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real time Gas Leak Detection with IoT: A Gateway to Safer and Smarter Homes

Real Time Gas Leak Detection with IoT: A Gateway to Safer an...

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Annual Information Technology, Electromechanical Engineering and Microelectronics conference (IEMECON)

作者： Shibagni Bhattacharjee Aastha Sinha Raj Krish Debajyoti Chatterjee Surajit Sur Department of CSE University of Engineering and Management Jaipur India Department of CSE Faculty University of Engineering and Management Jaipur India Department of ECE Faculty University of Engineering and Management Jaipur India

ISBN: (数字)9798350387315

ISBN: (纸本)9798350387322

the increasing reliance on flammable gases such as methane, butane, and liquefied petroleum gas (LPG) in residential and commercial buildings necessitates the development of efficient gas leakage detection systems to mitigate potential hazards. this paper presents the design and implementation of an advanced IoT- based gas leakage sensor, engineered to enhance safety in buildings and apartments. the system utilizes sensitive gas sensors that detect the presence of flammable gases and activate audible alarms to alert occupants of potential danger. this reliable and cost- effective solution ensures early leak detection, preventing life- threatening situations and minimizing damage. this IoT-based automatic gas leakage sensor is designed to enhance safety in buildings and apartments. the system detects the presence of flammable gases such as methane, butane, and LPG. Upon detection, the sensor immediately activates a loud beep sound to alert occupants of the danger. the system ensures early detection of gas leaks, preventing potential hazards and safeguarding the lives of individuals within the vicinity. this reliable and cost- effective solution ensures early leak detection, preventing life- threatening situations and minimizing damage. this paper discusses the design, implementation and performance of the system.

关键词： Methane Gases Costs Buildings real-time systems Hazards Reliability Internet of things Leak detection Gas detectors

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Ai Machine Learning: Transformer Models For Enhanced Natural Language Processing - Techniques And applications

Ai Machine Learning: Transformer Models For Enhanced Natural...

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Annual Information Technology, Electromechanical Engineering and Microelectronics conference (IEMECON)

作者： Pragith S G.M. Karthik T. Sripriya E Rajasekaran S. Manthandi Periannasamy Mohamed Zubair Aslam M A Department of Computer Science Engineering Vellore Institute of Technology Vellore Tamil Nadu India Department of Information Security School of Computer Science and Engineering (SCOPE) Vellore Institute of Technology Vellore Tamil Nadu India Department of ECE Jeppiaar Institute of Technology Chennai Tamil Nadu India Department of Science and Humanities V.S.B Engineering College Karur Tamil Nadu India Department of Electronics and Communication Engineering Mallareddy Engineering College for Women Maisammaguda Hyderabad Department of AI & DS Dhanalakshmi College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798350387315

ISBN: (纸本)9798350387322

Modern technologies are needed to strengthen network security defenses because of the increase in cyber attacks in recent years. Because deep learning in particular allows for real-time threat identification and thorough performance evaluation, artificial intelligence (AI) has become a powerful tool for improving cybersecurity measures. this study examines how deep learning methods can be used in AI cybersecurity frameworks, with an emphasis on how well they can detect and counteract changing cyberthreats. the use of convolutional neural networks (CNNs) and deep neural networks (DNNs) for anomaly detection and pattern identification in network traffic is specifically discussed. the study also investigates the integration of AI-driven analytics to assess the performance and efficacy of security protocols, offering insights into optimizing defensive strategies against sophisticated cyber attacks. By leveraging AI capabilities, organizations can achieve proactive threat management, enhance incident response times, and fortify their networks against emerging cyber threats. this abstract sets the stage for a comprehensive exploration of AI's role in cybersecurity, highlighting its transformative impact on network security operations.

关键词： Deep learning Performance evaluation Standards organizations Network security Natural language processing real-time systems time factors Convolutional neural networks Artificial intelligence Cyberattack

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A Design Aid Tool for Asynchronous SAR ADCs

A Design Aid Tool for Asynchronous SAR ADCs

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Electrical Engineering (ICEENG), 12th international conference on

作者： Ayman A. El-Hinnawey Ayman H. Ismail Electronics and Comm. Engineering Ain Shams University Cairo Egypt

ISBN: (数字)9798350343427

ISBN: (纸本)9798350343434

Successive approximation register analog-to-digital converters (SAR ADCs) are one of the most common ADC architectures due to their high energy efficiency in the medium-to-high resolution range. Consequently, they are essential for many applications, including the Internet of things. However, the design process for SAR ADCs can be time-consuming and error-prone. Assessing the feasibility of specific process node requirements promptly, especially when considering complex specifications and tradeoffs, poses a significant challenge. this work presents a design automation tool for asynchronous SAR ADCs. the tool takes top-level specifications as input and provides device sizing results. the automation process adopts a hybrid approach, combining equations and lookup tables (LUTs) to automate critical design steps. these steps include capacitor sizing, sample and hold sizing, and comparator design. Both design time and manual effort are substantially reduced when employing this tool. To showcase the efficacy of the proposed tool, an 8-bit 8MS/s ADC design was generated in 65-nm CMOS technology. the resulting ADC performs very well, with an output power consumption of 36.78 μW at a supply voltage of 1 volts, a FoMw of 18.69 fJ/conversion-step, and a FoMs of 159.94 dB.

关键词： Degradation Automation Layout Voltage Manuals Mathematical models real-time systems

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Advanced Skin Disease Classification with VGG19, YOLOv3, and MobileNet

Advanced Skin Disease Classification with VGG19, YOLOv3, and...

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international conference on computing and Networking Technology (ICCNT)

作者： Himani Bhatia Satvik Vats Rishabh Sharma Lovely Professional University Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun India Graphic Era Deemed to be University Dehradun India Chitkara University Institute of Engineering and Technology Chitkara University Rajpura Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Skin diseases are classified into various types hence proper classification of the disease is very important because its determination of the type of treatment to offer the patients will highly determine the results of the patients. this research paper presents an advanced approach to skin disease classification by employing three state-of-the-art deep learning models: VGG19 – a convolutional model that is 19 layers deep, YOLOv3 – an improved version of the YOLO algorithm, MobileNet – a lightweight neural network designed for mobile devices. the performance of these models will be assessed and compared to identify the most effective way of categorizing numerous skin diseases using a large data set. ResNet and VGG19 are employed based on their resilience in feature extraction, and despite the model’s complexity, it is rather accurate. When it comes to the detection of skin abnormalities, object localization is critical, for which YOLOv3 is used because of its high accuracy. MobileNet is evaluated for its applicability in real-time classification for mobile and integrated embedded device applications. the dataset is composed of skin lesion images and has high resolutions and dermatoscopic images, which means that the models had a diverse set of skin conditions on which to train. Metrics including accuracy, precision, recall, and F1-score are used to assess and rank the efficiency of one specific model with another. In highlighting the strengths and weaknesses of each model, the findings help in informing studies applying such frameworks in real-world practice contexts. this present comparative study intends to Provide an Improved understanding in the field of dermatology With Better Machine Learning Algorithms to Automate skin Disease Identification, with an impressive patient care impact.

关键词： YOLO Deep learning Accuracy Machine learning algorithms Computational modeling Skin real-time systems Lesions Diseases Load modeling

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Closing the Teacher-Learner Loop: the Role of Affective Signals in Interactive RL

Closing the Teacher-Learner Loop: The Role of Affective Sign...

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international conference on Affective computing and Intelligent Interaction Workshops and Demos (ACIIW)

作者： Bernhard Hilpert LIACS Leiden University Leiden Netherlands

ISBN: (数字)9798331516451

ISBN: (纸本)9798331516468

Hybrid Intelligence (HI) is defined as “the combination of human and machine intelligence” for collaboration, “achieving goals that were unreachable by either [alone]” [2], leveraging the strength of both machine intelligence (such as strong optimization capabilities, effective handling of probabilities and less fallacy for confirmation biases) and human intelligence (such as generalization capabilities, situational understanding and common sense). In their research agenda, Akata and colleagues identify three key challenges for creating HI systems that relate to the interactive process: HI should be adaptive (how can a system learn from and adapt to humans and vice versa), explainable (how to create shared and explained awareness, goals and strategies) and collaborative (how to work in synergy). A popular learning mechanism for robots and AI agents that allows agents to dynamically adapt to their environment and promises interesting opportunities for adaptation in Human-Agent Interaction scenarios is Reinforcement Learning (RL). In RL, an agent learns through exploration and optimization of reward-based feedback for future actions (e.g. through the value-function method, policy search and actor critic approaches, [26]). While RL agents have been able to demonstrate their potential for success in a broad range of narrow tasks, many real-life applications present them with high-dimensional or continuous state-spaces that are not always fully observable which renders exploration and reproduction of actions costly and slow and usually require a considerable amount of domain knowledge or common sense, in order to succeed [19]. A more hybrid approach with Human-in-the-loop Reinforcement Learning, where human users enrich task learning in the form of teaching signals, provides a compelling modification of this setting that allows to shape and improve the learning process through methods such as evaluative teaching, Learning from Demonstration and instruction [9], [21] and

关键词： Learning systems Affective computing Shape Education Collaboration Reinforcement learning Space exploration Optimization Machine intelligence Robots

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applications of Deep Learning in Unified Credit Management of Commercial Banks

Applications of Deep Learning in Unified Credit Management o...

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international conference on Measuring Technology and Mechatronics Automation, ICMTMA

作者： Yang Wang Zhen-Dong Li Xia-Qing Xi Post-doctoral Research Workstation Bank of Zhengzhou Zhengzhou China School of Information Engineering Ningxia University Yinchuan China School of Economics Henan Institute of Technology Xinxiang China

ISBN: (数字)9781728170817

ISBN: (纸本)9781728170824

With the rapid development of new technologies, such as cloud computing, mobile internet, financial technology and big data, commercial banks in China have to step up their efforts to use new technologies such as artificial intelligence to innovate. In this paper, we focus on detecting the image orientation and rotating the image to the right angle automatically based on convolutional neural network in unified credit management of banks. First, in view of the characteristics of scanned images in banking systems, we analyze the process of credit evaluation and approval of Zhengzhou bank in detail. then, we convert the problem of skew correction into image classification in unified credit management of commercial banks, and we fine-tuning the pretrained VGG16 model with TensorFlow for image classification. Finally, we develop a deep learning platform in bank of Zhengzhou to test the performance of the proposed method. In contrast to traditional image processing methods, the test results on bank's real dataset show the superiority of our proposed method, and the proposed method has been applied in the process of credit approval to improve the automation and intelligence level of Zhengzhou Bank to a certain extent.

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Optimized adaptive sliding-mode position control system for linear induction motor drive

Optimized adaptive sliding-mode position control system for ...

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IEEE international conference on Networking, Sensing and Control

作者： Kou-Cheng Hsu Hsin-Han Chiang Chin-I Huang Tsu-Tian Lee Department Electrical Engineering Fu Jen Catholic University New Taipei Taiwan Fu Jen Catholic University College of Medicine New Taipei City New Taipei City TW Department Electrical Engineering Chung Yuan Christian University Chung Li Taiwan

ISBN: (纸本)9781467351980

this paper proposes an optimized adaptive position control system applied for a linear induction motor (LIM) drive taking into account the longitudinal end effects and uncertainties including the friction force. the dynamic mathematical model of an indirect field-oriented LIM drive is firstly derived for controlling the LIM. On the basis of a backstepping control law, a sliding mode controller (SMC) with embedded fuzzy boundary layer is designed to compensate the lumped uncertainties during the tracking control of periodic reference trajectories. Since it is difficult to obtain the bound of lumped uncertainties in advance in practical applications, an adaptive tuner based on the sense of Lyapunov stability theorem is derived to adjust the fuzzy boundary parameters in real-time. It is a quite complicated process of parameter tuning, especially for the proposed controller, due to the difficulty arisen from lacking of the accurate mathematical model of a system accompanied with unknown disturbance. therefore, the soft-computing technique is adopted for off-line optimizing the controller parameters. the effectiveness of the proposed control scheme is validated through simulations and experiments for several scenarios. Finally, the advantages of performance improvement and robustness are illustrated at the end of the optimization procedure.

关键词： Uncertainty Mathematical model Control systems Genetic algorithms Force Optimization Induction motors

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Parallel pattern mining on Graphics Processing Units

Parallel pattern mining on Graphics Processing Units

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international Carpathian Control conference (ICCC)

作者： Krzysztof Hryniów Institute of Control and Industrial Electronics Warsaw University of Technology Warsaw Poland

ISBN: (纸本)9781467344883

Frequent pattern mining is a field with many practical applications, where large computational power and speed are needed. Many state-of-the-art frequent pattern mining applications are an inefficient solutions for both shared memory and multiprocessor systems due to problems with parallelism and memory. One of possible solutions to the problem is the use of Graphics Processing Unit (GPU) in the system along with modification of classical pattern mining algorithms in such a way, that the sequential part of algorithm is run on host and the parallel part on GPU. Such solution allows for considerable speed-up (of up to two orders of magnitude), but for more complicated problems and FPM algorithms it can be hard to achieve. So far there were presented 3 modifications of the most basic Apriori algorithm for solving GPGPU (general-purpose computation on graphics hardware) problems. Each of proposed parallel implementations (PBI, TBI, GPA) is suited only for frequent itemset mining, furthermore even the best of them (TBI) obtains results only 2-5 times faster then CPU-based versions of FP-growth algorithm. On ICCC'12 there was presented parallel version of more complex GSP algorithm adjusted for GPGPU problems, capable of obtaining results 50-100 times faster then CPU-based version, with the same accuracy. Not only is the GPGPU implementation the fastest, but also it is suited for solving more complex frequent pattern mining problems. this paper presents extension of proposed modifications to more complex algorithms from Apriori family. the modifications are evaluated both theoretically and with use of experimental setup consisting of nVidia Tesla card and CUDA parallel computing platform. Solution proposed in the paper uses more complex, faster frequent pattern mining algorithms then GSP, making it well suited for solving real-time GPGPU problems for very large data sets.

关键词： Graphics processing units Data mining Databases Kernel Resource management real-time systems Memory management

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Towards real-time Vietnamese Traffic Sign Recognition on embedded systems

Towards Real-Time Vietnamese Traffic Sign Recognition on Emb...

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international conference on Information and Communication Technology Convergence (ICTC)

作者： Phuong-Nam Tran Nhat Truong Pham Nam Van Hai Phan Duc Tai Phane Cuong Tuan Nguyen Duc Ngoc Minh Dang Dept. of Computer Science and Engineering Kyung Hee University Gyeonggi-do Republic of Korea Dept. of Integrative Biotechnology Sungkyunkwan University Suwon Republic of Korea Dept. of Computing Fundamental FPT University Ho Chi Minh City Vietnam Faculty of Engineering Vietnamese-German University Binh Duong Vietnam

ISBN: (数字)9798350364637

ISBN: (纸本)9798350364644

AI development has brought many significant changes in various aspects of our daily lives in recent years. Integrating AI technology into various applications has revolutionized multiple domains, and one particularly vital area is traffic sign recognition, which significantly enhances driver safety. this paper presents an approach to traffic sign recognition specifically designed for the Jetson Nano 2GB device. By utilizing the YOLOv8 Nano model, the proposed approach achieves a remarkable frame rate of up to 32 frames per second (FPS). To optimize inference speed on Jetson with limited memory, the approach incorporates TensorRT and quantization techniques. In addition, this paper introduces a dataset called the Vietnamese Traffic Sign Detection Database 100 (VTSDB100). this dataset is an extension of the VTSDB46 dataset and encompasses a comprehensive collection of 100 different classes of traffic signs. these signs were captured in diverse locations within Ho Chi Minh City, Vietnam, providing a rich and diverse dataset for training and evaluating traffic sign recognition models. Extensive experiments and analyses were conducted using various object detection methods on the VTSDB100 dataset. the findings highlight the potential of deploying the proposed approach on resource-constrained devices and provide valuable insights for further research and development in AI-powered driver safety systems.

关键词： Training Quantization (signal) Databases Web services Urban areas Object detection real-time systems Safety Internet of things Artificial intelligence

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Madeira Mosquito Surveillance App (MMSA): Leveraging Mobile Phone Apps for Enhanced Mosquito Surveillance

Madeira Mosquito Surveillance App (MMSA): Leveraging Mobile ...

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IEEE international conference on Serious Games and applications for Health (SeGAH)

作者： Aisha Aldosery Anwar Musah Bruna Ornelas De Gouveia Patty Kostkova UCL Centre for Digital Public Health in Emergencies (dPHE) University College London London United Kingdom UCL Department of Geography Geospatial Analytics and Computing University College London London United Kingdom ITI– LARSyS Regional Health Directorate – Secretariat of Health and Civil Protection Funchal Portugal

ISBN: (数字)9798350384383

ISBN: (纸本)9798350384390

Mosquito surveillance is crucial for understanding the dynamics of mosquito populations, the implementation of public health programme aimed at controlling and preventing the spread of mosquito-borne diseases such as zika, chikungunya and dengue. Environmental surveillance agents, tasked with conducting routine entomological surveys in regions burdened with infective mosquitoes play an essential role in vector surveillance. Digital intervention, through mobile phone technology, offers the potential to augment mosquito surveillance efforts by overcoming the limitations inherent in traditional paper-based data collection methods. this public health informatics study introduces a multi-profile mobile application designed to enhance the process of data collection from mosquito ovitraps, ensure timely reporting, and improve field worker performance in Madeira Island, with an emphasis on user-centred design. the application seeks to boost operational efficiency for field agents and to streamline decision-making processes for health authorities. It explores the technical design, user experience, and architecture, alongside the challenges faced during implementation, including logistical hurdles during adaptation. Crucially, this research considers the varied on-groundchallenges encountered by agents, such as fears of data loss or errors in entry, which add complexity to the digital transition. the transition to a digitised data collection system is poised to have a significant impact which could facilitate the timely use of predictive analytical models to forecast mosquito population dynamics, enabling the early detection of hotspots. this, in turn, would allow fieldworker agents to be alerted to immediate action and direct their efforts to high-risk areas, significantly improving the efficiency of interventions against mosquito-borne diseases.

关键词： Surveillance Data collection Aerodynamics Vectors real-time systems Mobile handsets User experience

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