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3rd IEEE International Conference on Sustainable Energy and Future Electric Transportation, SeFet 2023

作者： Verma, Shresth Singh, Aditya H. Bhargava, Kshitij Institute of Infrastructure Technology Research and Management Department of Electrical and Computer Science Engineering Gujarat Ahmedabad380026 India

ISBN: (纸本)9798350319972

Perovskite solar cells (PSCs) are a promising alternative to traditional silicon solar cells due to their low cost, high efficiency, and ease of fabrication. In this study, we employed CH3NH3SnI3 as the absorber layer material in a PSC structure and utilized SCAPS-1D software to simulate the solar cell behavior. The optimized parameters were obtained by considering previous analysis conducted for the proposed structure. The results obtained like Voc, Jsc, FF, and η are 0.81 V, 32.76 mA/cm2, 77.0%, and 21.3% respectively. The simulation results indicate that CH3NH3SnI3 has great potential as a cost-effective and highly efficient absorber material in PSCs for standalone systems. Additionally, we conducted a module-level analysis of the simulated device for a standalone system by employing PVsyst software. Using the electrical parameters obtained from the simulation, the performance of a solar module containing 60 cells with dimensions of (1.038 × 1.038) m2 was analyzed. The analysis focused on evaluating the panel's output power, incident irradiation, and temperature, all of which were calculated for a specific geographic location. © 2023 IEEE.

关键词： Perovskite

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Intrusion Detection of Service Disruption Attack for Industrial Electrical Drive System 15

Intrusion Detection of Service Disruption Attack for Industr...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Oberoi, Vansh Gautam Karthik, V. Dheeba, J. Raja Singh, R. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Vellore India Vellore Institute of Technology School of Electrical Engineering Tamil Nadu Vellore India Automotive Research Center Vellore Institute of Technology School of Electrical Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798350370249

Industrial drive systems play a critical role in modern manufacturing processes, and their reliable operation is essential for productivity. These systems often consist of multiple interconnected control units that communicate through transmission control protocols. The security of these protocols remains vulnerable to attacks if the network is compromised. In this article, the security challenges faced by industrial electrical drive system is investigated and the strategies to enhance their resilience against cyber threats are proposed. The intended drive system is realized with a 2.2 kW squirrel cage induction motor and two-level voltage source inverter controlled using the F28379D microcontroller. The microcontroller is interfaced with the computer through USB for deploying the control program, as well as to monitor and tune the drive parameters. The focus lies on the computer, which provides speed commands to the microcontroller. By implementing these strategies, an effective intrusion detection system is tailored to the unique requirements of industrial electrical drive systems, safeguarding them against service disruption attacks. © 2024 IEEE.

关键词： Microcontrollers

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Forensic Data Storage on Cloud for Secure Access: An Overview 14

Forensic Data Storage on Cloud for Secure Access: An Overvie...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Dudhe, Preeti Gupta, S.R. Department of Information Technology Prof Ram Meghe Institute of Technology & Research Badnera India Department of Computer Science and Engineering Prof Ram Meghe Institute of Technology & Research Badnera Indonesia

The Internet’s popularity has skyrocketed in recent years, and it is critical for providing a wide range of services to people worldwide. Cloud computing, which has risen dramatically in popularity to effectively provide services at a low cost, is one of the most well-known technologies. It provides consumers with on-demand services while minimizing their operating expenses by allocating virtual instances and software services. Due to the availability of incredibly powerful processing and storage at a cheap cost, an adversary or attacker may choose to launch attacks from devices inside or outside the cloud. As a result, resources are being used more frequently, and cloud services are becoming unavailable for long periods of time. This chapter delves into the forensic procedure, the challenges of cloud forensics, and the methods utilized to obtain information in a cloud scenario. The collection of evidence from a variety of sources, including web browsers, cloud storage (such as Google Drive, Dropbox, and Microsoft SkyDrive), cloud log analysis, and corporal indication recording, is critical for discovering the perpetrators of attacks. © Grenze Scientific Society, 2023.

关键词： Web browsers

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Quantum Resnet18 for Classifying and Predicting the Maize Leaf Diseases

Quantum Resnet18 for Classifying and Predicting the Maize Le...

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2024 International Conference on Recent Advances in Electrical, Electronics, Ubiquitous Communication, and Computational Intelligence, RAEEUCCI 2024

作者： Kujani, T. Bhuvanya, R. Sathya, T. Arivubrakan, P. Usha, V. Thangaselvi, R. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Computer Science and Engineering Tamilnadu Chennai India Sri Ramachandra Institute of Higher Education and Research Sri Ramachandra Faculty of Engineering and Technology Tamilnadu Chennai India Sri Sairam Engineering College Computer Science and Business Systems Tamilnadu Chennai India

ISBN: (纸本)9798350354539

The agriculture industry contributes one-third of the worldwide Gross Domestic Product (GDP). Moreover, a substantial number of developing countries rely on their agricultural output as it offers job prospects for a considerable portion of the impoverished population. This necessitates the use of techniques to guarantee the precise and effective identification of plant diseases to minimize any detrimental consequences on the crops. Recently, the application of quantum-enhanced techniques to machine learning tasks including recommendation systems, classification, and regression has increased significantly. For this reason, this work suggests using the hybrid quantum-based Resnet18 transfer learning model to categorize and diagnose diseases that impact the leaves of maize plants. The dataset employed consists of four categories including Leaf Blight, Grey Leaf spots, Common Rust, and Healthy leaves. When compared to Resnet18 without quantum, this method outperforms it in terms of recall, accuracy, precision, and f1-score. The validation accuracy of 87% is achieved using the model. In the future, the proposed effort can serve as a practical tool to aid farmers in recognizing the diseases and safeguarding their maize crops. © 2024 IEEE.

关键词： Crops

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Recognition and Identification of COVID-19 from Chest X-Rays for Earlier Diagnosis 4

Recognition and Identification of COVID-19 from Chest X-Rays...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Kavitha, P. Sharma, Manish Pradeep Kumar, G.M. Barve, Amit Patil, Harshal Maranan, Ramya CMR Institute of Technology Department of Computer Science and Engineering Karnataka Bengaluru560037 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India BMS Institute of Technology and Management Department of AI & ML Karnataka Bangalore560064 India Parul Institute of Technology Parul University Faculty of Engineering and Technology Department of Computer Science and Engineering P.O. Limda Gujarat 391760 India Computer Science and Engineering Maharashtra Pune412115 India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350385793

Early detection is critical for controlling the spread and death of the COVID-19 worldwide pandemic, which affects global health care and lifestyle. The gold standard diagnosis, Reverse Transcription Polymerase Chain Reaction (RT-PCR) is both costly and has a lengthy process time. So more readily available and fast diagnostic methods are necessary. Many researchers proved that COVID-19 data can be effectively detected by Chest X-rays (CXR). However, diagnosing chest X-ray images for COVID-19 is difficult and error-prone. The only way to improve the COVID-19 diagnosis is to use DL algorithms. In this research, a novel deep learning model Convolutional Neural Network and Autoencoder (CNN-AE) tuned by Bat Optimization Algorithm (BOA) is proposed. The proposed approach would identify whether the CXR images were positive or negative for COVID. We received the CXR images from Kaggle. The pre-processing phases in the proposed technique assist with providing relevant information and reducing biased outputs. This is followed by the classification model. We were able to evaluate the performance of our proposed model by using Kaggle datasets. Our proposed model is evaluated using Kaggle datasets and achieved an average accuracy, True Positive Rate (TPR), True Negative Rate (TNR), precision, F1-score, False Positive Rate (FPR), and False Negative Rate (FNR) of 99.28%, 99.42%, 99.14%, 99.42%, 99.28%, 0.85%, and 0.57% respectively. The outcome of the proposed method is compared with the existing and similar research. A comparative study shows that our proposed method outperforms the state-of-art methods. © 2024 IEEE.

关键词： COVID-19

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Real-Time Spammers Detection Based on Metadata Features with Machine Learning

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Intelligent Automation & Soft Computing 2023年第12期38卷 241-258页

作者： Adnan Ali Jinlong Li Huanhuan Chen Uzair Aslam Bhatti Asad Khan School of Computer Science and Technology University of Science and Technology of ChinaHefei230026China School of Information and Communication Engineering Hainan UniversityHaikou570228China Metaverse Research Institute School of Computer Science and Cyber EngineeringGuangzhou UniversityGuangzhou510006China

Spammer detection is to identify and block malicious activities performing *** users should be identified and terminated from social media to keep the social media process organic and to maintain the integrity of online social *** research aimed to find spammers based on hybrid approaches of graph mining,posted content,and metadata,using small and manually labeled ***,such hybrid approaches are unscalable,not robust,particular dataset dependent,and require numerous parameters,complex graphs,and natural language processing(NLP)resources to make decisions,which makes spammer detection impractical for real-time *** example,graph mining requires neighbors’information,posted content-based approaches require multiple tweets from user profiles,then NLP resources to make decisions that are not applicable in a real-time *** fill the gap,firstly,we propose a REal-time Metadata based Spammer detection(REMS)model based on only metadata features to identify spammers,which takes the least number of parameters and provides adequate *** is a scalable and robust model that uses only 19 metadata features of Twitter users to induce 73.81%F1-Score classification accuracy using a balanced training dataset(50%spam and 50%genuine users).The 19 features are 8 original and 11 derived features from the original features of Twitter users,identified with extensive experiments and ***,we present the largest and most diverse dataset of published research,comprising 211 K spam users and 1 million genuine *** diversity of the dataset can be measured as it comprises users who posted 2.1 million Tweets on seven topics(100 hashtags)from 6 different geographical *** REMS’s superior classification performance with multiple machine and deep learning methods indicates that only metadata features have the potential to identify spammers rather than focusing on volatile posted content and complex graph ***

关键词： Spam detection online social networks metadata machine learning

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SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro Processors 8

SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro P...

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8th International Conference on computer and Communication Systems, ICCCS 2023

作者： Qian, Hong Wu, Wei Zhu, Qi Wang, Fei Zhao, Jinwei Zheng, Yan School of Computer Science and Technology University of Science and Technology of China Hefei China Jiangnan Institute of Computing Technology System Software Research Department Wuxi China Tsinghua University Department of Computer Science and Technology Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

ISBN: (纸本)9781665456128

The Sunway family supercomputers have achieved a series of remarkable achievements. However, the toolchains provided by them are not perfect, which has brought great challenges to the development of high-performance application software. In this paper, a profiling and optimizing tool is proposed to assist people to analyze and optimize the performance of their programs. SWPFOPLD is independent of the application program and gathers the runtime performance data through the PMUs, an automatically hot functions rearrange optimization based on the performance data is furtherly accomplished. The evaluation shows that SWPFOPLD can be easily and effectively used to analyze and optimize the performance of the application programs. © 2023 IEEE.

关键词： Supercomputers

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HUSS:A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

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Data Intelligence 2023年第3期5卷 537-559页

作者： Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S.Sheng Key Laboratory of Knowledge Engineering with Big Data(the Ministry of Education of China) Hefei University of TechnologyChinaSchool of Computer Science and Information EngineeringHefei University of TechnologyHefeiChina Research Institute of Artificial Intelligence Zhejiang LabHangzhouChina Department of Computer Science Texas Tech UniversityLubbockTX 79409USA

Spreadsheets contain a lot of valuable data and have many practical *** key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets,e.g.,identifying cell function types and discovering relationships between cell *** existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells.A few studies do,but they ignore the layout structure information of spreadsheets,which affects the performance of cell function classification and the discovery of different relationship types of cell *** this paper,we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets(HUSS).Specifically,for improving the cell function classification,we propose an error correction mechanism(ECM)based on an existing cell function classification model[11]and the layout features of *** improving the table structure analysis,we propose five types of heuristic rules to extract four different types of cell pairs,based on the cell style and spatial location *** experimental results on five real-world datasets demonstrate that HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

关键词： Spreadsheet semantic structure Information extraction Heuristics Cell function analysis Table structure analysis

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Modeling and Simulation of Unmanned Swarm System Based on Dynamic Structure DEVS 5

Modeling and Simulation of Unmanned Swarm System Based on Dy...

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2023 5th International Conference on Modeling, Simulation, Optimization and Algorithm, ICMSOA 2023

作者： Zhang, Wangyang Li, Qingwei Xu, Xin Li, Weiqing School of Computer Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China The 28th Research Institute of China Electronics Technology Group Corporation Jiangsu Nanjing China

As a complex system with collaborative cooperation and capability emergence, the unmanned swarm system plays an important role in military, agriculture, aerospace and other fields. With the increasing complexity of unmanned swarm systems, traditional modeling methods cannot effectively describe the dynamics of swarm systems. In order to effectively design, use and evaluate unmanned swarm systems, this article conducts modeling and simulation of unmanned swarm systems based on DSDEVS(Dynamic Structure Discrete Event System Specification). Describe the unmanned swarm system from both behavioral and structural aspects. Based on component modeling, the microscopic behaviors of the unmanned platforms that constitute the unmanned swarm are described, and coupling relationships are established to describe the collaborative structure between unmanned platforms. According to the state transfer and event interaction characteristics of the unmanned swarm system, a synchronization mechanism is added to support changes in the swarm structure. This enables the unmanned swarm system to make adaptive structural adjustments according to environmental changes and its own goals during simulation operation, so as to achieve a state of enhanced capabilities and structural stability to complete the task. Finally, the effectiveness and feasibility of this method were verified through a simulation experiment of joint reconnaissance by a UAV swarm. © Published under licence by IOP Publishing Ltd.

关键词： Specifications

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Convolutional Neural Network Based Approach for Automatic Detection of Diseases from Pomegranate Plants 8

Convolutional Neural Network Based Approach for Automatic De...

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8th IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2024

作者： Rana, Harsh Singh Manjunatha, N. Pokhare, Somnath S. Marathe, Rajiv A. Rajan, Jeny National Institute of Technology Karnataka Dept. of Computer Science and Engineering Surathkal India ICAR National Research Centre on Pomegranate Solapur India

ISBN: (纸本)9798350350593

Recent advancements in Artificial Intelligence, particularly Convolutional Neural Networks (CNNs), have significantly enhanced computer vision tasks, including plant disease detection. India is a global leader in pomegranate cultivation and production. Pomegranate is a vital horticultural commodity with significant export potential but is highly susceptible to various diseases, leading to substantial economic losses. Existing research on automatic pomegranate disease detection has limitations, as it typically analyzes either fruits or leaves in isolation and uses datasets with plain backgrounds that do not reflect real-world complexities such as lighting variations and overlapping foliage. This study proposes a novel hybrid deep learning approach that addresses these limitations. We utilized a hybrid CNN model specifically developed using the pomegranate dataset provided by the ICAR-National research Centre on Pomegranate. This comprehensive collection includes 1,632 high-resolution images captured from orchards across India, categorized into three classes: healthy, bacterial diseases, and fungal diseases. Our model achieves an impressive accuracy rate of 98.46%, demonstrating its potential for real-world application in pomegranate disease detection and improved agricultural outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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