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Robust asymmetric Cherenkov radiation in tilted anisotropic medium

science China(Physics,Mechanics & Astronomy) 2024年第1期67卷 46-51页

作者： Shuwen Xue Yadong Xu Linkang Han Zhi Ning Chen Qiaoliang Bao Shan Zhu Huanyang Chen Institute of Electromagnetics and Acoustics and Department of Physics College of Physical Science and TechnologyJiujiang Research InstituteXiamen UniversityXiamen 361005China School of Physical Science and Technology Institute of Theoretical and Applied PhysicsSoochow UniversitySuzhou 215006China Department of Electrical and Computer Engineering National University of SingaporeSingapore 8117583Singapore Institute of Energy Materials Science(IEMS) University of Shanghai for Science and TechnologyShanghai 200093China

Cherenkov radiation(CR)is available for a wide variety of terahertz(THz)radiation sources,but its efficiency is deeply affected by intrinsic *** find that if the tilted angle(α)of anisotropic material and radiation angle(θ)meet the condition ofθ+α=π/2,the intensity of radiation fields for the charged particle bunch(CPB)moving from left to right cannot be influenced by intrinsic losses,which means long-distance radiation can be ***,we observe an asymmetric CR when the CPB moves from the opposite *** addition,we select natural van der Waals(vd W)materialα-MoO3as an example,further confirming that the radiation field can reach the far field and the asymmetric CR radiation can also be *** wonderful properties with long-distance radiation will extend the application of CR to a certain extent for future design and fabrication.

关键词： asymmetric Cherenkov radiation intrinsic losses long-distance radiation

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Plant Disease Detection using Image Processing with Machine Learning 4

Plant Disease Detection using Image Processing with Machine ...

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4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Kiran, Ajmeera Lokesh Naik, S.K. Raj, M Silpa Palvadi, Srinivas Kumar Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India Mlr Institute of Technology Dundigal Department of Computer Science and Engineering Hyderabad India India Koneru Lakshmaiah Education Foundation Department of Computer Science & Information Technology India

ISBN: (纸本)9798350300093

Crop diseases pose a severe challenge to food security, yet in many parts of the world it is still challenging to promptly diagnose them owing to a lack of necessary infrastructure. Image processing may be the most effective method to anticipate this issue and provide findings that are remarkably precise. Support vector machines and deep CNN were used in this research by improving training effectiveness and accuracy. In this study, Plant Disease Dataset is used which involves images of healthy and diseased plants collected by under some controlled conditions, SVM (support vector machine which used for feature extraction of images) and CNN (it will classify the dataset images into diseased plant or healthy plant) and further it'll be recommending the fertilizers according to the plant diseases. © 2023 IEEE.

关键词： Support vector machines

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A Novel Key Generation Algorithm Utilizing Lattice-Based Cryptography Principles 3rd

A Novel Key Generation Algorithm Utilizing Lattice-Based Cry...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Arul, Rajakumar Sivaselvam, B. Choudhary, Ankush Centre for Smart Grid Technologies Vellore Institute of Technology Chennai India Department of Computer Science and Engineering IIITDM Kancheepuram Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819726707

technology advancement is inevitable, and it has created a revolution in all the sectors creating huge opportunities. Artificial intelligence and machine learning have boosted the advancement of all the sectors making the life easier and comfortable. On the other hand, these advancements highly depend on the connectivity through which all the terminals are communicating. Thus, all the technology developments depend on the communication paradigm, particularly on the wireless medium. Securing the information and infrastructure is crucial, and it demands highly complex problems that cannot be breached with high computation machines like quantum computer. The emergence of quantum computer has made all the existing cryptographic algorithms vulnerable creating a space for advanced algorithms that are safe from quantum computer attacks. Key generation is the foundational process of any cryptographic algorithm, and in this research, we propose a novel key generation mechanism based on lattice using mathematical functions. The proposal is evaluated using theoretical assumptions, and it is found to be safe and secure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Encryption algorithms

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Robust multi-domain descriptive text classification leveraging conventional and hybrid deep learning models

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International Journal of Information technology (Singapore) 2024年第5期16卷 3219-3231页

作者： Bhowmik, Shovan Sultana, Sharmin Sajid, Ahmed Arian Reno, Saha Manjrekar, Aakash Department of Computer Science and Engineering Bangladesh Army International University of Science and Technology Syedpur Bangladesh Department of Computer and Information Sciences University of Delaware Newark United States Department of Computer Science University of Massachusetts Lowell Lowell United States Vidyalankar Institute of Technology University of Mumbai Mumbai India

Since an unprecedented amount of online information in the form of unstructured texts is generated daily, researchers have started to focus on the development of robust mechanisms with the help of Natural Language Processing (NLP) and Artificial Intelligence (AI) to predict sentiments. Reliable sentiment classification models can not only avoid the spreading of misinformation but also help people to sieve genuine data by creating fact-checking tools on the user end. The main focus of this work is to investigate the performance of six conventional Deep Learning (DL) models followed by one hybrid DL model in three domains (fake news identification, spam filtering, and cyberbullying detection) of opinion mining tasks by employing efficient deep architectures capable of predicting sentiments even if long sentences are provided. Our empirical results show the rigid performance of all models on a corpus with maximum accuracy of 99.55%, 97.57%, and 93.00% for fake news, spam email, and cyberbullying domains accomplished by Convolutional Neural Network (CNN), Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) hybrid, and CNN models accordingly. Our work also exhibits a comparison of compilation times for every model to speed up the training time in this state-of-the-art. Contrary to the previous studies, we have taken long descriptive paragraphs for training purposes with a maximum length of 3336 words in a single sentence. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Artificial Neural Network Convolutional Neural Network Cyberbullying Fake News Sentiment Analysis

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Explainable Artificial Intelligence(XAI)Model for Cancer Image Classification

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computer Modeling in engineering & sciences 2024年第10期141卷 401-441页

作者： Amit Singhal Krishna Kant Agrawal Angeles Quezada Adrian Rodriguez Aguiñaga Samantha Jiménez Satya Prakash Yadav Department of Computer Science&Engineering Raj Kumar Goel Institute of TechnologyGhaziabad201017India Department of School of Computing Science and Engineering Galgotias UniversityGreater Noida203201India Tecnológico Nacional de México Campus Tijuana Baja California22414México Universidad Autónoma de Baja California TijuanaBaja California22414México Department of Computer Science and Engineering G.LBajaj Institute of Technology and Management(GLBITM)Affiliated to Dr.A.P.J.Abdul Kalam Technical UniversityLucknow201306India School of Computer Science Engineering and Technology(SCSET) Bennett UniversityGreater Noida201310India

The use of Explainable Artificial Intelligence(XAI)models becomes increasingly important for making decisions in smart healthcare *** is to make sure that decisions are based on trustworthy algorithms and that healthcare workers understand the decisions made by these *** models can potentially enhance interpretability and explainability in decision-making processes that rely on artificial ***,the intricate nature of the healthcare field necessitates the utilization of sophisticated models to classify cancer *** research presents an advanced investigation of XAI models to classify cancer *** describes the different levels of explainability and interpretability associated with XAI models and the challenges faced in deploying them in healthcare *** addition,this study proposes a novel framework for cancer image classification that incorporates XAI models with deep learning and advanced medical imaging *** proposed model integrates several techniques,including end-to-end explainable evaluation,rule-based explanation,and useradaptive *** proposed XAI reaches 97.72%accuracy,90.72%precision,93.72%recall,96.72%F1-score,9.55%FDR,9.66%FOR,and 91.18%*** will discuss the potential applications of the proposed XAI models in the smart healthcare *** will help ensure trust and accountability in AI-based decisions,which is essential for achieving a safe and reliable smart healthcare environment.

关键词： Explainable artificial intelligence artificial intelligence XAI healthcare cancer image classification

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Advanced Schedulability Testing for Adaptive Mixed-Criticality Systems under Weakly Hard Real-Time Constraints 5

Advanced Schedulability Testing for Adaptive Mixed-Criticali...

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5th CPSSI International Symposium on Cyber-Physical Systems (Applications and Theory), CPSAT 2024

作者： Rabbiun, Hossein Mohammadi, Jamal Shirazi, Mahmoud Kargahi, Mehdi Department of Computer Science and Information Technology Zanjan Iran University of Tehran School of Electrical and Computer Engineering College of Engineering Tehran Iran

ISBN: (纸本)9798331529284

This paper advances the schedulability analysis of the Adaptive Mixed-Criticality for Weakly Hard Real-Time Systems (AMC-WH) which allows a specified number of consecutive low-criticality (LO) jobs of tasks to be skipped when the system operates in the high-criticality (HI) mode. The current AMC-WH schedulability test, based on the skip-over model, in which low tasks are guaranteed to meet m-s out of m consecutive deadlines, where s is the number of allowed skips and m is the cycle length, only calculates the worst-case response times (WCRT) for low tasks under a fixed execution pattern post-mode switch. To address this limitation, we propose a new sufficient schedulability test for AMC-WH using the (m,k)-firm model, which allows various execution patterns for low tasks after a mode switch, enhancing the flexibility and applicability. Additionally, we develop an exact schedulability test for AMC-WH based on response time analysis (RTA). This test modifies the existing exact tests for the AMC algorithm, incorporating updates to state-space exploration and pruning rules to account for low task execution after a mode switch. Simulation results validate the effectiveness of our tests, demonstrating enhanced schedulability analysis and optimized resource allocation. © 2024 IEEE.

关键词： Space research

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Multi-Scale HSV Color Feature Embedding for High-Fidelity NIR-to-RGB Spectrum Translation

Multi-Scale HSV Color Feature Embedding for High-Fidelity NI...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Zhai, Huiyu Chen, Mo Yang, Xingxing Kang, Guosheng School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hong Kong Baptist University Department of Computer Science Hong Kong

ISBN: (纸本)9781665410205

This study investigates the actual impact of ChatGPT on human resource management (HRM) occupations and compares it with assessments from large language models (LLMs). Using data from the Occupational Information Network (O*NET), we developed a novel methodology combining real-world evidence from Google search results with evaluations from GPT-4, GPT-3.5, and Claude 3 Opus. Our findings reveal significant variations in the degree of exposure among HRM occupations. Training and Development Managers were the most exposed, with 86.52% of their tasks potentially automated by ChatGPT, while Labor Relations Specialists were the least exposed at 11.08%. Notably, LLM assessments consistently overestimated the exposure levels by an average of 30% compared to real-world evidence. This discrepancy highlights the importance of critically evaluating LLM capabilities in task automation. Our research contributes to understanding AI's impact on HRM practices and provides valuable insights for professionals adapting to the AI era. It also underscores the need for caution when relying solely on LLM assessments for workforce planning and development. © 2024 IEEE.

关键词： Human resource management

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Attendance Automation System with Facial Authorization and Body Temperature Using Cloud Based Viola-Jones Face Recognition Algorithm 22nd

Attendance Automation System with Facial Authorization and B...

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22nd International Conference on Hybrid Intelligent Systems, HIS 2022 and the 18th International Conference on Information Assurance and Security, IAS 2022

作者： Devi Priya, R. Kirupa, P. Manoj Kumar, S. Mouthami, K. Department of Computer Science and Engineering Centre for IoT and Artificial Intelligence KPR Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9783031274084

Face recognition works all over the world from various perspectives. In the field of attendance systems, many methodologies have faced various drawbacks. In the headset of attendance monitoring, the attendance is registered immediately by scanning the face and compares with pattern in the database and marking the student attendance along with the detection of their body temperature automatically by using object vision algorithms. In the proposed system, facial feature recognition and detection is performed based on the Viola-Jones face detection algorithm. The system is for school or university students with any large strength, and even a single student with at least 5–6 images with different angle of their face to be stored. Hence, these automation systems need a large amount of storage space. Hence, cloud storage server is also used to store any number of images. The student's attendance is recorded by the camera installed in the class entrance. Students have to record their faces one by one before entering the classes and the camera can create snapshots for a particular set of defined timings. Then, it detects faces in snapshot images, compares them with the cloud database and then attendance is marked. The experimental results show that the proposed Viola-Jones face detection algorithm is better than many existing algorithms. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Face recognition

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Towards optimized tensor code generation for deep learning on sunway many-core processor

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Frontiers of computer science 2024年第2期18卷 1-15页

作者： Mingzhen LI Changxi LIU Jianjin LIAO Xuegui ZHENG Hailong YANG Rujun SUN Jun XU Lin GAN Guangwen YANG Zhongzhi LUAN Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China National University of Singapore Singapore 119077Singapore State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214000China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing 100854China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application *** the existing deep learning compilers,TVM is well known for its efficiency in code generation and optimization across diverse hardware *** the meanwhile,the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning *** paper combines the trends in these two ***,we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as *** addition,we leverage the architecture features during the compilation such as core group for massive parallelism,DMA for high bandwidth memory transfer and local device memory for data locality,in order to generate efficient codes for deep learning workloads on *** experiment results show that the codes generated by swTVM achieve 1.79x improvement of inference latency on average compared to the state-of-the-art deep learning framework on Sunway,across eight representative *** work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in *** believe this work will encourage more people to embrace the power of deep learning and Sunwaymany-coreprocessor.

关键词： sunway processor deep learning compiler code generation performance optimization

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Enhancing Student Support with AI: A College Assistance Chatbot Using NLP and ANN 2

Enhancing Student Support with AI: A College Assistance Chat...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Abdul Razak, M.S. Manoj Kumar, M.V. Nirmala, C.R. Naseer, R. Prashanth, B.S. Sneha, H.R. Bapuji Institute of Engineering and Technology Department of Computer Science and Engineering Karnataka Davanagere577004 India Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Karnataka Bengaluru560064 India

ISBN: (纸本)9798350372892

The rapid adoption of chatbots in various domains has revolutionized user support systems, particularly in educational institutions. This paper presents the development and implementation of a College Assistance Chatbot designed to aid university students in navigating academic and administrative queries. Leveraging Natural Language Processing (NLP) techniques and deep learning models, specifically an Artificial Neural Network (ANN), the chatbot efficiently understands and processes student inquiries. The system's architecture is meticulously crafted to manage communication effectively, ensuring timely and accurate responses. An extensive dataset was collected from college resources and pre-processed to train the neural network. The chatbot's performance was rigorously evaluated through a series of experimental campaigns, achieving an accuracy of 80.07% after 1000 epochs. The results indicate that the chatbot can reliably handle a wide range of student queries, providing a scalable solution for enhancing student support services. This innovative approach offers significant improvements over traditional methods, demonstrating high potential for broader application in educational settings. © 2024 IEEE.

关键词： Students

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