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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Nandankar, Praful V. Kothiyal, Arnav Kiran Kumar, D. Patil, Anuradha Patil, Harshal Maranan, Ramya Government College of Nagpur Department of Electrical Engineering Maharashtra 441108 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Bgs Institute of Technology Department of Information Science and Engineering Karnataka B G Nagar571448 India Sharnbasva University Faculty of Engineering and Technology Exclusively for Women Department of Electronics and Communication Engineering Karnataka Kalaburagi585103 India Iilm University School of Computer Science & Engineering Uttar Pradesh Greater Noida201 306 India Simats Saveetha School of Engineering Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798350376425

Parkinson's Disease (PD) is a degenerative neurological condition that impairs motor function. Tremors, stiffness, sluggishness, and trouble balancing are its hallmarks. Degeneration of dopamine-producing neurons in the brain is the disease's primary cause, resulting in progressive motor and non-motor symptoms. But the existing methods have lot of disadvantages such as over fitting, quality control and limited data availability. To overcome the aforementioned problem, A Spherical Convolutional Neural Network with Mantis Search Algorithm (SCNN-MSA) is proposed for accurately classifying PD. In this input image is taken from two datasets such as ADL dataset and REMPARK dataset. The Wavelet Decomposition Threshold Anisotropic Filter is used for the pre-processing (WDTAF) is proposed, for the purpose of removing noises. Following that segmentation are done using Spike-Driven Transformer-based U-Net (SDT-Unet). After that classification and feature extraction are done using Spherical Convolutional Neural Network (SCNN) and optimization are done using Mantis Search Algorithm (MSA) for classifying the Parkinson's Disease. The efficiency of the proposed SCNN-MSA is analyzed using a dataset and attains 99.93% accuracy, 98.76% recall and attains better results compared with the existing methods. This demonstrates the approach's increased efficiency and potential advancement in the industry. The model's accuracy at identifying PD in EMG signals might allow doctors to treat patients considerably faster. © 2024 IEEE.

关键词： Convolutional neural networks

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Smart Ports Solutions for Cargo Container Tracking and Vessel Traffic Counting Systems using IoT and Cloud Computing 10

Smart Ports Solutions for Cargo Container Tracking and Vesse...

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10th International Conference on communication and Signal Processing, ICCSP 2024

作者： Priya, S. Sairam, A. Azath, H. Manivannan, S.K. Mohankumar, N. Vedasundara Vinayagam, P. Deemed To Be University Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India Vit Bhopal University School of Computing Science and Engineering Bhopal India Faculty of Management Srm Institute of Science and Technology Chennai Tamil Nadu Kattankulathur India Symbiosis Institute of Technology Maharashtra Nagpur India Adhiparasakthi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Melmaruvathur India

ISBN: (纸本)9798350353068

This research examines current port operations that use Internet of Things (IoT) technologies for cargo container monitoring and vessel traffic counts. Effective port management is crucial for smooth commodities movement and congestion reduction as global commerce grows. Addressing these issues with IoT solutions is promising. It analyzes cargo container IoT sensors and devices for real-time location, condition, and security monitoring. These IoT-enabled containers provide port status data for better logistical planning and security. IoT devices are used to count and control vessel traffic in the research. Sensors and data analytics at important port locations provide real-time vessel tracking and traffic monitoring. This data improves safety and optimizes vessel traffic flow, lowering congestion and wait times. The pros and downsides of using IoT in port operations include data privacy, security, and infrastructure needs. Smart port technologies may enhance marine efficiency, cost savings, and sustainability. The IoT can revolutionize port operations, notably cargo container monitoring and vessel traffic counts. Port capacity, security, and the environmental and economic viability of worldwide trade may all be improved with the help of the IoT. © 2024 IEEE.

关键词： Internet of things

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Ensemble Deep Learning for Human-Object Interaction Detection 2

Ensemble Deep Learning for Human-Object Interaction Detectio...

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2nd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2022

作者： Mansour, Ahmed E. Mohammed, Ammar Elsayed, Hussein Abd El Atty Ramly, Salwa H. El Ain Shams University Faculty of Engineering Electronics and Electrical Communication Department Cairo Egypt Cairo University Faculty of Graduate Studies of Statistical Researches Computer Science Department Cairo Egypt

ISBN: (纸本)9781665466776

Human-object interaction (HOI) detection is the task of predicting the visual relationships between humans and their surroundings in images and videos by locating and inferring the interactions between human-object pairs. The majority of existing models have approached this task by detecting human and object instances and predicting interactions between them using a single model and relying on visual features to differentiate between different actions. In this article, we propose a novel method for solving the HOI detection problem by employing ensemble voting on simple models that use only spatial features to predict human-object pair actions. Additionally, we solve the false-positive pairs generated by mis-grouping and non-interaction objects in the image using a bipartite matching. Our proposed approach outperforms many state-of-the-art models on the V-COCO dataset while requiring less inference time. © 2022 IEEE.

关键词： Visualization Computational modeling Object detection Predictive models Feature extraction Ubiquitous computing Prediction algorithms

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Smart Energy Management System Using Machine Learning

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computers, Materials & Continua 2024年第1期78卷 959-973页

作者： Ali Sheraz Akram Sagheer Abbas Muhammad Adnan Khan Atifa Athar Taher M.Ghazal Hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeSharjah1797United Arab Emirates Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Energy management is an inspiring domain in developing of renewable energy ***,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and *** major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental *** energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user *** address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve *** inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy *** Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)*** IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource *** this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management *** proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

关键词： Intelligent energy management system smart cities machine learning

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Driving Activity Classification Using Deep Residual Networks Based on Smart Glasses Sensors

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Intelligent Automation & Soft Computing 2023年第11期38卷 139-151页

作者： Narit Hnoohom Sakorn Mekruksavanich Anuchit Jitpattanakul Department of Computer Engineering Image Information and Intelligence LaboratoryFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Computer Engineering School of Information and Communication TechnologyUniversity of PhayaoPhayaoThailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research InstituteKing Mongkut’s University of Technology North BangkokBangkok10800Thailand

Accidents are still an issue in an intelligent transportation system,despite developments in self-driving technology(ITS).Drivers who engage in risky behavior account for more than half of all road *** a result,reckless driving behaviour can cause congestion and *** vision and multimodal sensors have been used to study driving behaviour categorization to lessen this *** research has also collected and analyzed a wide range of data,including electroencephalography(EEG),electrooculography(EOG),and photographs of the driver’s *** the other hand,driving a car is a complicated action that requires a wide range of body *** this work,we proposed a ResNet-SE model,an efﬁcient deep learning classiﬁer for driving activity clas-siﬁcation based on signal data obtained in real-world trafﬁc conditions using smart ***-to-end learning can be achieved by combining residual networks and channel attention approaches into a single learning *** data from 3-point EOG electrodes,tri-axial accelerometer,and tri-axial gyroscope from the Smart Glasses dataset was utilized in this *** performed various experiments and compared the proposed model to base-line deep learning algorithms(CNNs and LSTMs)to demonstrate its *** to the research results,the proposed model outperforms the previous deep learning models in this domain with an accuracy of 99.17%and an F1-score of 98.96%.

关键词： Smart glasses human activity recognition deep learning wearable sensors driving activity

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Resource-efficient neural networks for embedded systems

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The Journal of Machine Learning Research 2024年第1期25卷 2506-2556页

作者： Wolfgang Roth Günther Schindler Bernhard Klein Robert Peharz Sebastian Tschiatschek Holger Fröning Franz Pernkopf Zoubin Ghahramani Graz University of Technology Austria Laboratory of Signal Processing and Speech Communication Heidelberg University Germany Institute of Computer Engineering Graz University of Technology Austria Institute of Theoretical Computer Science University of Vienna Austria Faculty of Computer Science University of Cambridge UK

While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation, and the vision of the Internet of Things fuel the interest in resource-efficient approaches. These approaches aim for a carefully chosen trade-off between performance and resource consumption in terms of computation and energy. The development of such approaches is among the major challenges in current machine learning research and key to ensure a smooth transition of machine learning technology from a scientific environment with virtually unlimited computing resources into everyday's applications. In this article, we provide an overview of the current state of the art of machine learning techniques facilitating these real-world requirements. In particular, we focus on resource-efficient inference based on deep neural networks (DNNs), the predominant machine learning models of the past decade. We give a comprehensive overview of the vast literature that can be mainly split into three non-mutually exclusive categories: (i) quantized neural networks, (ii) network pruning, and (iii) structural efficiency. These techniques can be applied during training or as post-processing, and they are widely used to reduce the computational demands in terms of memory footprint, inference speed, and energy efficiency. We also briey discuss different concepts of embedded hardware for DNNs and their compatibility with machine learning techniques as well as potential for energy and latency reduction. We substantiate our discussion with experiments on well-known benchmark data sets using compression techniques (quantization, pruning) for a set of resource-constrained embedded systems, such as CPUs, GPUs and FPGAs. The obtained results highlight the difficulty of finding good trade-offs between resource efficiency and prediction quality.

关键词： resource-efficient machine learning inference deep neural networks

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Deep Pyramidal Residual Network for Indoor-Outdoor Activity Recognition Based on Wearable Sensor

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Intelligent Automation & Soft Computing 2023年第9期37卷 2669-2686页

作者： Sakorn Mekruksavanich Narit Hnoohom Anuchit Jitpattanakul Department of Computer Engineering School of Information and Communication Technology University of PhayaoPhayao56000Thailand Image Information and Intelligence Laboratory Department of Computer EngineeringFaculty of EngineeringMahidol UniversityNakhon Pathom73170Thailand Department of Mathematics Faculty of Applied ScienceKing Mongkut’s University of Technology North BangkokBangkok10800Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Science and Technology Research Institute King Mongkut’s University of Technology North BangkokBangkok10800Thailand

Recognition of human activity is one of the most exciting aspects of time-series classification,with substantial practical and theoretical *** evidence indicates that activity recognition from wearable sensors is an effective technique for tracking elderly adults and children in indoor and outdoor ***,researchers have demon-strated considerable passion for developing cutting-edge deep learning sys-tems capable of exploiting unprocessed sensor data from wearable devices and generating practical decision assistance in many *** study provides a deep learning-based approach for recognizing indoor and outdoor movement utilizing an enhanced deep pyramidal residual model called Sen-PyramidNet and motion information from wearable sensors(accelerometer and gyroscope).The suggested technique develops a residual unit based on a deep pyramidal residual network and introduces the concept of a pyramidal residual unit to increase detection *** proposed deep learning-based model was assessed using the publicly available 19Nonsens dataset,which gathered motion signals from various indoor and outdoor activities,including practicing various body *** experimental findings demon-strate that the proposed approach can efficiently reuse characteristics and has achieved an identification accuracy of 96.37%for indoor and 97.25%for outdoor ***,comparison experiments demonstrate that the SenPyramidNet surpasses other cutting-edge deep learning models in terms of accuracy and ***,this study explores the influence of several wearable sensors on indoor and outdoor action recognition ability.

关键词： Human activity recognition deep learning wearable sensors indoor and outdoor activity deep pyramidal residual network

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Enhancing IoT Network Security: A Double Decker Convolutional Neural Network with Brown-Bear Optimization for Intrusion Detection 8

Enhancing IoT Network Security: A Double Decker Convolutiona...

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8th International Conference on Inventive Computation Technologies, ICICT 2025

作者： Chaturvedi, Abhay Suhas, S. Divya Shivani, J.L. Raja, Ch. Soni, Umang Natrayan, L. Department of Electronics & Communication Engineering GLA University Uttar Pradesh Mathura 281406 India Department of Computer Science and Engineering (CSE) SJCE JSS Science and Technology University Karnataka Mysore India Department of Electronics and Communication Engineering CMR College of Engineering & Technology Telangana Hyderabad 501401 India Department of Electronics and Communication Engineering Mahatma Gandhi Institute of Technology Telangana Hyderabad 500075 India Department of Electronics & Communication Engineering Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Post Limda Gujarat 391760 India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai 602105 India

ISBN: (纸本)9798331512248

The extensive adoption of Internet of Things (IoT) networks has exponentially increased cybersecurity threats, necessitating effective intrusion detection mechanisms to secure connected devices. Traditional security mechanisms fail to detect sophisticated cyberattacks as IoT environments are dynamic in nature. Existing intrusion detection techniques suffer from high false alarms and low computational efficiency, making them impractical to apply in resource-constrained IoT networks. To overcome these challenges, this paper presents Enhancing IoT Network Security: A Double Decker Convolutional Neural Network with Brown-Bear Optimization for Intrusion Detection (BrBO-DD-CNN), which yields high detection efficiency and accuracy. This paper utilizes data from the NSL-KDD and BoT-IoT datasets, which are IoT network traffic and attack types. Raw data is preprocessed by Min-Max Zscore Normalization to remove duplicates and complete missing values. Planet Optimization selects significant features, reducing data complexity without losing key information. Preprocessed data is input to a Double Decker Convolutional Neural Network that detects low-level and high-level features to differentiate normal and malicious traffic. Brown-Bear Optimization optimizes model parameters, enhancing convergence and detection performance. This research yields an accuracy of 99.98% for NSL-KDD and 99.95% for BoT-IoT. Detection rate is 99.97% and 99.93%, and F1-score is 99.96% and 99.92%, respectively, proving its effectiveness. The results guarantee that BrBO-DD-CNN performs better than existing intrusion detection techniques, offering a highly accurate, efficient, and scalable intrusion detection mechanism to secure IoT networks against emerging cyberattacks. © 2025 IEEE.

关键词： Attack BoT-IoT Internet of Things Intrusion Detection NSL-KDD

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Necessity of emergent materials development for air filtration to increase air quality

Necessity of emergent materials development for air filtrati...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Omar Ahmed Mihai Oproescu Gabriel-Vasile Iana Science and Materials Engineering Doctoral School National University of Science and Technology Politehnica Bucharest Faculty of Electronics Communication and Computer Science Pitești University Centre National University of Science and Technology POLITEHNICA Bucharest Pitesti Romania Faculty of Electronics Communication and Computer Science National University of Science and Technology POLITEHNICA Bucharest

ISBN: (数字)9798350371154

ISBN: (纸本)9798350371161

Air quality is a major public health issue globally. Air pollution is responsible for millions of premature deaths annually and can cause a range of health problems, including respiratory diseases. The need to develop emergent materials and new technologies to increase air quality offers a series of opportunities due to the benefits offered. Emerging materials can be used to create more efficient air filters, air quality monitoring sensors and catalysts to reduce emissions. This article summarizes the main legislative regulations for reducing pollution, as well as emerging materials that play a role in increasing air purity. The main methods of increasing air quality are highlighted: nanoremediation and nanofiltration, mentioning their main disadvantages.

关键词： Nanoparticles Air cleaners Volatile organic compounds Electric potential Filters Filtration Air pollution

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Diversified Grid Optimization Model to Handle Fast Charging Provision of Electric Vehicles Using Artificial Intelligence (AI) Technique

Diversified Grid Optimization Model to Handle Fast Charging ...

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2024 International Conference on Innovative Computing, Intelligent communication and Smart Electrical Systems, ICSES 2024

作者： Karthik, G. Kumar, R.Sampath Manimegalai, L. Rajkumar, G.Vinoth Adkane, R.V. Odeh, Mahmoud Department of Computer Science and Engineering Salem India Kpr institute of Engineering and Technology Department of Mechatronics Engineering Coimbatore India R.M.K. College of Engineering and Technology Department of Electronics and Communication Chennai India J.P College of Engineering Department of Electronics and Communication Engineering Tenkasi India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Zarqa University Faculty of Information Technology Department of Cyber Secuity Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

The transportation industry is taking a giant leap forward towards a dependable, carbon-neutral future thanks to electric vehicles (EVs). The growing number of electric vehicles on the road is putting a strain on power networks and electrical grids caused by dispersed energy resources, particularly the EVI's demands for charging and discharging. Without proper coordination of charging and discharging, the already overburdened power distribution system would struggle to handle the large number of electric vehicles on the road. To get more people to use scheduling programs, electric vehicle owners can use dynamic pricing, a kind of demand response. Studying the timing of electric vehicle charging and discharging as well as its dynamic pricing model is, hence, crucial. There has been a lot of study on scheduling and demand forecasting models for electric vehicle charging that employ artificial intelligence, with many academics claiming that AI approaches outperform more traditional optimization techniques. The Diversified Grid Optimization Model (DGOM) is a novel method for optimizing charging facilities that aims to provide fast and efficient charging. To test its efficacy, it is cross-validated with the Dynamic Vehicle Charging Model (DVCM), a traditional charging scheme. The investigations pertaining to electric vehicle charging and discharging are categorized into scheduling, pricing, and forecasting in this paper's experimental analysis. This work finds the missing pieces of the puzzle when it comes to dynamic pricing models and EV discharge scheduling by establishing the connection between forecasting, scheduling, and pricing mechanisms. © 2024 IEEE.

关键词： State of charge

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