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Sustainable Emerging Innovations in engineering and Technology (ICSEIET), International Conference on

作者： Rahul Kumar Arpit Pal Tejendra Singh Alok Singh Chauhan Department of Computer Science and Engineering School of Computing Science and Engineering Galgotias University Greater Noida India School of Computing Science & Engineering Galgotias University Greater Noida India

Leaf detection is a critical task in various fields, including agriculture, ecology, and botany, with applications ranging from plant species identification to disease diagnosis. This research paper explores the techniques used for leaf detection, the applications where it is employed, and the challenges faced in this domain. Traditional image processing techniques, machine learning approaches, deep learning-based methods, and hybrid approaches are examined and compared. The applications of leaf detection include agriculture and crop monitoring, plant species identification, disease diagnosis and management, ecological studies, and remote sensing. The challenges in leaf detection arise from the variability in leaf shapes and textures, occlusion and complex backgrounds, realtime processing requirements, and robustness to environmental factors. Future directions and emerging trends in multi-modal leaf detection, domain adaptation, integration of remote sensing technologies, and advanced image analysis techniques are discussed. The findings of this research have implications for further research and practical applications, highlighting the need for more robust and accurate leaf detection techniques and the utilization of advanced technologies. The research contributes to a deeper understanding of plant ecosystems, optimization of agricultural practices, and improved management of natural resources.

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GPNST: An Improved Graphical Password Authentication Scheme Leveraging Neural Style Transfer Technique on Smartphones

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International Journal of Information Security 2025年第3期24卷 1-34页

作者： Ray, Palash Giri, Debasis Obaidat, Mohammad S. Jana, Manasi Sasmal, Mahuya Alenazi, Mohammed J. F. Haldia Institute of Technology West Bengal Haldia India Maulana Abul Kalam Azad University of Technology West Bengal Nadia India King Abdullah II School of Information Technology The University of Jordan Amman Jordan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Department of Computational Intelligence School of Computing SRM University SRM Nagar TN Kattankulathur603203 India School of Engineering The Amity University UP Noida201301 India King Saud University Riyadh Saudi Arabia

The growing prevalence of smartphones and the increasing dependence on digital services have made it crucial to have authentication techniques that are both secure and user-friendly. This study examines the effectiveness and usability of graphical passwords named GPNST as an alternative authentication method on mobile devices. Graphical passwords use users’ capacity to remember visuals or patterns, offering a possible equilibrium between security and user pleasure. GPNST is a two-factor authentication technique that requires users to remember content and style images, or stylized images, as portfolio images. Additionally, a unique key token is assigned to each user device and securely sent to the server using the reversible data hiding approach, therefore mitigating several potential attacks. The research uses a combination of quantitative analysis to evaluate the strength of passwords and qualitative evaluations to measure user satisfaction and memorability. We investigate the influence of several design elements in graphical passwords, such as the intricacy of images and the layout of grids, on both the level of security and ease of use. In addition, the study evaluates the robustness of graphical passwords against popular forms of attacks, such as shoulder surfing and brute-force efforts. The findings provide valuable insights regarding the practicality of incorporating graphical passwords into smartphone authentication systems. This has implications for designers, developers, and users who are looking for secure and user-friendly solutions in the constantly changing field of mobile technology. The GPNST scheme demonstrates outstanding performance in our assessment test, with an accuracy rate of up to 94.80% and a login time of 9.61 seconds. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Static random access storage

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Not All Pairs are Equal: Hierarchical Learning for Average-Precision-Oriented Video Retrieval 24

Not All Pairs are Equal: Hierarchical Learning for Average-P...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Yang Xu, Qianqian Wen, Peisong Dai, Siran Huang, Qingming School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences Beijing China Institute of Computing Technology Chinese Academy of Sciences BDKM University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798400706868

The rapid growth of online video resources has significantly promoted the development of video retrieval methods. As a standard evaluation metric for video retrieval, Average Precision (AP) assesses the overall rankings of relevant videos at the top list, making the predicted scores a reliable reference for the users. However, recent video retrieval methods utilize pair-wise losses that treat all sample pairs equally, leading to an evident gap between the training objective and evaluation metric. To effectively bridge this gap, in this work, we aim to address two primary challenges: a) The current similarity measure and AP-based loss are suboptimal for video retrieval;b) The noticeable noise from frame-to-frame matching introduces ambiguity in estimating the AP loss. In response to these challenges, we propose the Hierarchical learning framework for Average-Precision-oriented Video Retrieval (HAP-VR). For the former challenge, we develop the TopK-Chamfer Similarity and QuadLinear-AP loss to measure and optimize video-level similarities in terms of AP. For the latter challenge, we suggest constraining the frame-level similarities to achieve an accurate AP loss estimation. Experimental results present that HAP-VR outperforms existing methods on several benchmark datasets, providing a feasible solution for video retrieval tasks and thus offering potential benefits for the multi-media application. © 2024 Owner/Author.

关键词： Self-supervised learning

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Bacterial Classification in Microbiology using BiT

Bacterial Classification in Microbiology using BiT

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Communication, computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Shaik Shifa Tanveer Vishali Kumar Deva Hema Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

Bacteria are microscopic living organisms which have only one cell in it and they are classified into many different types. Microbial classification holds a crucial role in shaping our understanding of the microscopic realm, impacting scientific exploration, medical practices, environmental sciences, and various industrial applications. In the medical field, microbial classification is indispensable for disease diagnosis, treatment, and prevention. In the industrial sector, microbial classification is fundamental for bioprocessing, biofuel production, and bioremediation. Understanding microbial characteristics enables industries to harness their capabilities for efficient production and environmental remediation. It plays very important role in both environment and human life however some other kinds of bacteria are considered as very harmful. Harmful bacteria can cause serious diseases and illness. So it’s very important to classify bacteria for proper diagnosis. Given the vast diversity of microorganisms and difference in appearance, manually classifying bacteria is a challenging task which takes lot of time. So,This paper aims to streamline this arduous task by employing deep learning techniques such as using ResNet50 architecture and using BiT. The integration of BiT and ResNet at different stages ensures efficient feature extraction and classification, leading to high accuracy and reliability in bacterial classification tasks.

关键词： Microscopy Prevention and mitigation Production Computer architecture Bacteria Telecommunication computing Reliability Medical diagnostic imaging Tuning Residual neural networks

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A Classification of Face Recognition System Using Faster Region-Based Convolutional Neural Network 2

A Classification of Face Recognition System Using Faster Reg...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Maheswari, M. Alabdeli, Haider Aruna, T.M. Reddy, R. Archana Revathi, R. School of Computing Srm Institue of Science and Technology Department of Computational Intelligence Kattankulathur India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Artificial Intelligence and Machine Learning Bengaluru India School of Computer Science and Engineering Sr University Warangal India Karpagam Academy of Higher Education Department of Computer Science Echanari Coimbatore India

ISBN: (纸本)9798350373110

In recent times, face recognition is a kind of biometric authentication which is widely used for security purpose in many areas like image processing, pattern recognition. However, recognizing the human face was very difficult as the poses in the input images vary, various conditions of light, brightness, expressions, ages. To overcome from it, a proposed method was introduced in this paper known as Faster Region-based Convolutional Neural Network (RCNN). The main advantage of using the proposed method is it will minimize the training process time as it consisting of two fully connected layers namely classifier and regression layer. Initially, the proposed method was implemented on ORL database consisting of 40 classes, then the images in the database undergoes image resizing and enhancing in the preprocessing stage. Where, Image resizing id done to reduce images size so that every image is same in size that will minimize the processing time and image enhancing was done to adjust the brightness of the image. In further, for extracting the features from preprocessed images, CNN is used, so that extracted features are given to the classification stage to detect the face of human or not. In detecting the faces, proposed Faster RCNN achieved high accuracy of 98.64% when compared with other existing methods like IFC, EFRA and VGG16. © 2024 IEEE.

关键词： Convolutional neural networks

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Thermal transport in lithium-ion battery: A micro perspective for thermal management

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Frontiers of physics 2022年第1期17卷 143-153页

作者： Changqing Xiang Cheng-Wei Wu Wu-Xing Zhou Guofeng Xie Gang Zhang College of Information Science and Engineering Jishou UniversityJishou416000China School of Materials Science and Engineering&Hunan Provincial Key Laboratory of Advanced Materials for New Energy Storage and Conversion Hunan University of Science and TechnologyXiangtan411201China Institute of High Performance Computing A*STAR Singapore138632Singapore

In recent years, lithium ion (Li-ion) batteries have served as significant power sources in portable electronic devices and electric vehicles because of their high energy density and rate capability. There are growing concerns towards the safety of Li-ion batteries, in which thermal conductivities of anodes, cathodes, electrolytes and separator play key roles for determining the thermal energy transport in Li-ion battery. In this review, we summarize the state-of-the-art studies on the thermal conductivities of commonly used anodes, cathodes, electrolytes and separator in Li-ion batteries, including both theoretical and experimental reports. First, the thermal conductivities of anodes and cathodes are discussed, and the effects of delithiation degree and temperature of materials are also discussed. Then, we review the thermal conductivities of commonly used electrolytes, especially on solid electrolytes. Finally, the basic concept of interfacial thermal conductance and simulation methods are presented, as well as the interfacial thermal conductance between separator and cathodes. This perspective review would provide atomic perspective knowledge to understand thermal transport in Li-ion battery, which will be beneficial to the thermal management and temperature control in electrochemical energy storage devices.

关键词： lithium ion batteries thermal management phonon transport interfacial thermal transport amorphous materials

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An Enhanced Convolutional Neural Network Based on L1 Regularization for Segmentation of Coronary Arteries in X-Ray Angiograms 5

An Enhanced Convolutional Neural Network Based on L1 Regular...

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5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Wahid, Fazli Ma, Yingliang Sheikh, Ramla Aamir, Muhammad University of East Anglia School of Computing Sciences Norwich United Kingdom University of Haripur Department of Information Technology Khyber Pakhtunkhwa Pakistan University of Derby Data Science Research Centre Colloge of Science and Engineering Derby United Kingdom

ISBN: (纸本)9798350394764

The automatic segmentation of coronary arteries in X-Ray angiograms has a significant role in identification and detection of various abnormalities in vessels which can be adequately helpful in computer aided diagnosis (CAD) for supporting in analyzing the coronary heart diseases. Previously many machine learning (ML) algorithms and deep learning (DL) models have been applied for this purpose but all of them are suffered from some serious drawbacks when applied in their standardized working formats which leads to low performance of these models. Therefore, in this work an enhanced convolutional neural network named eCNN which is based on L1 regularization has been applied for automatic segmentation of coronary arteries. It has been observed that incorporating L1 regularization with different coefficient values has distinct effects on the working mechanism of CNN architecture resulting in improving its performance. In the proposed method, firstly, Gaussian Matched Filters (GMF) have been applied to enhance the quality of X-Ray angiograms. In segmentation stages, the eCNN has been applied for the segmentation. In this stage, dual L1 regularization (coefficient: 0.001 for convolutional and 0.01 for dense layers) improves model interpretability and generalization, facilitating effective segmentation of arteries. Finally, standard performance evaluation parameters have been used to evaluate the performance of the proposed model and compared with state-of-the-art techniques. © 2024 IEEE.

关键词： Convolutional neural networks

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Brain Tumor detection Using Machine Learning and Deep Learning Approaches

Brain Tumor detection Using Machine Learning and Deep Learni...

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2022 IEEE International Conference on Advances in computing, Communication and Applied Informatics, ACCAI 2022

作者： Khan, Oneza Tehreem Rajeswari, D. School of Computing College of Engineering and Technology Department of Data Science and Business Systems Kattankulathur603203 India

ISBN: (纸本)9781665495295

Recent years has provided extremely interesting and exciting developments and applications of Machine Learning (ML) and Deep Learning (DL) to automize healthcare delivery. ML techniques are surpassing human performances in detecting, predicting, diagnosing any disease and even recommending treatments for the same. ML along with Deep Learning has revolutionized Medical Imaging Technology. Brain Tumor detection is one of the many healthcare area Artificial Intelligence has marveled at. A tumor is basically an area or part of the body suffering by swelling caused by distorted tissues where the cells start multiplying uncontrollably. Magnetic Resonance Imaging (MRI) is the technique used to detect the tumor. MRI is quite commonly used in medical science to detect abnormalities in any part of the body. Tumor in the brain is far more perilous and different to treat than in any other part of the body which makes the early prediction and monitoring of brain tumor extremely expedient. This paper discusses the various algorithms, techniques, new approaches and their comparison with the existing ones and the general process of brain tumor detection with the help of MRI. © 2022 IEEE.

关键词： Magnetic resonance imaging

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Chronic Kidney Disease Analysis using Various Biological Parameters under Machine Learning Techniques

Chronic Kidney Disease Analysis using Various Biological Par...

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Automation, computing and Renewable Systems (ICACRS), International Conference on

作者： M. Suresh E. Nirmala M. Maragatharajan Manish Yadav School of Computing Science and Engineering VIT Bhopal University Kotri Kalan Madhya Pradesh School of Computing Science Engineering and Artificial Intelligence VIT Bhopal University Kotri Kalan Madhya Pradesh

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

Kidney chronic disease (KCD) has become one of the most critical illnesses which should be diagnosed properly within a certain limit of time for better treatment. Nowadays different algorithms being used for treatment of patients. By using the best predictory model, doctor can easily predict the disease on time. For this verdict, Kidney Chronic Disease Prediction has been discussed in this article. In this article various datasets have been used by the different article authors to predict the disease which are pre-processed with different methods like Univariate Feature Selection (UFS) and Recursive Feature Elimination (RFE), Data mining techniques, etc. which are further used to train and test various ML models get the accuracy by predictory models. The scores gained by the predictory models is then validated to check the precision of the output accuracy of by the model using methods like performance matrices, various cross validation techniques, Mathew Correlation Coefficient (MCC). This whole article is all around the analyses of different articles based on the machine learning and validation techniques with respect to the accuracy. Most of the models attained the accuracy of 99 percent and above. In this article, along with machine learning models it also discussed about two neural networks, which attained the accuracy between 93 to 98% with a good precision value.

关键词： Analytical models Renewable energy sources Accuracy Biological system modeling Machine learning Predictive models Feature extraction Data models Tuning Diseases

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A Web Service Recommendation Method Based on Adaptive Gate Network and xDeepFM 1

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22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Tao, Zhi Cao, Buqing Ye, Hongfan Kang, Guosheng Peng, Zhenlian Wen, Yiping School of Computer Science and Engineering and Hunan Key Laboratory of Service Computing and New Software Service Technology Hunan University of Science and Technology Hunan Xiangtan China School of Computer and Artificial Intelligence Huaihua University Huaihua China

ISBN: (数字)9783031226779

ISBN: (纸本)9783031226762

With the rapid development of service computing technology, more and more companies and organizations are encapsulating and publishing their operational data or resources to the Internet in the form of Web services, resulting in an exponential increase in the number of Web services. To automatically generate or recommend a group of Web services according to the user's natural language requirements description, to build a Mashup application to meet the user's requirement is a hot topic in service computing. Some researchers enhance the quality of Web service recommendation by using auxiliary information into the recommendation system. However, they mainly focus on adding external information (e.g., pre-training of external corpora) to enhance semantic features, while some internal statistical features of the corpus such as word distribution on labels and frequency are not fully exploited. Compared to other exterior knowledge, statistical features are naturally compatible with Web service recommendation tasks. To fully exploit the statistical features, this paper proposes a Web service recommendation method based on adaptive gate network and xDeepFM model. In this method, firstly, the description document of Web services is taken as the basic corpus, the semantic and statistical information in the corpus are mined by utilizing the adaptive gate network, and the statistical features are encoded by a variational encoder. Secondly, the similarity between Web services is derived from the semantic features, at the same time the popularity and co-occurrence of Web services are calculated. Thirdly, the xDeepFM model is used to mine the explicit and implicit higher-order interactions in the sparse matrix which consists of the above information to recommend Web services for Mashup application. Finally, a multiple sets of experiments based on the dataset crawled from the ProgrammableWeb have been conducted to evaluate the proposed method and the experimental result shows that t

关键词： Web services

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