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Cybernetics, Cognition and Machine Learning Applications (ICCCMLA), IEEE International Conference on

作者： Kavita Patil Rohit Patil Vedanti Koyande Amaya Singh Thakur Kshitij Kadam Computer Science and Business System JSPM’s Rajarshi Shahu College of Engineering Pune India

ISBN: (数字)9798331505790

ISBN: (纸本)9798331505806

This survey paper examines the advancements and challenges in chatbot technology, focusing on deep neural networks (DNNs) and their application in natural language processing (NLP). It discusses various chatbot models, including Elizabot, Alicebot, Mitsuku, and Cleverbot, highlighting their evolution from rule-based systems to sophisticated AI conversational agents. The study introduces a specialized chatbot for website integration, emphasizing the importance of swift, accurate, and personalized interactions to enhance customer engagement. Additionally, the paper explores the integration of Large Language Models (LLMs) such as Gemini, GPT and BERT, fine-tuning with deep learning techniques to improve chatbot performance, and their potential to revolutionize customer interactions and business growth.

关键词： Surveys Deep learning Analytical models Large language models Focusing Artificial neural networks Chatbots Cognition Cybernetics Business

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Deep Learning and Machine Learning Algorithms to Predict Lung Cancer

Deep Learning and Machine Learning Algorithms to Predict Lun...

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Emerging Trends in Information Technology and engineering (ic-ETITE), Conference on

作者： K. Gowthami Kamalakannan. J School of Computer Science Engineering and Information System. Vellore Institute Of Technology Vellore India

Lung cancer is the most deadly illness for patients, and it is an incurable condition. Patient survival rates are greatly increased by early cancer prognosis and identification. The Computed Tomography (CT) scan is an important imaging tool for a lung cancer diagnosis. On the other hand, a manual CT scan inspection is laborious and prone to mistakes. Lung cancer can be impulsively recognized by utilizing an image processing technique of computer-Aided Diagnosis (CAD). In recent years, various researchers identified various ways for accurate prediction by Using techniques for deep learning (DL) and machine learning (ML). This research represents a number of methodologies like Lung cancer disease prediction analysis makes use of Artificial Neural Network (ANN), Logistic Regression (LR), Support Vector Machine (SVM), K-means clustering, Convolutional Neural Network (CNN) Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM).

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Significance of Internet-of-Things Edge and Fog Computing in Education Sector

Significance of Internet-of-Things Edge and Fog Computing in...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Siddiqui, Shams Tabrez Khan, Mohammad Rafeek Khan, Zeba Rana, Nadim Khan, Haneef Alam, Md Imran College of Cs It Jazan University Department of Computer Science Jazan Saudi Arabia College of Cs It Jazan University Department of Computer and Network Engineering Jazan Saudi Arabia Computer and Information System Applied College Jazan University Jazan Saudi Arabia College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia

ISBN: (纸本)9798350347050

IoT gadgets include smart lights, ovens, and industrial data-collecting equipment. IDC expects 41.6 billion connected IoT devices to generate 79.4 ZB of data by 2025. Early IoT devices sent data to the cloud for analysis. When sending trillions of gigabytes to the cloud, the data pipeline clogs. Edge computing enables IoT devices to handle data locally instead of sending it to the cloud. Data is processed at the 'edge' of your local network, rather than transferred away. Technology's versatility makes it valuable in education. IoT edge computing has different requirements than non-IoT edge computing. IoT devices have limited data processing and storage capabilities, thus processing must occur off the device. The edge offers an environment to manage huge numbers of IoT devices and data. Many functions can be off-loaded to the edge, reducing device cost. In education, IoT edge computing architecture has gained importance due to real-time applications. In order to facilitate the analysis of educational system architecture, this paper seeks to provide an IoT Edge computing framework in the field of education. The framework will identify IoT-related issues and concerns. This study discusses IoT Edge computing in education, identifies limits, and makes recommendations. As well as the paper evaluates IoT Edge computing's performance in educational systems. © 2023 IEEE.

关键词： Edge computing

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A Noise Shaping SAR ADC with kT/C Noise Cancellation and Mismatch Error Shaping

A Noise Shaping SAR ADC with kT/C Noise Cancellation and Mis...

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IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA)

作者： Tiange Yi Zhenbing Li Bing Du Shiheng Yang Jiaxin Liu University of Electronic Science and Technology of China National Computer System Engineering Research Institute of China

ISBN: (数字)9798331530723

ISBN: (纸本)9798331530730

The resolution of SAR ADCs is primarily limited by sampling noise, DAC mismatch, and comparator noise. This work presents a noise-shaping SAR ADC design that addresses all these error sources. Sampling noise is mitigated using a kT/C noise cancellation technique combined with resampling. DAC mismatch is managed through data-weighted averaging (DWA) and mismatch error shaping (MES), while comparator noise is reduced using a second-order passive noise-shaping (NS) technique. Designed in a 28nm CMOS process, the ADC achieves 86.8 dB SNDR across a 2.8 MHz bandwidth with a power consumption of 3.8 mW.

关键词： Integrated circuit technology Power demand Bandwidth Noise cancellation CMOS process Noise shaping

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A Wideband Flat-Gain Dense Dielectric Patch Antenna with Filtering Response

A Wideband Flat-Gain Dense Dielectric Patch Antenna with Fil...

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International Conference on Microwave and Millimeter Wave Technology Proceedings

作者： Jia-Rui Lu Jing-Kang Xu Jun Xu Dong-Sheng La School of Computer and Communication Engineering Northeastern University at Qinhuangdao China Beijing Institute of Spacecraft System Engineering Beijing China

ISBN: (数字)9798350389968

ISBN: (纸本)9798350389975

In this paper, a wideband flat-gain Dense Dielectric Patch Antenna (DDPA) with filtering response is proposed. It consists of a stepped feedline, a double-layer printed circuit board (PCB), a cross-shaped ground, and a dense dielectric patch (DDP). The DDP is loaded with cylindrical vias, notches and metallic patches. The cross-shaped ground is used to restrain the unnecessary $T{M_{22}}$ mode. The $T{M_{10}}$ and $T{M_{12}}$ are effectively excited and fused by the notches and metal patches to form a passband with relative bandwidth of $22.57{{\% }}$ . The study also analyzes the impact of cylindrical via radius on bandwidth. Ultimately, the radiation pattern was revealed, the discrepancy between the co-polarization and cross-polarization surpasses 30dBi.

关键词： Microwave antennas Microwave integrated circuits Filtering Patch antennas Printed circuits Millimeter wave technology Dielectrics

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A Holistic State Estimation Framework for Active Distribution Network with Battery Energy Storage system

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Journal of Modern Power systems and Clean Energy 2022年第3期10卷 627-636页

作者： Shaojian Song Huangjiao Wei Yuzhang Lin Cheng Wang Antonio Gómez-Expósito IEEE School of Electrical Engineering Guangxi UniversityNanning 530004China Department of Electrical and Computer Engineering University of MassachusettsLowellMA 01852USA State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China Laboratory of Engineering for Energy and Environmental Sustainability University of SevilleSpain

Battery energy storage systems(BESSs)are expected to play a crucial role in the operation and control of active distribution networks(ADNs).In this paper,a holistic state estimation framework is developed for ADNs with BESSs integrated.A dynamic equivalent model of BESS is developed,and the state transition and measurement equations are *** on the equivalence between the correction stage of the iterated extended Kalman filter(IEKF)and the weighted least squares(WLS)regression,a holistic state estimation framework is proposed to capture the static state variables of ADNs and the dynamic state variables of BESSs,especially the state of charge(SOC).A bad data processing method is also *** simulation results show that the proposed holistic state estimation framework improves the accuracy of state estimation as well as the capability of bad data detection for both ADNs and BESSs,providing comprehensive situational awareness for the whole system.

关键词： Active distribution network(ADN) anomaly detection battery energy storage system(BESS) Kalman filtering situational awareness state estimation state of charge(SOC)

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Improved Demonstration-Knowledge Utilization in Reinforcement Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第5期5卷 2139-2150页

作者： Liu, Yanyu Zeng, Yifeng Ma, Biyang Pan, Yinghui Gao, Huifan Zhang, Yuting Xiamen University Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Department of Automation Xiamen361102 China Northumbria University Department of Computer and Information Sciences NewcastleNE1 8ST United Kingdom Minnan Normal University Department of Computer Science Zhangzhou363000 China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Reinforcement learning (RL) has made great success in recent years. Generally, the learning process requires a huge amount of interaction with the environment before an agent can achieve acceptable performance. This motivates many techniques, such as incorporating prior knowledge which is usually presented as experts' demonstration, and using a probability distribution to represent state-and-action values, to accelerate the learning process. The methods perform well when the prior knowledge is genuinely correct and no much change occurs to the learning environment. However, the requirement is not perfectly realistic in many complex applications. The demonstration knowledge may not reflect the true environment and even be full of noise. In this article, we introduce a dynamic distribution merging method to improve knowledge utilization in a general RL algorithm, namely Q-learning. The new method adapts a normal distribution to represent state-action values and merges the prior and learned knowledge in a discriminative way. We theoretically analyze the new learning method and demonstrate its empirical performance over multiple problem domains. © 2020 IEEE.

关键词： Merging

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Machine Learning-based Evaluation of Heart Rate Variability Response in Children with Autism Spectrum Disorder 3

Machine Learning-based Evaluation of Heart Rate Variability ...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Mohammed, Vazeer Ali Mohammed, Mehmood Ali Mohammed, Murtuza Ali Logeshwaran, J. Jiwani, Nasmin Lewis University Department of Computer and Information System IL United States University of the Cumberlands Department of Information Technology WilliamsburgKY United States Sri Eshwar College of Engineering Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9781665462167

At present, various electronic devices are used to monitor human heart rates. However, its functions are to avoid predicting the problems caused by heart rate variability in advance and analyzing its implications. It makes it difficult to diagnose problems caused by heart rate variability. A human should have an average heart rate of 72. At the same time, the newborn's heart should beat between 120 and 160 beats per minute. A baby born with autism spectrum disorder may have a lower-than-average heart rate. Complete blockage of the heart at birth is rare. Abnormal heart rate leads to heart block. So, there is a high chance of the child's death due to permanent heart blockage at any time. Most heart diseases in children with Autism Spectrum Disorder (ASD) are present at birth. A significant congenital disability is a hole in the heart. Many people do not realize that having holes in the heart is a common occurrence. Before the baby is born, tiny holes form in the muscular wall that divides the heart into the right and left halves. This paper proposed Machine Learning-Based Evaluation to identify the Heart Rate Variability Response in Children with Autism Spectrum Disorder with Autism Spectrum Disorder. The reasons for this are yet to be identified. However, 70 per cent of perforations resolve spontaneously before or after birth. Exceptionally, Children with Autism Spectrum Disorder with perforations that do not close properly may require surgery or a perforator brace, depending on the location and size of the perforation. © 2023 IEEE.

关键词： Heart

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Sagittarius: Floating Self Navigating Robot for Monitor the Water Quality of Rural Reservoirs

Sagittarius: Floating Self Navigating Robot for Monitor the ...

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Advancements in Computing (ICAC), International Conference on

作者： Nishan Ilandara Pedige Thaveesha Abeywarna Randheera Amarasinge Menuka Nawarathna Bandara Anuradha Javakody Narmada Gamage Department of Computer System Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

This paper proposes an optimal solution for monitoring water quality in Sri Lanka, where reservoirs are essential for daily life and agriculture, especially in rural areas. Despite its importance, 3.6 million communities are affected by severe water quality problems, contributing to a high incidence of chronic kidney diseases. While water purification plants are the only solution, high salinity and hardness often make these sources unsuitable. Many rural reservoirs remain unpurified but could potentially provide safe water if properly monitored and maintained. Yet water sources are frequently contaminated by chemical waste and activities associated with agricultural business but effective solutions for continuous water quality monitoring are lacking. This study introduces an autonomous robot equipped with advanced sensors to monitor critical water quality parameters in real-time. By integrating machine learning for data analysis and prediction, Sagittarius enables continuous assessment of reservoir water quality, providing timely insights for public health protection. Field tests demonstrate a prediction accuracy exceeding 98.5%, supporting proactive water management and monitoring. This system has shown potential to aid in early detection of contamination, informing authorities promptly for preventive actions, and enabling sustainable water usage. This solution can significantly contribute to public health protection and informed water resource management decisions.

关键词： Purification Salinity (geophysical) Water quality Reservoirs Water pollution Real-time systems Sensors Public healthcare Protection Monitoring

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Enhanced Crow Search with Deep Learning-Based Cyberattack Detection in SDN-IoT Environment

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Intelligent Automation & Soft Computing 2023年第6期36卷 3157-3173页

作者： Abdelwahed Motwakel Fadwa Alrowais Khaled Tarmissi Radwa Marzouk Abdullah Mohamed Abu Sarwar Zamani Ishfaq Yaseen Mohamed I.Eldesouki Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMeccaSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharj16436Saudi Arabia

The paradigm shift towards the Internet of Things(IoT)phe-nomenon and the rise of edge-computing models provide massive poten-tial for several upcoming IoT applications like smart grid,smart energy,smart home,smart health and smart transportation ***,it also provides a sequence of novel cyber-security *** IoT networks provide several advantages,the heterogeneous nature of the network and the wide connectivity of the devices make the network easy for *** result in financial loss and data breaches for organizations and ***,it becomes crucial to secure the IoT environment from such *** this motivation,the current study introduces an effectual Enhanced Crow Search Algorithm with Deep Learning-Driven Cyberattack Detection(ECSADL-CAD)model for the Software-Defined Networking(SDN)-enabled IoT *** presented ECSADL-CAD approach aims to identify and classify the cyberattacks in the SDN-enabled IoT *** attain this,the ECSADL-CAD model initially pre-processes the *** the presented ECSADL-CAD model,the Reinforced Deep Belief Network(RDBN)model is employed for attack *** last,the ECSA-based hyperparameter tuning process gets executed to boost the overall classification outcomes.A series of simulations were conducted to validate the improved outcomes of the proposed ECSADL-CAD *** experimental outcomes confirmed the superiority of the proposed ECSADL-CAD model over other existing methodologies.

关键词： Software defined networks artificial intelligence cybersecurity deep learning internet of things

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