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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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Hybrid Deep Learning for Thai Cannabis Plant Classification: YOLO+CNN Approach 3

Hybrid Deep Learning for Thai Cannabis Plant Classification:...

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3rd International Conference on Innovative Mechanisms for Industry Applications, ICIMIA 2023

作者： Trivedi, Naresh Kumar Maheshwari, Himani Tiwari, Raj Gaurang Gautam, Vinay Agarwal, Ambuj Kumar Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University School of Computing Uttrakhand Dehradun India Sharda University Sharda School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350343632

Cannabis is a plant species with various subspecies, and its classification is vital for both medical and regulatory purposes. In the context of Thailand, where cannabis has significant cultural and economic importance, accurate identification of different cannabis subspecies is of utmost importance. This work presents a novel approach for the classification of Thai cannabis plants using a hybrid deep learning model that combines You Only Look Once (YOLO) and Convolutional Neural Networks (CNN). The proposed model is evaluated against other deep learning models commonly used for image classification, and it demonstrates exceptional performance, achieving an impressive accuracy rate of 95.37%. Experimental results show that the YOLO+CNN hybrid model outperforms other state-of-the-art deep learning models, in terms of classification accuracy. The proposed model seems efficient due to the YOLO model's ability to precisely localize cannabis plants in images and the CNN's capability to extract fine-grained features for accurate classification. The high classification accuracy achieved by the hybrid model has significant implications for applications such as monitoring the growth of specific cannabis subspecies, ensuring product quality in the cannabis industry, and aiding law enforcement in identifying illegal cannabis plants. This research paves the way for the development of more efficient and accurate tools for cannabis classification, benefiting both the scientific community and society at large. © 2023 IEEE.

关键词： Convolutional Neural Networks Deep Learning Hybrid Model Image Classification Plant Classification Thai Cannabis You Only Look Once

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Design and Development of an IoT Based Smart Irrigation System to Enhance Agricultural Productivity Using ESP8266 Wi-Fi Enabled Controller 5

Design and Development of an IoT Based Smart Irrigation Syst...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Parashar, Vivek Singh, Megha Senthamarai, M. Poonkuzhali, P. Vadar, Parashuram Shankar Swapna, B. School of Computing Science and Engineering Vit Bhopal University Bhopal India Oriental University Department of Computer Science and Engineering Indore India Nandha Engineering College Department of Artificial Intelligence and Data Science Tamilnadu Erode India R.M.D. Engineering College Department of Ece Kavaraipettai India Yashwantrao Chavan School of Rural Development Shivaji University Maharashtra Kolhapur India Dr Mgr Educational and Research Institute Chennai India

ISBN: (纸本)9798350379945

Innovative solutions in many fields, including agriculture, have been made possible by the widespread use of Internet of Things (IoT) technology. Our goal in this research was to increase crop yields by developing a smart irrigation system that would use the IoT. An ESP8266 Wi-Fi-enabled controller serves as the center of the system, collecting data and making decisions all at once. Important parts include sensors for soil moisture, air temperature and humidity, light intensity, water level, a pump to move water, and the ability to connect to a cloud service for data storage and analysis. Water level, soil moisture, temperature, humidity, and light intensity are some of the environmental characteristics that must be continuously monitored in order for the system to function. The data is sent to the main controller in real-time by the sensors using Wi-Fi. Based on predefined thresholds and algorithms, the system dynamically adjusts irrigation schedules to optimize water usage and enhance crop growth. To evaluate the system's performance, sensor readings from multiple trials were analyzed. Results indicate that the system effectively maintains optimal environmental conditions for plant growth, leading to improved agricultural productivity. The integration with a cloud platform enables remote monitoring and control, facilitating efficient water resource management and decision-making for farmers. © 2024 IEEE.

关键词： Soil moisture

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Multivariable Decoupling Control of Civil Turbofan Engines Based on Fully Actuated System Approach

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Journal of Systems science & Complexity 2023年第3期36卷 947-959页

作者： PENG Kai WANG Hongxia ZHANG Huanshui ZHANG Zhaorong YANG Fan School of Power and Energy Northwestern Polytechnical UniversityXi’an 710072China School of Electrical and Automation Engineering Shandong University of Science and TechnologyQingdao 266590China Department of Applied Mathematics The Hong Kong Polytechnic UniversityHung HomKowloonHong KongChina. Xi’an Aeronautics Computing Technique Research Institute AVICXi’an 710068China

Gas turbine engines must be operated by means of control,and how to achieve multivariable control decoupling with aero-engine control constraints is an open thorny issue attracting increasingly more *** paper considers the multivariable decoupling problems of aero-engines by using a compound controller,which originates from the fact that it is impossible to eliminate all the nonlinear dynamics of system to obtain desired constant linear closed-loop system by using full actuated control because of modeling errors and some physical *** controllers are involved in the compound *** is a fully actuated controller and the other is classical feedback *** order to use fully actuated control and maintain the accuracy of engine model,a full state scheduling linear parameter-varying(LPV)modeling method is proposed based on fuzzy neural network *** a general input matrix of the system,its generalized inverse is applied to design fully actuated controller to result in a pseudolinear *** with a feedback controller and control limiter,the control synthesis is *** simulation shows that the proposed method is possessed of a better decoupling and tracking effect compared with traditional control approach.

关键词： Compound control decoupling control fully actuated control turbofan engine

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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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Method for Classification of Cancers with Partial Least Squares Regression as Feature Selector with Kernel SVM

Method for Classification of Cancers with Partial Least Squa...

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2022 International Conference for Advancement in Technology, ICONAT 2022

作者： Koul, Nimrita Manvi, Sunilkumar S Gardiner, Bryan School of Computer Science and Engineering REVA University Bangalore India School of Computing Engineering and Intelligent Systems Ulster University Northern Ireland United Kingdom

ISBN: (纸本)9781665425773

Classification of cancers according to the exact place of origin in the body is an important research problem that is being addressed both clinically and computationally. Application of data science and machine learning to a vast volume of imaging and genomics data regarding cancers has enabled computational researchers to accurately classify the tumor samples according to their place of origin. In current work, we developed a method to classify tumors using partial least squares regression as feature selector and support vector machine classifiers. We have evaluated our approach on three cancer gene expression datasets and found classification accuracies of 100% in some circumstances. The comparison is conducted with standard classification methods like Decision Trees and simple Support Vector Machines with respect to standard performance parameters and the time taken for classification. The comparison in terms of training and testing accuracies and the time taken for classification results show that our method performs consistently better than conventional methods. © 2022 IEEE.

关键词： Support vector machines

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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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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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Leaf Detection: Techniques and Applications

Leaf Detection: Techniques and Applications

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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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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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