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Artificial Intelligence and Knowledge Discovery in Concurrent engineering (ICECONF), International Conference on

作者： M. Sangeetha G. Sasikala K. Anitha S D Prabu Ragavendiran K R Senthil Kumar M. Deivakani Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Tamil Nadu Department of Mathematics SRM Valliammai Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Engineering Saveetha Engineering College Thandalam Tamil Nadu India Department of Computer Science and Engineering RVS Technical campus - Coimbatore Coimbatore Tamil Nadu India Department of Mechanical Engineering R. M K. Engineering College Kavaraipettai Tamil Nadu India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India

In this paper, we develop an automatic diagnosis model that aims to screen and detect the presence of cervical cancer in women patients. The diagnosis model consists of a series of stages that involves pre-processing, feature extraction and classification of cancer using bees swarm optimization (BSO). The BSO helps to classify the instances from the extracted features in an effective way that does not fall into premature convergence. The fully grown solution provides effective classification of images from the pre-defined medical datasets. The simulation is conducted on python in a high-end computing system to test the efficacy of BSO in classifying the cervical cancer. The validation shows an increasing precision of classifying the instances than other state-of-art methods.

关键词： Computational modeling Machine learning Feature extraction Knowledge discovery Concurrent engineering Particle swarm optimization Cervical cancer

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Exploring the Security Implications of Stream and Block Ciphers in Cryptography and Network Security

Exploring the Security Implications of Stream and Block Ciph...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Dipannita Mondal S. Hemelatha Jayadurga R Nakul S. Sharma Sidharth Jain Jambi Ratna Raja Kumar Artificial Intelligence And Data Science Department Dr. D.Y.Patil College Of Engineering And Innovation Talegaon Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Master of Business Administration Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department of Management Mangalayatan University Aligarh India Department of Computer Engineering Genba Sopanrao Moze College of Engineering Balewadi Pune Maharashtra India

This paper examines the security implications of movement and block ciphers in cryptography and network security. Circulate ciphers encrypt facts as they are transmitted over a network. A block cipher encrypts and decrypts a set amount of information. The paper discusses how these types of cipher paintings and opinions affect data security. Additionally, the paper looks at several demanding situations associated with using them, along with crucial changes, block size, critical duration, and overall performance. The paper provides a complete assessment of the pros and cons of circulate and block ciphers and gives pointers for which sort of cipher to apply in one-of-a-kind situations.

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Optimizing Patient Satisfaction Surveys and Feedback with Cloud Computing and NLP Techniques

Optimizing Patient Satisfaction Surveys and Feedback with Cl...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： L. N. Jayanthi Kannan Shanmugam Challapalli Suma Sagar B S S. P. Maniraj T. R. GaneshBabu Department of Commerce Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennai Tamil Nadu India Department of Gaming Technology School of Computing Science and Engineering VIT Bhopal University Bhopal Madhapradesh India Department of Computer Science and Engineering Sagi Rama Krishnam Raju Engineering College (A) Bhimavaram Andhra Pradesh India Department of Electrical and Electronics Engineering REVA University Bengaluru Karnataka India Department of Data Science and Business Systems Faculty of Engineering & Technology Kattankulathur - Chennai Chennai Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

Healthcare quality assessment and identification of opportunities for improvement are crucially facilitated by patient satisfaction surveys. Traditional approaches sometimes face difficulties in handling substantial amounts of written comments and obtaining real useful data. It presents a new method that combines cloud computing with Natural Language Processing (NLP) approaches to enhance the analysis of patient satisfaction data. By using cloud platforms, it can effectively manage large datasets and provide scalable, cost-efficient processing. The use of NLP methods, such as text mining and sentiment analysis, enables the conversion of unstructured input into structured insights, thereby uncovering the fundamental patient feelings and patterns. This connection enables the analysis of data in real-time also improving the accuracy of interpreting feedback and facilitating fast enhancements in healthcare services. Future research will prioritise the improvement of NLP algorithms and the investigation of supplementary cloud-based technologies to enhance the accuracy and efficiency of patient satisfaction analysis to improved patient care and experience.

关键词： Surveys Text mining Cloud computing Technological innovation Accuracy Medical services Strategic planning Market research Natural language processing Real-time systems

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Deep learning-based multi-task prediction of response to neoadjuvant chemotherapy using multiscale whole slide images in breast cancer:A multicenter study

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Chinese Journal of Cancer Research 2025年第1期37卷 28-47页

作者： Qin Wang Feng Zhao Haicheng Zhang Tongpeng Chu Qi Wang Xipeng Pan Yuqian Chen Heng Zhou Tiantian Zheng Ziyin Li Fan Lin Haizhu Xie Heng Ma Lan Liu Lina Zhang Qin Li Weiwei Wang Yi Dai Ruijun Tang Jigang Wang Ping Yang Ning Mao School of Information and Electronic Engineering Shandong Technology and Business UniversityYantai 264005China Big Data and Artificial Intelligence Laboratory Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China Shandong Provincial Key Medical and Health Laboratory of Intelligent Diagnosis and Treatment for Women’s Diseases Yantai Yuhuangding HospitalYantai 264000China Department of Radiology Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China School of Computer Science and Technology Shandong Technology and Business UniversityYantai 264005China School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin 541004China School of Medical Imaging Binzhou Medical UniversityYantai 264003China Department of Radiology Jiangxi Cancer Hospitalthe Second Affiliated Hospital of Nanchang Medical CollegeNanchang 330006China Department of Radiology the First Affiliated Hospital of China Medical UniversityShenyang 400042China Department of Radiology Weifang Hospital of Traditional Chinese MedicineWeifang 262600China Department of Medical Imaging Affiliated Hospital of Jining Medical UniversityJining 272029China Department of Radiology the Peking University Shenzhen HospitalShenzhen 518036China Department of Pathology Guilin Traditional Chinese Medicine HospitalGuilin 541002China Department of Pathology the Affiliated Hospital of Qingdao UniversityQingdao 266555China Department of Pathology Yantai Yuhuangding Hospital of Qingdao UniversityYantai 264000China

Objective:Early predicting response before neoadjuvant chemotherapy(NAC)is crucial for personalized treatment plans for locally advanced breast cancer *** aim to develop a multi-task model using multiscale whole slide images(WSIs)features to predict the response to breast cancer NAC more ***:This work collected 1,670 whole slide images for training and validation sets,internal testing sets,external testing sets,and prospective testing sets of the weakly-supervised deep learning-based multi-task model(DLMM)in predicting treatment response and pCR to *** approach models two-by-two feature interactions across scales by employing concatenate fusion of single-scale feature representations,and controls the expressiveness of each representation via a gating-based attention ***:In the retrospective analysis,DLMM exhibited excellent predictive performance for the prediction of treatment response,with area under the receiver operating characteristic curves(AUCs)of 0.869[95%confidence interval(95%CI):0.806−0.933]in the internal testing set and 0.841(95%CI:0.814−0.867)in the external testing *** the pCR prediction task,DLMM reached AUCs of 0.865(95%CI:0.763−0.964)in the internal testing and 0.821(95%CI:0.763−0.878)in the pooled external testing *** the prospective testing study,DLMM also demonstrated favorable predictive performance,with AUCs of 0.829(95%CI:0.754−0.903)and 0.821(95%CI:0.692−0.949)in treatment response and pCR prediction,*** significantly outperformed the baseline models in all testing sets(P<0.05).Heatmaps were employed to interpret the decision-making basis of the ***,it was discovered that high DLMM scores were associated with immune-related pathways and cells in the microenvironment during biological basis ***:The DLMM represents a valuable tool that aids clinicians in selecting personalized treatment strategies for breast cancer patients.

关键词： Artificial intelligence breast cancer digital pathology whole slide images

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XoT (Xducation of Things): Harnessing AI and edge computing to educate all things

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IEEE Access 2024年

作者： Nurtantyana, Rio Hwang, Wu-Yuin Hariyanti, Uun Research Center for Data and Information Sciences Bandung Indonesia Telkom University School of Computing Indonesia Telkom University Center of Excellence Artificial Intelligence for Learning and Optimization Bandung Indonesia National Dong Hwa University College of Science and Engineering Department of Computer Science and Information Engineering Taiwan National Central University Graduate Institute of Network Learning Technology Taiwan Universitas Brawijaya Faculty of Computer Science Department of Information System Indonesia

Most English foreign language (EFL) studies focus solely on human beings. This research explores how edge computing can facilitate learning for all things. The XoT (Xducation of Things) framework was proposed to educate both human and all things. All things encompass two terms: AI-Agent and smartthings (covering physical and digital smart objects). At the core of this framework is Smart Question Answer Forwarding Mechanism (SQA-Forwarding), specifically designed to assist all things in building knowledge. To demonstrate this, the smartXoT environment was developed based on XoT framework, and its impacts on EFL learners was assessed. A quasi-experimental study involving 26 EFL learners, divided into an experimental group (EG) and a control group (CG), examined the differences in learning achievement of smartthings and EFL learners when using the smartXoT environment with/without SQA-Forwarding. Findings, on one hand, indicated that smartthings in the EG developed knowledge bases greater than those in the CG. On the other hand, the interaction between EFL learners and smartthings with SQA-Forwarding significantly improved learners' writing skills, with revisions playing a crucial role in enhancing writing quality. Thus, the XoT framework offers a novel and promising approach to educating both humans and all things. © 2013 IEEE.

关键词： Contrastive Learning

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An Atom Quest Optimizer for CNN to Distinguish IDs in SDN and IoT Eco system

An Atom Quest Optimizer for CNN to Distinguish IDs in SDN an...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： V.S Anusuya Devi Vijilius Helena Raj Balasubramanian Prabhu Kavin E. Gangadevi Balamurugan Balusamy Shilpa Gite New Horizon College of Engineering Ring Road Bellandur Post Bengaluru India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chengalpattu India Department of Computer Science Loyola College Chennai India Shiv Nadar University Delhi-National Capital Region (NCR) India AIML Department Symbiosis Institute of Technology Pune Symbiosis International (Deemed University) Pune India

Security has become crucial as Internet of Things (IoT) applications have grown in popularity. The prevalence of IoT vulnerabilities was recently exposed by a distributed denial-of-service (DDoS) assault, and many IoT strategies unintentionally assisted in the DDoS attack. The new software-defined anything (SDx) paradigm offers a method for managing IoT devices in a secure manner. We initially provide a broad agenda for the software-defined Internet of Things (SD-IoT) in this paper, which is based on the SDx paradigm. The implementation of an IDS based on Deep Learning (DL) procedures into an SDN construction is the focus of this paper, which also suggests a very real-world method to shorten the delay by using the consecutive implementation of prototypes with cumulative hardware difficulty. This method enables quick tests to identify the key issues, address them, and then implement the solution.

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A DCNN with Jellyfish Questoptimization Algorithm in Order to Find Fetus Images

A DCNN with Jellyfish Questoptimization Algorithm in Order t...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： K Aravinda B Manjunatha Balasubramanian Prabhu Kavin E. Gangadevi Balamurugan Balusamy Shilpa Gite New Horizon College of Engineering Ring Road Bellandur Post Bengaluru India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chengalpattu India Department of Computer Science Loyola College Chennai India Shiv Nadar University Delhi-NationalCapital Region (NCR) India AIML department Symbiosis Institute of Technology Pune. Symbiosis International (Deemed University) Pune India

Incomplete closure around the spinal cord is the underlying cause of spina bifida, a birth condition. Multiple methods exist for identifying the presence of spina bifida. Ultrasound can be used to look for spinal axis deformities. However, due to the distortion inherent in prenatal ultrasound pictures, this method requires the expertise of a skilled physician in order to pinpoint the anomaly. Examining ultrasound pictures visually may be a subjective and error-prone process. The diagnosis procedure for spinal deformities may benefit from a computerised support system that can automatically locate the site of the abnormality. An algorithm for determining whether the entire spine in an ultrasound picture is healthy or diseased would be the first step in developing such software. In this study, we combine the power of the Jellyfish Search Optimisation Algorithm (JFSOA) with a deep convolution neural network to better identify SB. Improved classification accuracy is achieved by JFSOA's optimal selection of the SDL model's learning rate. Foetal ultrasound scans taken in the second trimester are widely regarded as a useful screening technique for identifying birth defects. The foetal spine picture from an ultrasound scan taken at the 18th week of pregnancy is used. Preprocessing pipelines, such as contrast improvement, intensity modification, and denoising, are used to improve the quality of ultrasound pictures and eliminate noise. Using this method, the suggested model can reliably recognise pictures of people with spina bifida. The suggested approach accurately identifies and labels ultrasound pictures as normal or defective based on the attributes retrieved

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Towards Intelligent Transportation with Pedestrians and Vehicles In-the-Loop: A Surveillance Video-Assisted Federated Digital Twin Framework

arXiv

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arXiv 2025年

作者： Li, Xiaolong Wei, Jianhao Wang, Haidong Dong, Li Chen, Ruoyang Yi, Changyan Cai, Jun Niyato, Dusit Shen, Xuemin The Xiangjiang Laboratory Hunan Provincial General University Key Laboratory of IoT Intelligent Sensing and Distributed Collaborative Optimization Hunan University of Technology and Business Changsha410205 China The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China The Department of Electrical and Computer Engineering University of Concordia Montreal Canada The College of Computing and Data Science Nanyang Technological University Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada

In intelligent transportation systems (ITSs), incorporating pedestrians and vehicles in-the-loop is crucial for developing realistic and safe traffic management solutions. However, there is falls short of simulating complex real-world ITS scenarios, primarily due to the lack of a digital twin implementation framework for characterizing interactions between pedestrians and vehicles at different locations in different traffic environments. In this article, we propose a surveillance video assisted federated digital twin (SV-FDT) framework to empower ITSs with pedestrians and vehicles in-the-loop. Specifically, SVFDT builds comprehensive pedestrian-vehicle interaction models by leveraging multi-source traffic surveillance videos. Its architecture consists of three layers: (i) the end layer, which collects traffic surveillance videos from multiple sources;(ii) the edge layer, responsible for semantic segmentation-based visual understanding, twin agent-based interaction modeling, and local digital twin system (LDTS) creation in local regions;and (iii) the cloud layer, which integrates LDTSs across different regions to construct a global DT model in realtime. We analyze key design requirements and challenges and present core guidelines for SVFDT’s system implementation. A testbed evaluation demonstrates its effectiveness in optimizing traffic management. Comparisons with traditional terminal-server frameworks highlight SV-FDT’s advantages in mirroring delays, recognition accuracy, and subjective evaluation. Finally, we identify some open challenges and discuss future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Dfier: A Directed Vulnerability Verifier for Ethereum Smart Contracts

SSRN

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SSRN 2023年

作者： Wang, Zeli Dai, Weiqi Li, Ming Choo, Kim-Kwang Raymond Zou, Deqing Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing40065 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio United States

Existing smart contract vulnerability identification approaches mainly focus on complete program detection. Consequently, lots of known potentially vulnerable locations need manual verification, which is energy-exhaustive and error-prone. Fuzzing is an effective way to detect vulnerabilities. However, current fuzzers cannot effectively probe these suspicious targets because they overlook two key factors: exploring all paths toward the targets and providing high-quality directed seed inputs. This paper presents a Directed vulnerability veriFier (DFier), elaborating effective transaction sequences with directed inputs for the fuzzer, to focus on analyzing target paths and automatically detect whether the given locations are vulnerable. Specifically, DFier fuses the effectiveness of static analysis and the efficiency of fuzzing. The static analysis helps locate target paths and generate directed inputs for the fuzzer under three heuristic strategies. The first prefers inputs with the larger possibility of triggering potential vulnerabilities based on target features. The second guides the value mutation to satisfy the path constraints. The third adequately leverages execution information to help find exploits faster. Extensive experiments demonstrate that DFier is effective in verifying contract security, compared with three existing contract fuzzers (i.e., contractFuzzer, sFuzz, and conFuzzius). And the performance losses are in an acceptable range. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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Intelligent Caching Strategies for 5G Edge Networks using Machine Learning

Intelligent Caching Strategies for 5G Edge Networks using Ma...

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Smart Electronics and Communication (ICOSEC), International Conference on

作者： Naga Venkata Ramakrishna Guduri S. Sethu R. Shalinirajan R. Reena Karthikeyan M G.S. Uthayakumar Electrical and Electronics Section Engineering Department University of Technology and Applied Sciences-Al Musannah Sultanate of Oman Department of Computer Science and Engineering Vels Institute of Science Technology & Advanced Studies (VISTAS) Chennai Tamilnadu India Department of Computer Science and Business System Panimalar Engineering College Chennai Tamilnadu India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamilnadu India Department of Computer Science and Engineering (Cyber Security) R.M.K. College of Engineering and Technology Chennai Tamilnadu India Department of Electronics and Communication Engineering St. Joseph's Institute of Technology Chennai Tamilnadu India

To reduce latency and improve the user experience, content caching solutions at the network periphery will become increasingly important as 5G networks and data-intensive applications. In this proposed work, the deployment of a caching architecture for 5G periphery networks using machine learning is done. Utilizing information on user behavior, network conditions, and content prominence enables more accurate forecasts and cache management decisions. This trains the prediction models using a wide range of machine-learning techniques to accommodate the ever-changing needs of both networks and content. To evaluate the efficiency of the proposed architecture by measuring its ability to increase cache hit rates and decrease latency. These techniques significantly outperform the standard caching methods. With the intelligent caching architecture of 5G periphery networks, throughput is increased, latency is reduced, and congestion is alleviated. The findings of this study contribute to the growing corpus of literature on AI-driven optimization in 5G networks and shed light on how such solutions could be enhanced for future networks. Neural Networks achieved 96% accuracy than SVM which is 93%.

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