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Enhancing Industrial Load Scheduling through Demand Side management and Advanced Optimization Techniques Using Neuro-Fuzzy and Deep Learning Approaches 4

Enhancing Industrial Load Scheduling through Demand Side Man...

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4th IEEE International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2023

作者： Agarwal, Shivani Sharma, Ritu Tamilselvi, M. Sharma, Harsh Mohan Sahu, Dinesh Prasad Singh, Jagendra Ajay Kumar Garg Engineering College Department of Information Technology Ghaziabad India Gyan Ganga Institute of Technology and Science Dept. of Electrical and Electronics Engineering Jabalpur India Roever Engineering College Perambalur Deartment of Computer Science and Engineering Perambalur India Raj Kumar Goel Institute of Technology Department of Management Ghaziabad India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350306118

This study aims to investigate whether physical factors like temperature, humidity, and pressure interact with current in Micro-electromechanical systems (MEMS). To study these interactions, a straightforward system with four spin-operated engines was employed. The pressure, humidity, and temperature may all change as a result of changing the motor's output current. The results are evaluated and predicted using LSTM and Neuro-Fuzzy models. Sensors are used to keep an eye on these circumstances. Neurofuzzy models employ fuzzy logic and artificial intelligence to capture interconnections, as opposed to LSTM models, which use their capacity to comprehend data connections to capture long-Term effects. The results demonstrate that the body's capacity to exert energy is enhanced by the effect of body energy on temperature, humidity, and pressure. The research could lead to improvements in temperature control, energy efficiency, and the functionality and design of massive industrial machinery. A straightforward design is used to launch controlled experiments, and the data collected is then used to train and assess the model. By offering helpful suggestions for the improvement of the motor and the operating electric economy, this study contributes to our understanding of the evolution of the electric motor. © 2023 IEEE.

关键词： Electric loads

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Real-time Arm Motion Tracking and Hand Gesture Recognition Based on a Single Inertial Measurement Unit

Real-time Arm Motion Tracking and Hand Gesture Recognition B...

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International Conference on Internet of Things: Systems, management and Security

作者： Tien-Chiao Chang Yu-Chi Wu Chin-Chuan Han Chao-Shu Chang Dept. of Electrical Engineering National United University Miao-Li Taiwan Dept. of Computer Science and Information Engineering National United University Miao-Li Taiwan Dept. of Information Management National United University Miao-Li Taiwan

ISBN: (数字)9798350366501

ISBN: (纸本)9798350366518

With the development of virtual reality (VR) and augmented reality (AR) devices, handheld controllers and camera-based hand tracking are the most common methods for interacting with the virtual world. This paper proposes arm motion tracking and hand gesture recognition methods based on a single IMU (Inertial Measurement Unit) sensor embedded in a wristband. This approach is more convenient than handheld controllers and more energy-efficient than camera-based tracking. We utilize two deep learning models: one for arm motion tracking and the other for hand gesture recognition. When training the arm motion tracking model, we use two IMU sensors-one placed on the wrist to provide forearm pose data and the other placed above the elbow to provide upper arm pose data. We use the elbow’s IMU data as the reference for training the model and calculate the real-time upper arm pose vector based on the wrist’s pose data. We train the hand gesture recognition model using only the wrist-mounted IMU sensor. This model captures different vibrations in the IMU during various gestures (such as grab, release, snap, and tap) for training. We achieve real-time arm motion tracking and hand gesture recognition using only a single IMU sensor. The angle error for arm motion tracking is only 15 degrees, and the hand gesture recognition accuracy reaches 95%.

关键词： Training Wrist Solid modeling Measurement units Accuracy Tracking Gesture recognition Inertial navigation Real-time systems Data models

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SDN-based Mobile Edge Computing with Vacation of Virtual Machines

SDN-based Mobile Edge Computing with Vacation of Virtual Mac...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Suchismita Rout Ankita Jaiswal Monika Keshao Bagade Sudhansu Shekhar Patra School of Computer Engineering KIIT Deemed To be University Bhubaneswar India Dept. of Computer Sc. & Engineering Shri Ramdeobaba College of Engineering and Management Nagpur India School of Computer Applications KIIT Deemed to be University Bhubaneswar India

A vast array of IoT devices have been implemented worldwide as cloud computing and virtualization have become more and more commonplace. Because of this, processing of computationally demanding tasks is moved from the edge to the centralised cloud, increasing energy consumption in cloud data centres and causing a noticeable delay for vital applications. The paradigm for cloud computing has shifted in recent years from centralised to mobile edge computing (MEC), where computational workloads are delegated to edge servers close to user equipment (UE). This epitome results in minimal latency for UE, effective resource utilisation at the edge, and decreased energy consumption in cloud data centres. Given this, developing optimal energy-saving solutions for resource allocation and computing job offloading in MEC networks has become a challenging challenge due to the rapidly growing size and complexity of these networks. The vacation policy is a suggested power-saving traffic management strategy for edge servers. Software-defined network (SDN) architecture allows virtual machines (VMs) to take vacations. The goal of QoS optimisation is to decrease the active servers serving the tasks while maintaining QoS criteria. Numerous performance metrics of the system are calculated, including average system size, sojourn duration, percentage of client requests fulfilled, and frequency of desertion. Lastly, many numerical results are given to demonstrate the influence of certain particular factors on the MEC performance metrics.

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Unveiling Disparities: NLP Analysis of Software Industry and Vocational Education Gaps

Unveiling Disparities: NLP Analysis of Software Industry and...

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Natural Language-Based Software engineering (NLBSE), IEEE/ACM International Workshop on

作者： Emil Bäckstrand Rasmus Djupedal Lena-Maria Öberg Francisco Gomes De Oliveira Neto Dept. of Quality Management Communication and Inf. Systems Mid Sweden University Östersund Sweden Dept. of Computer Science and Engineering Chalmers|University of Gothenburg Gothenburg Sweden

ISBN: (数字)9798400705762

ISBN: (纸本)9798350352559

The rapid growth of software industry highlights the importance of the education system in producing competent professionals to meet industry demands. Previous research has identified a gap between industry needs and the content of educational programs. This study presents Vocational Education and Labour Market Analyser (VELMA), a tool designed to extract information from job ads and educational curricula (both from Sweden), utilising topic modelling to identify the diverse technologies and skills in demand within the industry and those covered by professional education. Particularly, we use Latent Dirichlet Allocation (LDA) to categorise keywords into cohesive themes for document frequency analysis. Our findings highlight industry demand for skills in cloud and embedded technologies, security engineering, and software architecture. In contrast, the Higher Vocational Education (HVE) curricula emphasise the education of web developers and general object-oriented programming languages. CCS CONCEPTS • Applied computing $\rightarrow$ Document management and text processing; Education; $\cdot$ Software and its engineering $\rightarrow$ Designing software.

关键词： Industries Analytical models Text analysis Object oriented modeling Education Software Natural language processing

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Low power implementation of Geometric High-order Decorrelation-based Source Separation on an FPGA board

Low power implementation of Geometric High-order Decorrelati...

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IEEE Cool Chips

作者： Ziquan Qin Kaijie Wei Hideharu Amano Kazuhiro Nakadai Dept. of Information and Computer Science Keio University Yokohama Japan Dept. of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan

Open source software for robot audition called HARK aims to make “OpenCV” in audio signal processing, providing comprehensive functions from multichannel audio input to sound localization, sound source separation, and au-tomatic speech recognition. Since each of these HARK modules takes considerable energy when executed on PC, we propose to implement each module on an FPGA board called M-KUBOS connected. Here, we focus on the most computationally expensive function of HARK; the sound source separation, and implement it on a Zynq Ultrascale+ board. More than twice a performance improvement was achieved by using the sound frequency level parallelization in the HLS description compared to the software execution on the Ryzen 3990X64-core server. Power evaluation of the real board showed that the energy consumption is only 1/23.4 of the server.

关键词： Location awareness Energy consumption Source separation Power demand Speech recognition Parallel processing Servers

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A novel approach incorporating feature extraction followed by YOLOv7 for quality enhancement of mangoes in Bangladesh

A novel approach incorporating feature extraction followed b...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Debashis Kundu Md. Mushfiqur Rahaman Md. Nazmus Sakib Md. Faruk Abdullah Al Sohan Jannatul Fardaush Mithu Afzal Hossain Nishita Aktar Dept. Management Information Systems American International University- Bangladesh Dhaka Bangladesh Dept. of Computer Science and Engineering Daffodil Institute of IT Dhaka Bangladesh Dept. of Computer Science and Engineering World University of Bangladesh Dhaka Bangladesh Dept. of Computer Science American International University- Bangladesh Dhaka Bangladesh Dept. of Electrical and Computer Engineering North South University Dhaka Bangladesh Dept. of Computer Science and Engineering Army Institute of Business Administration (AIBA) Savar Dhaka Bangladesh

Qualitative approach for automated grading and quality assessment of fruits, machine learning techniques are crucial in agricultural applications. Automation enhances a nation’s agricultural quality, production, and economic prosperity. Fruit quality grading, particularly the surface fault identification of a fruit, is a crucial indicator in the export market. This is particularly important for mangoes, which are quite well-liked inBangladesh. On the other hand, the physical grading of mangoes is a procedure that is labor-intensive, prone to error, and very subjective. In this paper, we proposed a YOLOv7 integrated Discrete wave transformation computer vision system. The proposed model includes support vector machine (SVM) and decision tree for the classification of high-quality mangoes. The results of the experiments show that the proposed solution obtained 96.25% accuracy when the system was trained and tested using a publicly accessible mango database.

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Challenges in Integrating Data Analytics in Small Businesses

Challenges in Integrating Data Analytics in Small Businesses

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Priyanka Kaushik S. Mahalakshmi Sangeeta Singh Ankur Kumar Agrawal Sadaf Nazli Sneha G Dept. of Computer Sci. Engineering Chandigarh University Chandigarh India Dept. of CSE Madhav University Pindwara India Dept. of Buss Management & Commerce Aligarh India Dept. Of Management J.S. University Shikohabad India Dept. of Business Administration Dhanalakshmi Srinivasan College Coimbatore India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

For small businesses, the integration of data analytics is fraught with a variety of obstacles that prevent these organizations from capitalizing on decision making support tools powered by their data. In this abstract, this will examine the major challenges these small enterprises face: limited financial resources and a lack of technical knowledge or scalable tools suited for their scale. Initial Costs: As an SMB, the worst thing that you can do is spend your entire tech budget on analytics software because oftentimes even when you think like a giant but act like small business Analytics Software companies have prohibitively high costs. Furthermore, the integration itself may be impeded by legacy technology infrastructure and staff entrenched in traditional modes of working who are unwilling to embrace new methods. In contrast, overcoming these obstacles by adopting data analytics has sizable revenue and competitive capabilities Solving these problems with budget solutions, adequate training and clear strategies will allow small businesses to exploit the potential of data analytics. This paper aims to establish a ground comprehension on these issues and necessitates the need for agile, mobile analytics solutions in small enterprises.

关键词： Training Data analysis Costs Decision making Companies Software Faces

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A Proposed Model for Machine Learning-Based Early Chronic Kidney Disease Detection

A Proposed Model for Machine Learning-Based Early Chronic Ki...

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Advancement in Technology (ICONAT), International Conference for

作者： Sonam Bhandurge Kuldeep Sambrekar Shridhar Allagi Vijay S. Rajpurohit Sanjeev Sannakki Dept. Computer Science and Engineering Angadi Institute of Technology and Management (Affiliated to Visvesvaraya Technological University Belagavi) Belagavi Karnataka India Dept. Computer Science and Engineering Gogte Institute of Technology(Affiliated to Visvesvaraya Technological University Belagavi) Belagavi Karnataka India Dept. Computer Science and Engineering Kle Institute of Technology Hubli ((Affiliated to Visvesvaraya Technological University Belagavi)

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

Chronic kidney disease (CKD) is a sluggish and cumulative decline of kidney characteristics over the years. Early detection of CKD is essential when you consider that delayed detection and hospital treatment of the situation might eventually bring about the final stage of CKD and the patient’s death. The prediction of diseases has grown to be appreciably impacted by means of the utilization of machine learning (ML) in healthcare. Thus, ML methods are the simplest in CKD prediction. The major goal is to use distinct supervised ML techniques, such as Support Vector Machine, Random Forest, Artificial Neural Network, and Logistic Regression, to examine and verify how correct they may be at detecting CKD. The proposed model provides various strategies for detecting CKD using medical facts which incorporates data preprocessing, strategies for handling missing values, and data reduction. It is noted that the clinical data has been classified with an overall accuracy of $100 \%$ by the Random Forest algorithm. The suggested ML model will help doctors identify chronic kidney ailments early on, which will be an effective manner of preventing the illness.

关键词： Support vector machines Accuracy Predictive models Feature extraction Prediction algorithms Chronic kidney disease Vectors Random forests Monitoring Diseases

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Rethinking Machine Ethics – Can LLMs Perform Moral Reasoning through the Lens of Moral Theories?

arXiv

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arXiv 2023年

作者： Zhou, Jingyan Hu, Minda Li, Junan Zhang, Xiaoying Wu, Xixin King, Irwin Meng, Helen Dept. of Systems Engineering & Engineering Management The Chinese University of Hong Kong Hong Kong Dept. of Computer Science & Engineering The Chinese University of Hong Kong Hong Kong

Making moral judgments is an essential step toward developing ethical AI systems. Prevalent approaches are mostly implemented in a bottom-up manner, which uses a large set of annotated data to train models based on crowd-sourced opinions about morality. These approaches have been criticized for overgeneralizing the moral stances of a limited group of annotators and lacking explainability. This work proposes a flexible top-down framework to steer (Large) Language Models (LMs) to perform moral reasoning with well-established moral theories from interdisciplinary research. The theory-guided top-down framework can incorporate various moral theories. Our experiments demonstrate the effectiveness of the proposed framework on datasets derived from moral theories. Furthermore, we show the alignment between different moral theories and existing morality datasets. Our analysis exhibits the potential and flaws in existing resources (models and datasets) in developing explainable moral judgment-making systems. © 2023, CC BY-NC-ND.

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Real-time Detection of Diverse Bangladeshi Traffic Signs Using YOLOv8

Real-time Detection of Diverse Bangladeshi Traffic Signs Usi...

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Future Machine Learning and Data Science (FMLDS), IEEE International Conference on

作者： Md. Habib Ehsanu Hoque Mrittika Mahbub Mohd Razali Bin Md Tomari Rezaul Bashar Md. Golam Rashed Dipankar Das Dept. of Information and Communication Engineering University of Rajshahi Bangladesh Dept. of Computer Science and Engineering Pundra University of Science and Technology Bangladesh Dept. of Electronic Engineering University Tun Hussein Onn Malaysia (UTHM) Malaysia Science Technology and Management Crescent Sydney Australia

ISBN: (数字)9798350391213

ISBN: (纸本)9798350391220

The need for intelligent traffic management solutions is crucial in the rapidly evolving fields of autonomous traffic control and navigation systems, particularly for minimizing traffic accidents and ensuring the efficient operation of traffic signals. Improving traffic safety is of paramount importance, as road accidents claim over a million lives annually worldwide, with a significant proportion of these fatalities occurring in developing countries like Bangladesh. This research aims to develop a smart assistant capable of identifying and categorizing traffic signs to enhance road safety, especially in areas where drivers may not be fully aware of traffic regulations. To achieve this, we generated a comprehensive dataset containing 5,540 distinct images of traffic signs collected from various regions across Bangladesh, including highways, rural and urban roads, and challenging locations such as steep curves. The dataset comprises 41 categories of traffic signs. The dataset includes challenges such as color variations, blurriness, occlusion, small sign sizes, and low-light conditions. To further enhance the dataset's diversity, data augmentation techniques such as random rotations, shearing, and zooming were applied. We trained the YOLOv8 deep learning model, renowned for its real-time object detection capabilities, on this dataset. The model achieved impressive results, with a mean Average Precision (mAP) of 0.80, a recall of 0.87, a precision of 0.92, and an F1 score of 0.89, demonstrating its effectiveness in real-world traffic sign detection and classification.

关键词： Training Road accidents Virtual assistants Traffic control Streaming media Real-time systems Robustness Road safety Shearing Vehicles

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