nanyang Technological University Singapore Kindi Center for Computing Research College of Engineering Qatar University Doha Qatar College of Engineering Wayne State University Detroit United States

ISBN: (纸本)9798350314588

This paper proposes a novel hybridization of two metaheuristic algorithms to solve the real-parameter single objective numerical optimization problems. The proposed Three-stage Cyclic Hybrid SFS and L-SHADE (3-sCHSL) algorithm integrates the high-level interactions of Stochastic Fractal Search (SFS) and L-SHADE algorithms in a single framework. Furthermore, a guided control population initialization strategy is injected in 3-sCHSL to address the stagnation and diversity loss issues as the evolution process evolves. The performance of the proposed algorithm is tested under different complexity levels of different transformations of the CEC 2021 benchmark suite with dimension 20. The experimental results demonstrated the efficiency and competitiveness of the proposed algorithm against recent state-of-the-art algorithms. In addition, 3-sCHSL achieved a superior performance when evaluating two engineering design problems. © 2023 IEEE.

关键词： Optimization

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Precision Enhanced Breast Cancer Prediction Using Deep Learning Models 1

Precision Enhanced Breast Cancer Prediction Using Deep Learn...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Kunchapu, Abhiram Oviya, I.R. Bhasuran, Balu Computer Science and Engineering - Ai Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India University of California Mission Bay San FranciscoCA94143 United States

ISBN: (纸本)9798350330915

Breast cancer is still a major worldwide health con- cern, which emphasises how important it is to have accurate and trustworthy diagnostic techniques. Our goal is to improve breast cancer identification in this scenario by utilizing mammography pictures from the RSNA Breast Cancer dataset. Leveraging the power of deep learning, our methodology integrates the SEResNet and ConvNeXtV2 models, simultaneously exploring and comparing various methods. To enhance the performance of our models, we incorporated changes in activation functions by replacing ReLU with GELU, an alternative activation function. Furthermore, we improved downsampling by introducing sepa- rate downsampling layers in the model architecture, optimizing spatial downsampling, and employing 3×3 convolutions with a stride of 2 at the start of each stage and 1×1 convolutions with a stride of 2 at the shortcut connection. The results achieved are highly promising, with a remarkably low loss of 0.0445 and impressive performance metrics. The F1 score stands impressively high at 0.96, complemented by a recall score of 0.95 and a precision of 0.97, showcasing exceptional performance, The accuracy score is also impressive at 92.5. These findings underscore the pivotal role of precision in elevating breast cancer detection accuracy, providing a path for more accurate diagnoses and ultimately improving patient outcomes. These enhancements significantly contributed to the overall performance of our breast cancer detection models, reinforcing the importance of precision in healthcare. This research strengthens the diagnostic tools available in the ongoing fight against breast cancer. © 2023 IEEE.

关键词： Breast Cancer Detection,Deep Learning computer-Aided Diagnosis Convo- lutional Neural Networks Mam- mography Image Analysis

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A Deep Reinforcement Learning Perspective on Adaptive Federated Dropout 20

A Deep Reinforcement Learning Perspective on Adaptive Federa...

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20th IEEE International Conference on Mobile Ad Hoc and Smart Systems, MASS 2023

作者： Sun, Tonghui Huang, Yan Xie, Zhenzhen Pang, Junjie Chen, Xingyun Cai, Zhipeng Qingdao University College of Computer Science and Technology Qingdao266000 China Kennesaw State University College of Computing and Software Engineering AtlantaGA United States Shandong University School of Computer Science and Technology Qingdao266237 China Georgia State University Department of Computer Science AtlantaGA United States

ISBN: (纸本)9798350324334

Federated learning is a distributed learning solution that achieves high-quality machine learning models while ensuring privacy and collaboration among various end devices. However, different kinds of end devices can lead to an unstable training process due to limited and dynamic communication and computation resources. Federated Dropout is an important technique for mitigating such resource bottlenecks by randomly removing a fixed percentage of activations of model components. However, most of the existing FL dropout methods cannot effectively utilize the distinguished characteristics of different clients or fail to automatically adapt to end device heterogeneity. In this paper, we thus propose Adaptive Federated Dropout with Reinforcement Learning (AFD-RL). AFD-RL employs reinforcement learning technology to adapt to the clients' unbalanced computation and communication resources and provide a personalized dropout strategy for each client. By automatically selecting suitable model components for each client in each communication round, AFD-RL achieves better accuracy with fewer communication rounds. Extensive experiments validate the effectiveness of the proposed approach in improving training efficiency and inference accuracy. © 2023 IEEE.

关键词： Reinforcement learning

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Comparative Study Analysis on News Articles Categorization using LSA and NMF Approaches 13

Comparative Study Analysis on News Articles Categorization u...

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13th International Conference on computing Communication and Networking Technologies, ICCCNT 2022

作者： Rupendra Reddy, Bandi Sai Tharun Reddy, Darukumalli Sandeep Preetham, M.C. Rajasekhar, A.H.N. Subramani, R. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India Bengaluru560029 India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Due to exponentially growing news articles every day, most of their important data goes unnoticed. It is important to come up with the ability to automatically analyse these articles and segregate them based on the context and related to their particular domain. This paper applies topic modelling which is one of the most growing unsupervised machine learning fields on a million headlines articles in order to produce topics to describe the context of the news article. There are various generative models but we specifically focusing on the non-negative matrix factorization (NMF) and Latent Semantic Analysis (LSA) for implementing and evaluating news dataset. Furthermore, the findings reveal that both NMF and LSA are useful topic modelling tools and classification frameworks, but based on the experimental results the LSA model performed well to identify the hidden data with better mean coherence values and also consumes lesser execution time than NMF. © 2022 IEEE.

关键词： Non-negative matrix factorization

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Exploration on IoT based Edge Cloud computing Techniques for Improving the Patient Information Management System

Exploration on IoT based Edge Cloud Computing Techniques for...

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2022 International Conference on Edge computing and Applications, ICECAA 2022

作者： Saranya, S.S. Fatima, N. Sabiyath Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Kattakulathur Campus Tamil Nadu Chennai India Department of Computer Science and Engineering B.S.Abdur Rahman Institute of Science and Technology Tamil Nadu Vandalur Chennai India

ISBN: (数字)9781665482325

ISBN: (纸本)9781665482325

Clinics around the globe are adopting newer, highly advanced technologies. The Internet of Things (IoT) has emerged due to advancements in information and communication technology. Integration of the IoT with accessible medical devices, and patients and their surroundings has opened up new possibilities for data collecting and more intelligent decisions in recent years. IoT technologies benefithealthcare professionals and patients in today's medical context because they are used in several treatment domains (like real-time monitoring, patient information and healthcare management). Diverse and distributed devices will gather, analyse, and transmit real-time medical data to open clouds in the upcoming world of IoT for healthcare, allowing for the accumulation, piling, and analysis of elementary data streams in many new procedures and the activation of context-dependent intimations. Healthmonitoring and remote treatment are possible with an IoT paradigm that uses sensors (environmental, wearable, implanted) distributed throughout the local *** been critical to create new technology for storing large amounts of clinical data in recent years. Cloud storage has become enormous, and it is managed by merging IoT and Edge computing (EC) technologies. According to recent research, the integration of IoT and the cloud has a considerable impact on remote healthcare. This survey report focuses on howIoT devices with EC can enhance people's health while guaranteeing that medicines and medicine delivery are error-free. This research will act as a source and reference of knowledge regarding smarthealthcare for healthcare professionals, researchers, and others interested in learning more about IoT with EC-based healthcare applications. © 2022 IEEE.

关键词： Internet of things

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A Comprehensive Study on Blockchain: Transforming the World 2

A Comprehensive Study on Blockchain: Transforming the World

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2nd International Conference for Innovation in Technology, INOCON 2023

作者： Thakur, Sujata Negi Maurya, Sudhanshu Rawat, Bhupesh Graphic Era Hill University Department of Computer Science & Engineering Haldwani Campus Nainital India Graphic Era Hill University School of Computing Bhimtal Campus Nainital India

ISBN: (纸本)9798350320923

We are going towards digitization, and while discussing it, 'money' is the most common word that comes to everyone's mind. We have 'Bitcoins' to support this. The Blockchain, a shared ledger system, allows for the trading of Bitcoin and other types of digital money. In essence, bitcoins are long sequences of code that have been transformed into currency. Digital money that is created and held electronically is called bitcoin. Bitcoin transfers employ a shared ledger system called a blockchain. It is also being used in a number of other fields, including the government, the healthcare industry, and e-voting systems. These days, people are incredibly concerned about transaction security. Due to its comprehensive security characteristics, blockchain networks are increasingly being used. Throughout the world, people are embracing the blockchain network since it has complete security features. In our work, we have also covered some of the unique features of blockchain besides security. Every invention, including blockchains, must overcome numerous difficulties, as we all very well know. Some of the obstacles that the adoption of blockchain technology faces have been briefly discussed. We have covered the fundamentals of current blockchain technology, as well as its applications, features, and difficulties. © 2023 IEEE.

关键词： Blockchain

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Short-term speed prediction of urban roads based on multi-source feature fusion 23

Short-term speed prediction of urban roads based on multi-so...

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23rd IEEE International Conference on High Performance computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Liu, Silin Liao, Zhuhua Liu, Yizhi Yi, Aiping School of Computer Science and Engineering Hunan University of Science and Technology Hunan Key Laboratory of Service Computing and New Software Service Technology Xiangtan China

ISBN: (纸本)9781665494571

Short-term traffic forecasting is an important means to prevent urban congestion. In this paper, the urban traffic operation time series data sets are used for modeling, combining with various characteristic variables of weather conditions, air quality index (AQI) and travel time index (TTI). A bidirectional long and short-term memory network framework (MF-BiLSTM) based on multi-source feature fusion was established to predict the average road speed. The comparison experiment selects three benchmark models: ARIMA, LSTM and CNN, and a bidirectional long short-term memory network (BiLSTM) model using only road speed time series data sets, and compares the prediction accuracy of these models under three prediction durations. The results show that MF-BiLSTM has the best prediction performance in terms of prediction error measurement, the prediction accuracy is 2.93%, 1.95%, 2.87%, 0.89% higher than that of ARIMA, LSTM, CNN, and BiLSTM, respectively. In addition, comparing the prediction accuracy of each model under different weather conditions and air pollution levels, MF-BiLSTM has the smallest deviation. © 2021 IEEE.

关键词： Correlation Roads Atmospheric modeling Time series analysis Weather forecasting Predictive models Air pollution

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Multi Layered Rule-Based Technique for Explicit Aspect Extraction from Online Reviews

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computers, Materials & Continua 2022年第12期73卷 4641-4656页

作者： Mubashar Hussain Toqir A.Rana Aksam Iftikhar M.Usman Ashraf Muhammad Waseem Iqbal Ahmed Alshaflut Abdullah Alourani School of Systems and Technology University of Management and TechnologyLahore54770Pakistan Department of Computer Science and It The University of LahoreLahore54000Pakistan School of Computer Sciences Universiti Sains Malaysia(USM)Penang11800Malaysia Department of Computer Science Comsats University IslamabadLahore54000Pakistan Department of Computer Science GC Women University SialkotPakistan Department of Software Engineering The Superior UniversityLahore53700Pakistan Faculty of Computing and It Albaha UniversityAlbahaSaudi Arabia Department of Computer Science Majmaah UniversitySaudi Arabia

In the field of sentiment analysis,extracting aspects or opinion targets fromuser reviews about a product is a key *** the polarity of an opinion is much more useful if we also know the targeted Aspect or *** based approaches,like dependency-based rules,are quite popular and effective for this ***,they are heavily dependent on the authenticity of the employed parts-of-speech(POS)tagger and dependency *** popular rule based approach is to use sequential rules,wherein the rules formulated by learning from the user’s ***,in general,the sequential rule-based approaches have poor generalization ***,existing approaches mostly consider an aspect as a noun or noun phrase,so these approaches are unable to extract verb *** this article,we have proposed a multi-layered rule-based(ML-RB)technique using the syntactic dependency parser based rules along with some selective sequential rules in separate layers to extract noun ***,after rigorous analysis,we have also constructed rules for the extraction of verb *** verb rules primarily based on the association between verb and opinion *** proposed multi-layer technique compensates for the weaknesses of individual layers and yields improved results on two publicly available customer review *** F1 score for both the datasets are 0.90 and 0.88,respectively,which are better than existing *** improved results can be attributed to the application of sequential/syntactic rules in a layered manner as well as the capability to extract both noun and verb aspects.

关键词： Explicit aspect aspect extraction opinion mining rule-based verb aspects

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Fortifying Information Security: Security Implications of Microservice and Monolithic Architectures 16

Fortifying Information Security: Security Implications of Mi...

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16th International Conference on Security of Information and Networks, SIN 2023

作者： Nemmini, Sanghamitra Abhishek, S. Anjali, T. Raj, Sincy Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amrita School of Computing Amritapuri India Vrije Universiteit Brussel Macs Pleinlaan 2 BrusselsB-1050 Belgium

ISBN: (纸本)9798350343212

Monolithic architecture is a traditional and primary method for creating software programs. On the other hand, monolithic architecture's shortcomings were addressed by developing the unique evolutionary technique known as microservice architecture, which led to businesses' widespread acceptance of this design. However, the microservice design raises several security concerns, particularly web application security, a significant business problem. The purpose of this study is to analyse and contrast the security and safety features of monolithic and microservice architectures, emphasizing the security issues that each presents and the solutions that have been proposed to address them. Examining both architectures security risks and investigating viable mitigation strategies are covered in the paper. Microservices improve security and flexibility but also need a more complex and dynamic security strategy. © 2023 IEEE.

关键词： Architecture Attacks Microservices Monolithic Remote Security Web Application

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Towards Developing an Ontology for Safety of Navigation Sensors in Autonomous Vehicles 15

Towards Developing an Ontology for Safety of Navigation Sens...

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15th International Conference on Knowledge engineering and Ontology Development, KEOD 2023 as part of the 15th International Joint Conference on Knowledge Discovery, Knowledge engineering and Knowledge Management, IC3K 2023

作者： Alharbi, Mohammed Karimi, Hassan A. Geoinformatics Laboratory School of Computing and Information University of Pittsburgh PittsburghPA15260 United States College of Computer Science and Engineering Taibah University Medina42353 Saudi Arabia

ISBN: (纸本)9789897586712

Understanding and handling uncertainties associated with navigation sensors in autonomous vehicles (AVs) is vital to enhancing their safety and reliability. Given the unpredictable nature of real-world driving environments, accurate interpretation and management of such uncertainties can significantly improve navigation decision-making in AVs. This paper proposes a novel semantic model (ontology) for navigation sensors and their interactions in AVs, focusing specifically on sensor uncertainties. At the heart of this new ontology is understanding the sources of sensor uncertainties within specific environments. The ultimate goal of the proposed ontology is to standardize knowledge of AV navigation systems for the purpose of alleviating safety concerns that stand in the way of widespread AV adoption. The proposed ontology was evaluated with scenarios to demonstrate its functionality. Copyright © 2023 by SCITEPRESS – science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0).

关键词： Ontology

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