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Automation and Computation (AUTOCOM), International Conference on

作者： Kapil Kumar Priyank Pandey Manju Khari School of Computer & Systems Sciences Jawaharlal Nehru University New Delhi India Department of Computer Science & Engineering Graphic era deemed to be university Dehradun Uttarakhand India

Smart antennas are now widely expended in transmission systems due to their vital plusses. To boost the performance of smart antenna operation, it is crucial to design an efficient beamforming pattern based on specific antenna parameters. Due to their numerous benefits, smart antennas are now often utilized in communication systems. It is essential to create a successful beamforming pattern based on certain antenna parameters with the objective of improvement in the performance of smart antenna operation. Beamforming strategies utilizing a combination of soft computing techniques were proposed in earlier works. These methods, however, did not take signal Direction of Arrival (DOA) precision into account. While determining the beamforming pattern for smart antennas, this research addresses the DOA issue. The researchers use a variety of techniques and conduct a comparison analysis to determine an exact DOA estimate. The authors proposed a method that shows an optimal result after resolving the existing problems for estimating the DOA with precision. In comparison to the traditional DOA pattern, this innovation produces a more accurate DOA pattern with reduced undesired side lobes. The researchers derive the antenna beamforming pattern in accordance with the necessary direction of angles using the predicted DOA pattern. The experimental results demonstrated that the proposed beamforming technology performs better than earlier techniques. In this work, advancement in beamforming precision can lead to better communication system performance and overall efficiency of smart antennas.

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Integration of SAP and Intelligent Robotic Process Automation

Integration of SAP and Intelligent Robotic Process Automatio...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Yendluri, Dileep Kumar Tatikonda, Reshmi Thatikonda, Ramya Ponnala, Jainath Kempanna, M. Bhuvanesh, A. University of the Cumberlands Department of Computer Science WilliamsburgMO United States University of the Central Missouri Department of Computer Information Systems WarrensburgMO United States University of the Cumberlands Department of Information Technology WilliamsburgMO United States Jawaharlal Technological University Department of ECE Hyderbad India Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India

ISBN: (纸本)9798350319088

This article explores how implementing IRPA in enterprises could have a revolutionary effect. Businesses looking to maximize efficiency can find a partnership between IRPA and SAP systems to be a wise way to achieve their goals. This study investigates how several company operations including payroll processing, human administration, financial and accounting processes, and logistics are impacted by integration. This talk explores the benefits, challenges, and best practices for integrating IRPA with SAP, offering specific instances from real-world projects that highlight the effectiveness of this relationship. The future scope of the IRPA-SAP integration looks at enhanced analytics, hyper-automation, cutting-edge cognitive features, broader industry applications, and the growth of human-robot cooperation. A thorough analysis highlights tactics that businesses may use to use SAP's and IRPA's combined power to accelerate digital transformation and achieve peak performance. © 2023 IEEE.

关键词： Integration

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Retraction Note: Big data analytics for dynamic network slicing in 5G and beyond with dynamic user preferences

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Optical and Quantum Electronics 2024年第11期56卷 1-2页

作者： Syamala, Maganti Anusuya, R. Sonkar, Sanjay Kumar Goswami, Chandrashekhar salunkhe, Satish Elangovan, Muniyandy Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computer Science and Engineering Modern Institute of Technology and Research Center Alwar India Department of Computer Science & Information Systems Shri Ramswaroop Memorial University Barabanki India Department of CSE School of Computing MIT ADT University Pune India Computer Engineering Department Terna Engineering College Navi Mumbai Affiliated to University of Mumbai Navi Mumbai India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of R&D Bond Marine Consultancy London UK

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Agricultural Water Conservation through AI-Enhanced Solar IoT Networks

Agricultural Water Conservation through AI-Enhanced Solar Io...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： S. P. Maniraj Monisha J R. Amutha R. Kavitha P. J. Shriidhar C. Srinivasan Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Tamilnadu India Department of Computer Science and Engineering (Cyber Security) R.M.K. College of Engineering and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering ULTRA College of Engineering and Technology Madurai Tamil Nadu India Department of Computer Science and Engineering Chennai Institute of Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Vinayaka Missions Kirupananda Variyar Engineering College Vinayaka Missions Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha Universityi Chennai Tamil Nadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

With the increasing water shortage and climatic uncertainty, creative strategies for effective water management in agriculture are necessary. This research investigates the integration of Artificial Intelligence (AI), particularly Artificial Neural Networks (ANN), with solar-powered Internet of Things (IoT) networks to enhance water utilization in agricultural activities. The proposed system facilitates accurate irrigation scheduling using real-time data from soil moisture sensors, meteorological stations, and crop health monitoring devices. The ANN model analyses this data to predict ideal irrigation schedules and volumes, minimizing water waste while improving crop yields. The solar-powered IoT framework provides sustainability and reduces dependence on traditional energy sources. Field trials indicate that the AI-enhanced system may promote water saving by as much as 30% relative to traditional irrigation techniques. Moreover, using AI enables adaptive learning, enabling the system to enhance its irrigation tactics using historical and real-time data. This novel method conserves water resources and fosters sustainable agriculture practices, coinciding with global initiatives to address water shortages and assure food security. The findings underscore the potential of technology-based solutions to revolutionize agricultural water management for a sustainable future.

关键词： Irrigation Accuracy Food security Artificial neural networks Water conservation Real-time systems Internet of Things Sustainable development Water resources Intelligent sensors

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Jigsaw-Based Benchmarking for Learning Robotic Manipulation 8

Jigsaw-Based Benchmarking for Learning Robotic Manipulation

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Liu, Xiaobo Wan, Fang Ge, Sheng Wang, Haokun Sun, Haoran Song, Chaoyang Southern University of Science and Technology Department of Mechanical and Energy Engineering Guangdong Shenzhen518055 China School of Design Southern University of Science and Technology Guangdong Shenzhen518055 China Hong Kong University of Science and Technology Robotics & Autonomous Systems Thrust System Hub Hong Kong Hong Kong The University of Hong Kong Department of Computer Science Hong Kong Hong Kong

ISBN: (纸本)9798350300178

Benchmarking provides experimental evidence of the scientific baseline to enhance the progression of fundamental research, which is also applicable to robotics. In this paper, we propose a method to benchmark metrics of robotic manipulation, which addresses the spatial-temporal reasoning skills for robot learning with the jigsaw game. In particular, our approach exploits a simple set of jigsaw pieces by designing a structured protocol, which can be highly customizable according to a wide range of task specifications. Researchers can selectively adopt the proposed protocol to benchmark their research outputs, on a comparable scale in the functional, task, and system-level of details. The purpose is to provide a potential look-up table for learning-based robot manipulation, commonly available in other engineering disciplines, to facilitate the adoption of robotics through calculated, empirical, and systematic experimental evidence. © 2023 IEEE.

关键词： Benchmarking

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Text Analysis on Early Reactions to ChatGPT as a Tool for Academic Progress or Exploitation

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SN computer science 2024年第4期5卷 366页

作者： Bukar, Umar Ali Sayeed, Md Shohel Razak, Siti Fatimah Abdul Yogarayan, Sumendra Amodu, Oluwatosin Ahmed Raja Mahmood, Raja Azlina Centre for Intelligent Cloud Computing (CICC) Faculty of Information Science & amp Technology Multimedia University Jalan Ayer Keroh Lama Bukit Beruang Melaka 75450 Malaysia Department of Electrical Electronics & amp Systems Engineering Universiti Kebangsaan Malaysia (UKM) Selangor Bangi 43600 Malaysia Information and Communication Engineering Department Elizade University Ondo State Ilara-Mokin Nigeria Department of Communication Technology and Network Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor Darul Ehsan Serdang 43400 Malaysia

Large language models (LLMs) have garnered significant attention lately, and one particular implementation that has captivated users is ChatGPT, a first-of-its kind innovation that sparks intense debates among professionals regarding its ethical boundaries, especially after it was released for public use. Some of the debates are ongoing, with several people getting more used to this tool and some losing touch with several ethical concerns. Thus, it is crucial to explore the early professional’s perspectives surrounding ChatGPT to comprehend its potential impact on the educational sector and, in particular, society in general. This research aims to address this knowledge gap by employing data mining techniques on LinkedIn to analyze ChatGPT-related discussions and provide a comprehensive textual examination. The dataset for this investigation consists of 166 records written in English by LinkedIn users discussing ChatGPT in its early stage. The study utilized VOSviewer software to clean, analyze, mine, and visualize the data. Among the sample, it was observed that 49% of the users are PhD holders, predominantly with experience in scientific research and data science fields. Hence, the findings reveal that issues such as plagiarism, references, citations, manuscripts, papers, and literature reviews stand out as the major concerns. These results suggest that while ChatGPT can contribute to academic progress by fostering new knowledge, thoughts, and ideas, it can also facilitate academic misconduct, including plagiarism and inaccurate information dissemination. Therefore, this paper also provides discussions on the implications and ethical issues of ChatGPT. In particular, it is imperative to establish clear ethical boundaries and guidelines for the appropriate use of LLMs in academic settings. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Academic ChatGPT Ethics LinkedIn LLMs Text analysis Visualization VOSviewer

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Agile Field Test Support for Drone Rescue Missions

Agile Field Test Support for Drone Rescue Missions

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IEEE Conference on Commerce and Enterprise Computing, CEC

作者： Stefan Biffl Kristof Meixner Michael Vierhauser CDL-SQI Institute of Information Systems Engineering TU Wien CDP Vienna Austria Department of Computer Science University of Innsbruck Austria

ISBN: (数字)9798331529093

ISBN: (纸本)9798331529109

Performing field testing of drone missions requires the structured definition of field tests that can be adapted to changing environmental conditions. However, traditional field test checklists on paper require the tester to manually adapt tasks to emerging and changing test conditions. Further, the scattered, heterogeneous documentation on (electronic) paper is hard to integrate and validate - once field tests are completed - for test data analysis. Field test managers, testers, and analysts require an agile field test information system that provides capabilities for (i) providing efficient guidance to field testers on tasks adapted to the field test mission state; and (ii) creating structured field test documentation for advanced analysis of results and causes of risky effects. This paper introduces the Agile Field Test (AFT) approach for behavior-driven test specification and derivation of role-specific information systems for agile guidance on and documentation of field test tasks. We evaluated the effectiveness of the AFT approach with a real-world multi-drone water rescue mission. The study results indicate AFT to be effective for representing and validating field test task specifications and the concepts required for field test documentation as a foundation for test result analysis.

关键词： Data analysis Documentation Informatics Drones Testing Business

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Foreword

Foreword

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作者： Abawajy, Jemal Xu, Zheng Atiquzzaman, Mohammed Zhang, Xiaolu Faculty of Science Engineering and Built Env ironment Deakin University GeelongVIC Australia School of Computer Engineering and Sciences Shanghai Polytechnic University Shanghai China School of Computer Science University of Oklahoma NormanOK United States Department of Information Systems and Cyber Security The University of Texas at San Antonio San AntonioTX United States

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Model Predictive control for Autonomous Driving: Comparing Kinematic and Dynamic Models of Tractor-Trailer systems

Model Predictive Control for Autonomous Driving: Comparing K...

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International Conference on Intelligent Transportation

作者： Gustav Vallinder Jonas Mårtensson Pedro F. Lima Sriharsha Bhat Scania CV AB Södertälje Sweden Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Investigations of kinematic and dynamic models of tractor-trailer systems have historically been performed for stability analysis or state estimation. In this work, we present and evaluate kinematic and dynamic tractor-trailer models for model predictive control (MPC). We show in open-loop simulations that a kinematic and a dynamic model are equivalent at low speeds and short discretization time steps. A zero speed singularity and stiff dynamics prevents the usage of the dynamic model in control design, where discretization time steps are longer. A method of discretization is proposed to resolve the low speed feasibility of the dynamic model. In closed-loop simulations, the real-time applicability of the kinematic and dynamic models in a nonlinear MPC is verified.

关键词： control design Optimal control Kinematics Predictive models Real-time systems Stability analysis State estimation Intelligent transportation systems Autonomous vehicles Predictive control

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Redefining Trust: Assessing Reliability of Machine Learning Algorithms in Intrusion Detection systems

Redefining Trust: Assessing Reliability of Machine Learning ...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Hossein Sayadi Zhangying He Tahereh Miari Mehrdad Aliasgari Department of Computer Engineering and Computer Science California State University Long Beach CA USA Center for Information Systems and Technology Claremont Graduate University CA USA

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

The performance limitations of conventional software-based Intrusion Detection systems (IDSs) have paved the way for the emergence of hardware-oriented approaches. These approaches harness the power of Machine Learning (ML) algorithms applied to processors’ hardware-related data, thereby enhancing the overall system’s security and efficiency. However, ensuring the dependability of ML models’ decisions is crucial, yet this aspect has been largely overlooked in previous studies. In this paper, we delve into the reliability of machine learning algorithms within hardware-oriented intrusion detection systems, focusing specifically on malware detection. Our investigation aims to bridge the existing gap by shedding light on the tradeoffs between performance vs. reliability and robustness levels exhibited by ML models in intrusion detection systems. We conduct a thorough evaluation of ML algorithms in hardware-oriented IDSs, considering factors such as training data size, number of hardware events used, and internal data separability (malware stealthiness). Additionally, we incorporate an effective model observer module to assess prediction probabilities in real-time; thereby, employing a threshold to determine the ML model’s confidence for enhanced reliable intrusion detection.

关键词： Machine learning algorithms Intrusion detection Training data Predictive models Malware Robustness Real-time systems

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