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2023 International Conference on Advancement in Computation and computer Technologies, InCACCT 2023

作者： Gupta, Vaishali Patel, Ruchi Institute of Engineering and Science Department of Computer Science and Engineering IPS Academy MP Indore India Gyan Ganga Institute of Technology and Sciences Department of Computer Science and Engineering MP Jabalpur India

ISBN: (纸本)9798350396485

People all over the world are afflicted by lung disease, which is a prevalent illness. The earliest possible diagnosis of lung illness is necessary. Due to this, a number of deep learning models for processing image data evolved over time. Advances in deep learning have helped identify lung disorders and detect them in diagnostic images. Various types of modern deep learning techniques, including vanilla neural networks, convolutional neural networks (CNN), visual geometry group (VGG) dependent neural networks, and capsule networks, can be used to classify lung cancer. The basic CNN performs poorly when trying to handle rotated, curved, or other unusual image orientations. As a result, the proposed work explored using principal components analysis (PCA) and the VGG16 deep learning architecture. In order to extract significant features from an image dataset, PCA is generally used. The Chest X-ray of National Institutes of Health (NIH) is taken as dataset which contains 112,120 images of X-ray of 30,805 different patients. In the current work, accuracy is used to evaluate performance, and VGG 16's accuracy is 79.1%. The PCA approach has raised it by up to 96%. Additionally, the proposed architecture is contrasted with current work. © 2023 IEEE.

关键词： Principal component analysis

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Achieving high strength-ductility synergy in a Mg_(97)Y_(1)Zn_(1)Ho_(1) alloy via a nano-spaced long-period stacking-ordered phase

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Journal of Magnesium and Alloys 2023年第4期11卷 1321-1331页

作者： Mingyu Fan Ye Cui Yang Zhang Xinghao Wei Xue Cao Peter K.Liaw Yuansheng Yang Zhongwu Zhang Key Laboratory of Superlight Materials and Surface Technology Ministry of EducationCollege of Materials Science and Chemical EngineeringHarbin Engineering UniversityHarbin 150001PR China College of Computer Science and Technology Harbin Engineering UniversityHarbin 150001PR China Department of Materials Science and Engineering The University of TennesseeKnoxvilleTN 37996-2100USA Institute of Metal Research Chinese Academy of SciencesShenyang 110016PR China

Achieving high strength in Mg alloys is usually accompanied by ductility ***,a novel Mg97Y1Zn1Ho1 at.%alloy with a yield strength of 403 MPa and an elongation of 10%is *** strength-ductility synergy is obtained by a comprehensive strategy,including a lamella bimodal microstructure design and the introduction of nano-spaced solute-segregated 14H long-period stacking-ordered phase(14H LPSO phase)through rare-earth Ho *** lamella bimodal microstructure consists of elongated un-recrystallized(un-DRXed)coarse grains and fine dynamically-recrystallized grains(DRXed regions).The nano-spaced solute-segregated 14H LPSO phase is distributed in DRXed *** outstanding yield strength is mainly contributed by grain-boundary strengthening,18R LPSO strengthening,and fiberlike reinforcement strengthening from the nano-spaced 14H LPSO *** high elongation is due primarily to the combined effects of the bimodal and lamellar microstructures through enhancing the work-hardening capability.

关键词： Mg wrought alloy Mechanical properties Long-period stacking-ordered(LPSO)phase Age-strengthening behavior Strengthening mechanism

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Analysis of Skin Lesions for Identifying of Skin Cancer Melanoma using Customized CNN Model with Transfer Learning 4

Analysis of Skin Lesions for Identifying of Skin Cancer Mela...

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4th IEEE Global Conference for Advancement in technology, GCAT 2023

作者： Sinchana, N.S. Jeevitha, K. Kumar Swamy, S. Kodipalli, Ashwini Rao, Trupthi Department of Computer Science and Engineering Global Academy of Technology Karnataka Bangalore India Department of Artificial Intelligence and Data Science and Engineering Global Academy of Technology Karnataka Bangalore India

ISBN: (纸本)9798350305258

Melanoma is a type of cancer known to be the most dangerous and is called the life threatening disease. Every year, there is rise in number of people being diagnosed with melanoma which is more frequent among the males than the females. This type of skin cancer has an uncontrollable growth of cells that can grow really faster, if not treated it can spread all over the body. Hence this disease needs to be detected at the early stages so that the patients can be treated properly. Machine learning approaches were applied to detect skin cancer but the drawbacks were that the performance to identify the cancer was limited. Therefore, we constructed a model by utilizing a Convolution Neural Network of the most advanced deep learning where we conduct investigations for discovering the melanoma skin cancer on the basis of the skin lesion. In this research, using transfer learning techniques ResNet and VGG based models have been brought together for melanoma skin detection. The procedures were applied to train these models using optimizers like RMSprop, Adam, Stochastic Gradient Descent (SGD) and findings were presented with the support of accuracy, confusion matrix, sensitivity, specificity and precision. © 2023 IEEE.

关键词： Convolutional Neural Network (CNN) Deep Learning Melanoma skin cancer Transfer Learning

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I $^{2}$ RNN: An Incremental and Interpretable Recurrent Neural Network for Encrypted Traffic Classification

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IEEE Transactions on Dependable and Secure Computing 2023年 1-14页

作者： Song, Zhuoxue Zhao, Ziming Zhang, Fan Xiong, Gang Cheng, Guang Zhao, Xinjie Guo, Shize Chen, Binbin College of Computer Science and Technology Zhejiang University Hangzhou China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Science and Engineering Southeast University Nanjing China Information Systems Technology and Design Singapore University of Technology and Design Singapore

Traffic classification occupies a significant role in cybersecurity and network management. The widespread of encryption transmission protocols such as SSL/TLS has led to the dominance of deep learning based approaches. In cybersecurity, strong adversaries often complicate their strategies by constantly developing emerging attacks. Meanwhile, security practitioners desire to grasp the reasons for inference results. However, existing deep learning approaches lack efficient adaptation for incremental traffic types and often have less interpretability. In this paper, we propose I $^{2}$ RNN, an Incremental and Interpretable Recurrent Neural Network for encrypted traffic classification. The I $^{2}$ RNN proposes a novel propagation process to extract the sequence fingerprints from sessions with local robustness. Meanwhile, this proposal provides interpretability including time-series feature attribution and inter-class similarity portrait. Moreover, we design I $^{2}$ RNN in an incremental manner to adapt to emerging traffic types. The I $^{2}$ RNN only needs to train an additional set of parameters for the newly added traffic type rather than retraining the whole model with the entire dataset. Extensive experimental results show that our I $^{2}$ RNN can achieve remarkable performance in traffic classification, incremental learning, and model interpretability. Compared with other local interpretability methods, our I $^{2}$ RNN exhibits excellent stability, robustness, and effectiveness in the interpretation of network traffic data. IEEE

关键词： Cryptography

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Hybrid Edge-Cloud Approach for Renewable Energy Management Using Deep Learning with Predictive Analytics

Hybrid Edge-Cloud Approach for Renewable Energy Management U...

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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Rekha, V. Sharmila, N. Komala, M. Pavithra, G.S. Mallikarjunaswamy, S. Naveen, K.B. Shilpa, M. Sheela, S. Department of Computer Science and Engineering Bangalore India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Computer Science and Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Bgs Institute of Technology Adichunchanagiri University Dept of Electronics and Electrical Engineering BG Nagara India Bengaluru Institute of Technology Dept of Information Science and Engineering Bengaluru India Global Academy of Technology Dept of Computer Sceinec and Engineering Bengaluru India

ISBN: (纸本)9798350388602

The integration of renewable energy sources into smart grids requires advanced management systems to optimize energy production, storage, and distribution. Conventional methods like Static Energy Allocation (SEA) and Rule-Based Control (RBC) struggle with real-time adaptability, leading to inefficiencies in handling the variable nature of renewable energy. SEA and RBC lack the predictive capabilities needed to effectively manage dynamic energy generation, resulting in increased energy losses and operational *** paper proposes a Hybrid Edge-Cloud Renewable Energy Management System (HECREMS) using Deep Learning with Predictive Analytics (DLPA). HECREMS integrates edge computing for real-time data processing with cloud computing for enhanced predictive analytics, providing a robust framework for efficient energy management. The proposed system leverages the low latency of edge devices and the computational power of the cloud to improve energy forecasting and decision-making. HECREMS outperforms traditional SEA and RBC methods, achieving a 0.25% improvement in energy utilization efficiency, a 0.20% reduction in operational costs, and a 0.30% increase in system responsiveness. By addressing the drawbacks of conventional approaches, HECREMS demonstrates significant advancements in the efficient and sustainable management of renewable energy within smart grids. © 2024 IEEE.

关键词： Cloud analytics

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Customized CNN Model for Alzheimer's Disease Stages Classification and Comparison with Transfer Learning 1

Customized CNN Model for Alzheimer's Disease Stages Classifi...

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1st International Conference on Recent Advances in Information technology for Sustainable Development, ICRAIS 2023

作者： Sindhu, B. Prasad, Rishitha Kodipalli, Ashwini Kumar Swamy, S. Rao, Trupthi Global Academy of Technology Department of Computer Science and Engineering Karnataka Bangalore India Global Academy of Technology Department of Artificial Intelligence and Data Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350306637

Alzheimer's disease, the predominant form of dementia, progressively worsens over time, initially causing mild memory loss and eventually severely hampering daily tasks and communication abilities. This degenerative condition affects regions of the brain integral to cognition, memory, and language. Due to its intricate nature, symptom experiences and progression can vary among individuals. This study proposes a predictive model to discern Alzheimer's stages. Leveraging 'deep learning' techniques through convolutional neural networks (CNNs), the algorithm is trained on an extensive dataset of brain MRI scans. It learns to recognize pivotal features and correlate them with diverse disease phases. The Customized CNN model's accuracy of 94.37% surpasses alternative machine learning methods, including transfer learning. This precision empowers early prediction, significantly benefiting patient care and therapeutic strategies, thereby enhancing Alzheimer's disease management. © 2023 IEEE.

关键词： Convolutional neural networks

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Enhancing Autism Spectrum Disorder Identification: A Machine Learning Approach using CatBoost

Enhancing Autism Spectrum Disorder Identification: A Machine...

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2024 IEEE International Conference on Innovation and Novelty in engineering and technology, INNOVA 2024

作者： Rathod, Vijayalaxmi N. Goudar, R.H. Dhananjaya, G.M. Patil, Minal Hukkeri, Geetabai S. Kaliwal, Rohit B. Dept. of Computer Science and Engineering Visvesvaraya Technological University Karnataka Belagavi India Dept. of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Bengaluru India

ISBN: (纸本)9798331505134

Autism is a complex neurodevelopment condition that affects an individual's behavior, communication, and social interaction. Identification is a critical endeavor in healthcare, necessitating accurate and efficient diagnostic methodologies, early identification is pivotal for timely intervention and improved outcomes in affected individuals. This paper investigates the use of machine learning algorithms, specifically CatBoost, for Autism trait identification using heterogeneous datasets from toddlers, children, adolescents, and adults. The research investigates the performance of CatBoost in handling mixed data types, including categorical features and missing values, without extensive preprocessing. Utilizing gradient boosting on decision trees, CatBoost demonstrates its efficacy in capturing complex relationships between features, facilitating high predictive accuracy in autism identification. Through rigorous evaluation metrics such as accuracy, precision, recall, and F1 score, the designed system achieves a precise accuracy of 92% for adult datasets and 88% for child and adolescent datasets. This study delineates CatBoost's robustness across diverse age groups, providing insightful information on its applicability for Autism Spectrum Disorder diagnosis in the healthcare domain. © 2024 IEEE.

关键词： Diseases

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Automated Land Cover Classification in Urban Environments with Deep Learning-Based Semantic Segmentation

Automated Land Cover Classification in Urban Environments wi...

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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Sheela, S. Jyothi, S. Latha, A.P. Ganesh, H.J. Komala, M. Naveen Kumar, C. Mallikarjunaswamy, S. Sharmila, N. Global Academy of Technology Department of Computer Science and Engineering Bengaluru India Dayananda Sagar College of Engineering Department of Information Science and Engineering Bengaluru India Global Academy of Technology Department of Information Science and Engineering Bengaluru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Jss Science and Technology Universit Dept of Electronics and Electrical Engineering Mysuru India

ISBN: (纸本)9798350388602

Partisan symbolism has become a vital asset for various applications, including land cover classification, urban planning, disaster management, and environmental monitoring. In this study, a deep learning approach is proposed for the semantic segmentation of satellite images. The proposed model leverages a multi-level U-Net architecture, which has shown effectiveness in various image segmentation tasks. The dataset used in this study comprises satellite images with corresponding ground truth masks labeling different land cover classes, such as buildings, roads, vegetation, water bodies, and unlabeled regions. The data is preprocessed, and the ground truth masks are encoded into categorical labels to facilitate model training. The model is trained using a combination of pixel-wise crossentropy loss and the Jaccard coefficient as evaluation metrics to optimize performance. Test results demonstrate the effectiveness of the proposed approach in accurately segmenting satellite images, achieving high accuracy and Jaccard coefficient values on both training and validation datasets. Additionally, a comprehensive analysis of the model's performance is provided, discussing potential applications and future directions for satellite image segmentation tasks. The trained model is saved for further deployment and integration into satellite image processing pipelines, offering valuable insights for various remote sensing applications. © 2024 IEEE.

关键词： Satellite imagery

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Smart Contract Based Carpooling Application for Secure and Efficient Ride Sharing

Smart Contract Based Carpooling Application for Secure and E...

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Recent Advances in Systems science and engineering (RASSE), IEEE International Conference on

作者： Ramani Bai V Pranav Sudhir Rashmi Joshi Vighnesh Nair Vishnu Anilkumar Department of Computer Science Engineering Vidya Academy of Science and Technology Thrissur Kerala India

Carpooling offers an effective solution to mitigate pollutant emissions and alleviate traffic congestion. This paper presents a novel system designed to address the shortcomings of centralized ride-sharing services by leveraging smart contracts. By ensuring data security, confidentiality, and privacy, the proposed system initiates the decentralization process for carpooling systems. Users can assume the role of either a driver or a passenger, enabling them to publish rides or search for and book desired rides. Smart contracts facilitate seamless execution of user requests, triggered by events such as ride creation or booking. The fare calculation is based on the passenger's route distance, current fuel price, and the driver's car mileage, resulting in significantly reduced costs. Upon booking a ride, the fare amount is debited from the passenger and credited to the driver only upon the passenger reaching their destination. The system's overarching objective is to simplify people's lives by minimizing travel expenses and traffic congestion, all while upholding user security.

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An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

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computer Systems science & engineering 2023年第2期44卷 1187-1200页

作者： G.Rajeshkumar M.Vinoth Kumar K.Sailaja Kumar Surbhi Bhatia Arwa Mashat Pankaj Dadheech Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerunduraiErode638057Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India Department of Master of Computer Applications M.S.Ramaiah Institute of TechnologyBangalore560054India Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversityRiyadh11533Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipur302017RajasthanIndia

A Mobile Ad hoc Network(MANET)is a group of low-power con-sumption of wireless mobile nodes that conﬁgure a wireless network without the assistance of any existing infrastructure/centralized *** primary aim of MANETs is to extendﬂexibility into the self-directed,mobile,and wireless domain,in which a cluster of autonomous nodes forms a MANET routing *** Intrusion Detection System(IDS)is a tool that examines a network for mal-icious behavior/policy violations.A network monitoring system is often used to report/gather any suspicious attacks/*** IDS is a software program or hardware system that monitors network/security trafﬁc for malicious attacks,sending out alerts whenever it detects malicious *** impact of Dynamic Source Routing(DSR)in MANETs challenging blackhole attack is investigated in this research *** Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a MANET *** system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node ***,KF is used to eliminate synchronisation errors that arise during the sending and receiving *** order to provide an energy-efﬁcient solution and to minimize network trafﬁc,route optimization in MANET by using Multi-Objective Particle Swarm Optimization(MOPSO)technique to determine the optimal num-ber of clustered MANET along with energy dissipation in *** to the researchﬁndings,the proposed CTAA-MPSO achieves a Packet Delivery Ratio(PDR)of 3.3%.In MANET,the PDR of CTAA-MPSO improves CTAA-PSO by 3.5%at 30%malware.

关键词： MANET intrusion detection system cluster kalmanﬁlter dynamic source routing multi-objective particle swarm optimization packet delivery ratio

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