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Advances in Computation, Communication and Information technology (ICAICCIT), International Conference on

作者： P S Metkewar N Uma Rajesh Kumar Dhanaraj Balasubramanian Prabhu Kavin Malathy Sathyamoorthy Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) (SIU) Pune Maharashtra India New Horizon College of Engineering Bengaluru Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chengalpattu India Department of Information Technology KPR Institute of Engineering and Technology Coimbatore India

In affective computing, emotion acknowledgement in the wild is a much-studied area. Although there have been advancements, the difficulty of emotion acknowledgement in the wild due to head movement, face deformation, illumination fluctuation, etc. remains an open subject. To improve the model's simplification ability and, by extension, its performance across a variety of learning tasks, it is crucial that a wide variety of features be extracted by the deep neural network. Interest in facial expression detection in natural settings has grown in recent years despite the difficulty of obtaining discriminative and informative characteristics from partially obscured photos. In the first stage of this paper's pre-processing, the noisy pictures are enhanced by Contrast Limited Adaptive Histogram Equalization (CLAHE). After that, Residual Neural Network (ResNet) features extraction is used, and the same method serves as an emotion classifier. An iterative technique called stochastic gradient descent (SGD) is utilized to refine the ResNet model's objective function. Finally, the Artificial Rabbits Optimization Algorithm (ARO) is used to choose the best value for, thereby enhancing the reliability of the categorization. The suggested method is effective, as evidenced by experimental findings on three popular facial expression recognition in-the-wild datasets: AffectNet, AFEW dataset, and RAF-DB, where it accomplishes state-of-the-art presentation with 96% accuracy and improves upon existing models by roughly 5% to 8%.

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Hybrid Machine Learning Approach of “KNN LSTM” for detection of Brain Tumour in MR images

Hybrid Machine Learning Approach of “KNN LSTM” for detecti...

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International Conference on Computing and Networking technology (ICCNT)

作者： G. Uganya Shruthi V Kulkarni Vani Valsaraj Anuja Govindrao Gunale D. Rajalakshmi B Srividhya Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of AIML HKBK College of Engineering opposite manyata tech park Nagawara Bengaluru Karnataka Department of Computer Engineering UPL University of Sustainable Technology Block no 402 Ankleshwar-Valia Road Vataria Gujarat Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai Department of ComputerApplication Faculty of Humanities and Sciences SRM Institute Of Science and Technology Chengalpattu Tamil Nadu

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Now a days, more existing techniques are available to found tumours on brain from MR images. But still the researchers are concentrated on to detect the brain tumour accurately with less time. So, this work proposed the hybrid machine learning algorithm of “K-Nearest Neighbour - Long Short-Term Memory” (“KNN-LSTM”) with increased ‘accuracy’, ‘sensitivity’, ‘specificity’, with less ‘time consumption’. In this work, the brain tumour is identified by predicting the MR images from huge dataset with usage of machine learning approach of “KNN-LSTM”. Brain tumour detection is difficult when to compare the abnormal and typical tissues of large capacity. In order to enable a speedy recovery following therapy, brain tumours should be identified as soon as feasible. If the brain tumour is discovered early on, patients can benefit from improved care. Due to the irregular spreading of the abrasions, brain tumours are still difficult to identify even with intensive study. Sometimes the tiny tumours look like normal, but it is challenging to find it. So, the techniques are need to increase the classification accuracy, and selecting the useful features. This paper proposes a hybrid machine learning model “KNN-LSTM” for categorizing the tumours with predicting through MR images. MRI brain imaging collection is used for our experimentation. The “KNN” algorithm usage is to extract the important characteristics from pictures once the data has been pre-processed effectively. The proposed work predicts the brain tumour with 99.15% of ‘accuracy’, 98% of ‘sensitivity’, 97.12% of ‘specificity’ and testing time consumption of 0.5 minutes.

关键词： Machine learning algorithms Accuracy Sensitivity Magnetic resonance imaging Machine learning Nearest neighbor methods Brain modeling Long short term memory Tumors Testing

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Predicting Crops Based on Soil and Environmental Characteristics Using ARIMA and LSTM Model

Predicting Crops Based on Soil and Environmental Characteris...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： U Naresh kumar K Madhana Mohan S. Pragadeeswaran Jobin M Scaria Baseera A D. Seenivasan Department of Artificial intelligence & Data science Annamacharya University Rajampet India Department of Electronics and Instrumentation Engineering Sri Sairam Engineering College Chennai India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Mt. Eriama Campus School of Information Technology IBS University Port Moresby Papua New Guinea School of Computing Science and Engineering VIT Bhopal University Sehore India Department of Computer Science and Business Systems M. Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

Food producers are under pressure to meet rising demand as the world's population and natural resources diminish. Overuse of pesticides and fertilizers has caused soil erosion and land degradation. Future food needs will require environmentally sustainable farming strategies. The suggested strategy includes model training, feature extraction, sensor integration, and model preparation. data preparation involves processing data before storing it machine-readable. SFFS and RFE select features. The model was trained using a hybrid ARIMA-LSTM technique, which blends deep learning and time-series forecasting. The ARIMA-LSTM model outperforms solo ARIMA and LSTM models with an average accuracy of 93.54%. Increased accuracy shows the model's capacity to anticipate sustainable agriculture approaches. data-driven and environmentally sustainable ways are needed to overcome global food production concerns, according to this study. When paired with good preprocessing and feature selection, ARIMA-LSTM can boost agricultural productivity while minimizing environmental impact.

关键词： Training Accuracy Food manufacturing Biological system modeling Soil Predictive models Feature extraction Agriculture Long short term memory Meteorology

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A Novel Ensemble Deep Learning Framework for Breast Cancer Prediction

A Novel Ensemble Deep Learning Framework for Breast Cancer P...

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Innovative Computing, Intelligent Communication and Smart Electrical systems (ICSES), International Conference on

作者： Pundru Chandra Shaker Reddy S Nithyapriya Nipun Sharma S Maheswari B Jayaram Tejaswini Katale School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Artificial Intelligence and Data Science Bannari Amman Institute of Technology Anna University Chennai Tamil Nadu India Electronics and Communication Engineering Presidency University Bangalore Karnataka India Department of Mathematics Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Computer Science and Business System RMK Engineering College Kavaraipettai Tamil Nadu India Department of Computer Engineering Vishwakarma Institute of Technology Pune Maharashtra India

Because of its high incidence and high mortality rate, breast cancer requires reliable means of early detection and diagnosis to improve survival rates for its patients. With advantages including increased accuracy and efficiency, deep learning (DL) and machine learning (ML) methods are quickly becoming indispensable in the fight against breast cancer. However, there is a need for additional study because existing approaches have scalability and performance constraints. Utilizing a pre-trained ResNet50V2 and ensemble-based ML approaches, we offer a hybrid approach to reliable breast cancer diagnosis in this study. Learning and extracting latent trends from complex breast-cancer(BC) images is made possible by DL's incorporation, with ML strategies contributing interpretability and generalizability. We performed wide experiments utilizing a openly accessible Invasive-Ductal-Carcinoma(IDC) dataset based on breast histopathology images of varying sample sizes. The resilience and great performance of our hybrid model are supported by the results of our extensive experiments. When compared to state-of-the-art models, our 95% accuracy rate, 94.5 precision, 94.2 recall, and 94.6 F1 score are all improvements. We also found that the best ML model to use with the ResNet50V2 architecture is the Light Boosting Classifier (LGB). This study's findings provide substantial contributions to the field of breast cancer diagnosis thanks to its novel approach, in-depth performance analysis, and trustworthy evaluation. In addition, it may help doctors make better decisions, provide better care, and boost results for people with breast cancer.

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Exploring Reinforcement Learning in Large-Scale data Processing

Exploring Reinforcement Learning in Large-Scale Data Process...

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

作者： Pawan Bhambu Ritesh Kumar P. Sharmila Vivek D. Patil Shivam Khurana Vivek V Department of Computer Science & Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Computer Science and Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Bangalore Karnataka India

Reinforcement mastering (RL) is vital for big-scale fast processing. RL utilizes the availability of large datasets and dealers skilled in this information to remedy a wide variety of obligations, from automated diagnostics and self-reliant using structures to clinical information analysis. Using exploiting the traits of the modern-day significant information era, RL allows efficient and correct decision-making in massive-scale information processing and answers complicated troubles. This paper explores the use of reinforcement learning (RL) in large-scale data processing. RL is a subfield of machine learning that focuses on decision making in dynamic environments. With the increasing demand for processing large amounts of data, traditional techniques such as rule-based systems and batch processing are becoming inadequate. This has led to the emergence of RL as a potential solution due to its ability to learn and adapt to changing environments. Through a comprehensive review of existing literature, this paper discusses the applications of RL in large-scale data processing and its potential benefits. Furthermore, it examines various challenges and their potential solutions in implementing RL for data processing tasks. The paper concludes by highlighting the future research directions in this domain and the potential impact of RL on improving data processing efficiency and accuracy. It similarly examines some of the demanding situations and possibilities related to using RL for huge-scale records processing and the capability programs of RL in the future.

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Advanced Bandwidth Management Techniques Using Wireless Mesh Networks for High-Speed Optical Fiber Communications

Advanced Bandwidth Management Techniques Using Wireless Mesh...

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

作者： Rakesh Kumar Yadav Sunil Sharma Kanika Seth Jeyalaxmi M Yashwant S. Ingle Vivek V Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Faculty of Engineering and Technology Computer Science and Engineering JAIN (Deemed-to-be University) Bangalore Karnataka India

This abstract encompasses superior bandwidth management techniques the usage of Wi-Fi mesh networks to improve the performance of high-velocity optical fiber communications. the point of interest lies at the access-, relay-and center-mesh architectures with their diverse, discrete techniques consisting of dynamic frequency allocation, load balancing and node optimization, as well as on highlights from latest research. Within the experiments, the effects exhibit giant enhancement in signal first-rate and effectiveness of bandwidth resources when the use of the proposed version, based totally at the convergence speed of the network fee. Furthermore, it's also evident that the optimized wireless mesh configuration brings about enormous fee financial savings. All the findings obtained at some stage in this research have eminently greater the talents of the proposed structure for growing green communications networks.

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Towards Smarter Supply Chains: Machine Learning Methodologies for Delivery Risk Assessment

Towards Smarter Supply Chains: Machine Learning Methodologie...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammad A Al Khaldy Feras Alnaimat Ahmad Al-Qerem Ali Mohd Ali Mohammed Rajab Business Intelligence Data Analytics University of Petra Amman Jordan Medical Engineering Faculty of Engineering Al-Ahliyya Amman University Amman Jordan Computer Science Department Zarqa University Jordan College of Engineering Technology Al-Balqa Applied University Jordan University Headquarter University Of Anbar Ramadi Anbar Iraq

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Supply chain management (SCM) has evolved into a core strategic component for enterprises seeking to gain a competitive edge in an increasingly interconnected global marketplace. With the ever-growing complexity and volume of data available-from procurement and production to distribution and last-mile delivery-organizations are turning to machine learning (ML) and big data analytics to optimize performance and stream-line operations. In this paper, we explore how integrating machine learning into various stages of the supply chain can unlock efficiencies, reduce delivery risks, and enable more informed, data-driven decisions. We employ the dataCo Global Supply Chain dataset as a case study to illustrate how ML classifiers, including K-Nearest Neighbors (KNN), Logistic Regression, Naive Bayes, and Random Forest, can be leveraged to predict late deliveries. Our results show that Random Forest achieved the highest accuracy (over 93%), demonstrating its superior ability to identify at-risk shipments. Furthermore, we emphasize the role of data exploration, feature engineering, and the critical need for addressing data imbalance in ensuring robust model performance. The discussion underscores the transformative potential of ML for improving supply chain efficiency, resilience, and strategic decision-making, while also highlighting future directions to handle evolving customer demands and emerging challenges in an era of rapidly expanding data.

关键词： Technological innovation Accuracy Costs Supply chains Customer satisfaction Nearest neighbor methods Big data Turning data models Random forests

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Insulating materials for realising carbon neutrality:Opportunities,remaining issues and challenges

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High Voltage 2022年第4期7卷 610-632页

作者： Chuanyang Li Yang Yang Guoqiang Xu Yao Zhou Mengshuo Jia Shaolong Zhong Yu Gao Chanyeop Park Qiang Liu Yalin Wang Shakeel Akram Xiaoliang Zeng Yi Li Fangwei Liang Bin Cui Junpeng Fang Lingling Tang Yulin Zeng Xingtao Hu Jiachen Gao Giovanni Mazzanti Jinliang He Jianxiao Wang Davide Fabiani Gilbert Teyssedre Yang Cao Feipeng Wang Yunlong Zi The State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijingChina Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina Simpson Querrey Institute Northwestern UniversityEvanstonIllinoisUSA Department of Materials Science and Engineering The Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA Department of Information Technology and Electrical Engineering ETH ZürichZürichSwitzerland School of Electrical and Information Engineering Tianjin UniversityTianjinChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA Electrical and Electronic Engineering The University of ManchesterManchesterUK Department of Electrical Engineering School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina Key Laboratory of Control of Power Transmission and Conversion(SJTU) Ministry of EducationShanghaiChina College of Electrical Engineering Sichuan UniversityChengduChina Shenzhen Institute of Advanced Technology Chinese Academy of Sciences ShenzhenShenzhenChina School of Electrical Engineering Wuhan UniversityWuhanChina School of Integrated Circuits Tsinghua UniversityBeijingChina Jiangsu Jinxin Electric Appliance Co.Ltd. YangzhouChina Tai'an Tiancheng Safety Evaluation Co. Ltd.Tai'anChina Department Electrical Electronic and Information EngineeringUniversity of BolognaBolognaItaly National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Laplace Paul Sabatier Universityand CNRSToulouseFrance Electrical and Computer Engineering University of ConnecticutStorrsConnecticutUSA State Key Laboratory of Power Transmission Equipment&System Security and New Technology School of Electrical EngineeringChongqing UniversityChongqingChina Thrust of Sustainable Energy and Environment The Hong Kong University of Science and Technology(Guangzhou)GuangzhouChina De

The 2050 carbon-neutral vision spawns a novel energy structure revolution,and the construction of the future energy structure is based on equipment *** material,as the core of electrical power equipment and electrified transportation asset,faces unprecedented challenges and *** goal of carbon neutral and the urgent need for innovation in electric power equipment and electrification assets are first *** engineering challenges constrained by the insulation system in future electric power equipment/devices and electrified transportation assets are *** materials,including intelligent insulating material,high thermal conductivity insulating material,high energy storage density insulating material,extreme environment resistant insulating material,and environmental-friendly insulating material,are cat-egorised with their scientific issues,opportunities and challenges under the goal of carbon neutrality being *** the context of carbon neutrality,not only improves the understanding of the insulation problems from a macro level,that is,electrical power equipment and electrified transportation asset,but also offers opportunities,remaining issues and challenges from the insulating material *** is hoped that this paper en-visions the challenges regarding design and reliability of insulations in electrical equipment and electric vehicles in the context of policies towards carbon neutrality *** authors also hope that this paper can be helpful in future development and research of novel insulating materials,which promote the realisation of the carbon-neutral vision.

关键词： neutral insulation extreme

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A Secure Authentication Framework for Consumer Mobile Crowdsourcing Networks

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IEEE Transactions on Consumer Electronics 2024年

作者： Aldosary, Abdallah Tanveer, Muhammad Ahmad, Musheer Maghrabi, Louai A. Ahmed, Emad A. Hussain, Amir El-Latif, Ahmed A. Abd Department of Computer Engineering Prince Sattam bin Abdulaziz University Riyadh Wadi Addwasir11991 Saudi Arabia University of Management and Technology Department of Computer Science Lahore54770 Pakistan Jamia Millia Islamia Department of Computer Engineering New Delhi110025 India University of Business and Technology College of Engineering Department of Software Engineering Jeddah Saudi Arabia South Valley University Faculty of Computers and Information Department of Computer Science Qena Egypt Edinburgh Napier University United Kingdom Prince Sultan University EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Riyadh11586 Saudi Arabia Menoufia University Faculty of Science Department of Mathematics and Computer Science Shebin El-Koom32511 Egypt

The Mobile crowdsourcing network (MCN) leverages collaborative intelligence to solve complex tasks through group cooperation. It comprises three main components: the end-user, the service provider, and the mobile user. The end-user requests crowd-sensing and computing services from the service provider, who handles task decomposition, distribution, re-composition, and recommendation. Mobile users perform the crowd sensing and computing tasks. Despite its benefits, MCN faces significant security and privacy challenges. Communications between the service provider and the end-user involve sensitive data, and mobile users and service providers communicate over open channels, making them vulnerable to security threats. To address these concerns, we propose a secure authentication framework for MCN, called SAF-MCN. This framework combines the lightweight cryptographic primitive ' ASCON' with elliptic curve cryptography. SAF-MCN allows the service provider to authenticate mobile users before they join the MCN. Once authenticated, a secure channel is established for encrypted communication over the public Internet. The security of SAFMCN is validated using both formal and informal methods, demonstrating its resilience against potential attacks. The use of Scyther further ensures the security of SAF-MCN. Comparative analysis shows that SAF-MCN is superior in terms of security features, computational efficiency, and communication cost. © 1975-2011 IEEE.

关键词： ASCON Authenticated Encryption Authentication Mobile Crowdsourcing Network Scyther Security

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Analyzing and Developing the Security of DNS-Based Authentication in Wireless Sensor Network

Analyzing and Developing the Security of DNS-Based Authentic...

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

作者： Umesh Chand Sharma Jambi Ratna Raja Kumar Meenakshi Kumari D. Anandhasilambarasan Latha B Prranjali Jadhav Department of Computer Science Mangalayatan University Aligarh India Department of Computer Engineering Genba Sopanrao Moze College of Engineering Balewadi Pune Maharashtra India Faculty of Business Mangagement & Commerce Usha Martin University Ranchi India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Information Technology Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA

Analyzing and developing the safety of DNS-based total Authentication in Wireless Sensor Networks (WSNs) is a critical research vicinity that is being actively pursued. While preceding work has blanketed authentication strategies to relax the communications between a node and its base station, our studies focus on analyzing the authentication schemes used to set up a comfy verbal exchange in a wireless Sensor network among nodes. We look at the assets of Symmetric and asymmetric Authentication Protocols for WSNs and examine their effectiveness in terms of their authentication effectiveness and overhead. We also discuss how they can be incorporated with current authentication schemes used for WSNs. We then gift a singular protocol that uses DNS-based Authentication to safely set up a verbal exchange in a Wi-Fi Sensor network. Ultimately, we put in force and tested our protocol and the degree of its overall performance in simulated surroundings. Our experiments' results show that our protocol gives an effective authentication answer for WSNs.

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