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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Gopalakrushna, A. Raj, Vijilius Helena Vani, V. Divya Singh, Navdeep Jethabhai, Patel Yogeshkumar Chakravarthi, M. Kalyan Vignana Bharathi Institute of technology Telangana Hyderabad India New Horizon College of Engineering Department of Applied Sciences Karnataka Bangalore India Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Sankalchand Patel University Faculty of Computer Science Shri C.J. Patel College of Computer Studies Gujarat Visnagar India University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman

ISBN: (纸本)9798350350067

Reliable power system operations, cost reduction, demand and supply balance maintenance, and efficient load scheduling all depend on real-time energy optimisation. Nevertheless, the unpredictability of electric loads and renewable energy sources (RES) makes real-time energy optimization difficult. As a result, the Energy Management Controller (EMC) was developed, which can automatically react to DR signals and resolve energy-related issues. In light of this, EDGE, a hybrid algorithm combining elements of genetic algorithm (GA) and enhanced differential evolution (EDE), is devised in this work. The EDGE algorithm is used to train the EMC to automatically react to DR signals and schedule the 3 classes of household loads - hybrid, non-interruptible, and interruptible - to handle energy management issues. The results show that in terms of power cost reduction, minimizing carbon release, PAR relief, and client discomfort maximization, the suggested EDGE algorithm performs better than benchmark methods. © 2024 IEEE.

关键词： energy optimization renewable energy Smart grid

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Procedural Modeling Method for Automatic Generation of Multi-Layout Virtual Content with Existing Elements

Procedural Modeling Method for Automatic Generation of Multi...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Emir Cogo Ehlimana Cogo Damir Pozderac Selma Rizvić Department of Computer Science and Informatics Faculty of Electrical Engineering University of Sarajevo Sarajevo Bosnia and Herzegovina

Procedural modeling is used to generate virtual content in organized layouts of exterior and interior elements. There is a large number of existing layout generation methods, and newer approaches propose the generation of multiple layout types within the same generation session. This introduces additional constraints when manually created layout elements need to be combined with the automatically generated content. Existing approaches are either designed to work with existing elements for a single layout type, or require a high amount of manual work for adding existing elements within multiple layouts. This paper presents a method that enables the application of existing subdivision methods on multiple layout types by inserting existing content into the generation result. This method can generate test cases by creating variations of partially generated layouts for procedural modeling methods that can work with existing content.

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An Empirical Investigation in Applying Reliable Industry 4.0 Based Machine Learning (ML) Approaches in Analysing and Monitoring Smart Meters using Multivariate Analysis of Variance (Manova) 2

An Empirical Investigation in Applying Reliable Industry 4.0...

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2nd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2022

作者： Rana, Md. Sohel Dixit, Anil Kumar Rajan, M.Sundar Malhotra, Sumit Radhika, S. Pant, Bhasker Nanjing University of Information and Science Technology Computer Science and Technology India Uttaranchal University Dehradun Department of Lcd India Arbaminch University Faculty of Electrical and Computer Engineering Ethiopia Chandigarh University Computer Science and Engineering Gharuan Mohali India Raghu Institute of Technology Department of CSE AndhraPradesh Visakhapatnam India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9781665437899

Conventional power systems have been evolving towards prospective smart grids in recent decades as market needs have changed and independent innovations, such as the 'Internet of Things' (IoT), have developed. The further automation of traditional development of industrial operations with a variety of current advanced techniques is termed as Industry 4.0. The most efficient strategy to boost the productivity of the enterprise environment, such as smart grids, is to use contemporary control technology that regulates and supervises interaction between smart devices. Furthermore, the advancement of loT -enabled smart meters is aided by a separate data connection, such as 'Bluetooth' or the 'Global System for Mobiles' (GSM), for remotely inspecting and operating smart meters. In this context, SPSS software is used for analysing the role of Industry 4.0 based Machine Learning or ML in analysing Smart Meters and MANOV A (and correlation) approach is considered to get proper result. This study proposes a machine learning-based architecture for analysing and monitoring smart meter data. The advantages of loT integration into Industry 4.0 include the ability to exchange actual information across a wide range of smart meters for various applications, such as factories, hospitals and residences, which is critical for smart grids. Results suggested that Smart Grid is beneficial to the extent of reducing electricity cost;however, maintenance cost and security issues are the major challenging factors in the new technology. © 2022 IEEE.

关键词： Internet of things

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Attention-Guided CNN with LSTM for Temporal Gastrointestinal Disease Progression Prediction in Endoscopic Video Sequences

Attention-Guided CNN with LSTM for Temporal Gastrointestinal...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Fayazoddin Mohamad Raghu Dhumpati R. Reenadevi P. S. V. Srinivasa Rao Kamal Sutaria Shirish Kulkarni RITE DRUGS LLC (DBA - K&G Pharmacy) NY USA Department of Computer Science & Engineering Bahrain Polytechnic Bahrain City Bahrain Department of Computer Science and Engineering (CSE) Sona College of Technology Salem India Department of Computer Science and Engineering Joginpally B R Engineering College Hyderabad India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Post Limda India Faculty of Law Symbiosis Law School Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

The detection and tracking of changes in the progression of GI disease using endoscopic video analysis remains difficult due to temporal changes and image complexity. Proper models of prediction are critical in diagnosing individuals at an early stage and offering them treatment. In the work presented in this paper, we introduce novel Attention-Guided Convolutional Neural Network (AG-CNN) combined with LSTM networks to analyze temporal endoscopic video sequences. It uses spatial attention to catch necessary areas and temporal attention to capture assertive illness transformation in video frames. For disease progression prediction the proposed model yield 94.8% accuracy with 93.2% sensitivity and 95.6% specificity. The attention mechanism increased the feature extraction speed by 22 percent when compared to traditional CNN-LSTM classes. Independent validation checks confirmed that the two models produced a prediction concordance of 92.7% based on different patient samples. The overall performance of temporal GI disease progression prediction by the AG-CNN-LSTM framework shows potential usage in clinical diagnosis by providing automated disease progression monitoring and treatment planning.

关键词： Wireless communication Accuracy Computational modeling Video sequences Predictive models Feature extraction Gastrointestinal tract Convolutional neural networks Long short term memory Diseases

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No-Reference Image Quality Assessment Using Meta-Learning 3rd

No-Reference Image Quality Assessment Using Meta-Learning

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The 3rd International Conference on Frontiers in Computing and Systems (COMSYS 2022)

作者： Dey, Ratnadeep Bhattacharjee, Debotosh Kejcar, Ondrej Department of Computer Science and Engineering Jadavpur University Kolkata India Center for Basic and Applied Science Faculty of Informatics and Management University of Hradec Kralove Rokitanskeho 62 Hradec Kralove500 03 Czech Republic

ISBN: (纸本)9789819926794

Deep learning-based no-reference image quality assessment faces problems like dependency on a large amount of experimental data and the generalization ability of the learned model. A deep learning model trained on a specific dataset cannot obtain the desired results for testing on other datasets. Similarly, a deep learning model trained with small experiment data does not provide the best result. This paper addresses these problems of the deep learning model using the meta-learning approach in the field of no-reference Image Quality Assessment. The no-reference image quality assessment is a small sample problem, where the amount of experimental data is very less. Although data augmentation techniques have been used to increase the amount of experimental data, they do not increase the variation of the data. Therefore, traditional deep learning-based techniques are unsuitable for no-reference image quality assessment. Another problem is the lack of generalization ability. A deep learning model trained with image quality datasets containing images distorted synthetically cannot efficiently assess the quality of images distorted naturally. This work proposes a meta-learning model that can be trained with more than one image quality dataset, where one image quality assessment dataset contains synthetic images and the other contains real images. Finally, another image quality assessment dataset has tested the trained model. The test result is better than the state-of-the-art methods, and the results establish the fact that the meta-learning model proposed in this paper tries to resolve the problems of the deep learning model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image quality

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Probabilistic Constellation Shaping for OFDM-Based ISAC Signaling

Probabilistic Constellation Shaping for OFDM-Based ISAC Sign...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Du, Zhen Liu, Fan Xiong, Yifeng Han, Tony Xiao Yuan, Weijie Cui, Yuanhao Yao, Changhua Eldar, Yonina C. School of Electronic and Information Engineering Nanjing University of Information Science & Technology Nanjing China Southern University of Science and Technology Department of Electronic and Electrical Engineering Shenzhen China School of Information and Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Huawei Technologies Ltd. Shenzhen China Weizmann Institute of Science Faculty of Mathematics and Computer Science Rehovot Israel

ISBN: (纸本)9798350370218

Integrated Sensing and Communications (ISAC) has garnered significant attention as a promising technology for the upcoming sixth-generation wireless communication systems (6G). In pursuit of this goal, a common strategy is that a unified waveform, such as Orthogonal Frequency Division Multiplexing (OFDM), should serve dual-functional roles by enabling simultaneous sensing and communications (S&C) operations. However, the sensing performance of an OFDM communication signal is substantially affected by the randomness of the data symbols mapped from bit streams. Therefore, achieving a balance between preserving communication capability (i.e., the randomness) while improving sensing performance remains a challenging task. To cope with this issue, in this paper we analyze the ambiguity function of the OFDM communication signal modulated by random data. Subsequently, a probabilistic constellation shaping (PCS) method is proposed to devise the probability distributions of constellation points, which is able to strike a scalable S&C tradeoff of the random transmitted signal. Finally, the superiority of the proposed PCS method over conventional uniformly distributed constellations is validated through numerical simulations. © 2023 IEEE.

关键词： ambiguity function ISAC OFDM PCS

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Intelligent Tracing and Process Improvement of Pathology Workflows using Character Recognition 3

Intelligent Tracing and Process Improvement of Pathology Wor...

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3rd International Health Data Workshop, HEDA 2023

作者： Hatlem, Markus Rabbi, Fazle Stünkel, Patrick Leh, Friedemann Faculty of Social Sciences University of Bergen Norway Department of Pathology Haukeland University Hospital Norway Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Norway

A pathology laboratory processes various types of tissue and cell specimens and plays a vital role in the diagnostic process. However, pathology departments are currently facing a significant challenge due to the steady increase in incoming specimens. Increasing the workforce to match the influx is generally not feasible, so Information and Communication Technology (ICT) is seen as a potential solution. One area where ICT can be applied is in process monitoring and tracing. The increase in incoming specimens has caused queues within the laboratory, resulting in more time spent locating and retrieving individual specimens. Existing methods of tracing specimens, such as barcodes or alphabetic sorting, also require manual labor, adding further overhead. In this paper, we propose a lightweight application of optical character recognition (OCR) for specimen tracing, as part of a larger research project to optimize pathology processes at a large regional hospital in Bergen. We present a specific solution that integrates into a general process monitoring environment, and we compare different implementation techniques, particularly edge detection and neural networks. Our preliminary results indicate that this implementation can achieve an accuracy of up to 93.41%, increase sorting speed up to 54% and save up to 35% of time spent in manual sorting activities. We conclude our findings with a general discussion and outlook onto other areas where this solution could theoretically be applied. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Process control

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Ecosystem of Technologies for Smart Agriculture to Improve the Efficiency and Profitability of Indian Farmers 10

Ecosystem of Technologies for Smart Agriculture to Improve t...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Sugandh, Urvashi Nigam, Swati Khari, Manju Banasthali Vidyapith Faculty of Mathematics and Computing Department of Computer Science Banasthali India School of Computing Science Engineering Galgotias University Uttar Pradesh Greater Noida India School of Computer and System Sciences Jawaharlal Nehru University New Delhi India

ISBN: (纸本)9789380544472

An increasing population demands more food, but the available resources are shrinking. The agriculture sector in India is facing several challenges such as climate change, water scarcity, soil degradation, and declining farm profitability. To address these challenges, there is a growing need for the adoption of smart agriculture practices that leverage technology. This paper presents an overview of the ecosystem of technologies for smart agriculture and their potential to improve the efficiency and profitability of Indian farmers. The paper explores the role of various technologies such as precision agriculture, IoT, artificial intelligence, drones, and robotics in improving crop yields, reducing input costs, and enhancing overall farm management. Additionally, the paper discusses the challenges that need to be overcome to ensure the successful adoption of smart agriculture technologies in India. These include the lack of awareness and access to technology, inadequate infrastructure, and limited availability of financing. The paper concludes that the ecosystem of technologies for smart agriculture holds significant promise for Indian farmers to overcome the challenges and improve the efficiency and profitability of their farms. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Profitability

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Decision Tree based Smart System for Pregnant Women Diagnosis

Decision Tree based Smart System for Pregnant Women Diagnosi...

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2022 International Conference on Intelligent Technology, System and Service for Internet of Everything, ITSS-IoE 2022

作者： Al-Hagree, Salah Alsayadi, Hamzah A. Alsurori, Muneer Mohammed, Marwan Mohsen, Ayedh Abdulaziz Albazel, Mohammad Gawbah, Hezam Alndary, Ebtisam Ibb University Faculty of Sciences Computer Science Department Yemen Alqalam University Faculty of Information Technology and Engineering Information Technology Department Yemen University of Science and Technology Information Technology Department Yemen

ISBN: (数字)9781665459983

ISBN: (纸本)9781665459983

one of the reasons of women and newborns death in developing countries is the complications during pregnancy and childbirth. An early diagnosis of pregnancy problems would help to alleviate some of these risks. In this work, a suggested solution to help the physicians for fast pregnant diagnosis is proposed. A smart system is designed for easing women diagnosis during pregnancy. The core purpose of this research work is to build up an Android application which gives a deliberate approach in providing the best diagnosis of pregnancy so that women will not be caught in the is the complications during pregnancy. Android application uses Machine Learning algorithms such as decision tree algorithm. In this paper, decision tree algorithm (DT) is used for predicting pregnancy states, which performs better and more robust than the other models. The best diagnosis of pregnancy is predicted using C4.5 and CART algorithms. Proposed system had used decision tree algorithms that employed a database of the most diseases associated with women during pregnancy. Pregnant women is interacted with proposed system via an android application mobile interface, they had provided the algorithm with the case conditions, which processed to guide both physician and pregnant women on the level of risk. Response of the system is based on input data that processed according to predetermined variables and decision tree outcomes. Implemented experiments had proofed the efficiency of the system to facilitate diagnosis for better pregnant mothers' health care. The CART algorithm has gained a better level of evaluation comparing to other C4.5 algorithm. CART algorithm has achieved the highest correctly classified instances and incorrectly classified instances, which were 89.65% in correctly classified instances, 90.77 % in incorrectly classified instances, 9.23% evaluation measure. © 2022 IEEE.

关键词： Decision trees

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Underwater small object detection via automatic encoding pyramid with neighborhood information

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Journal of Ocean engineering and Marine Energy 2025年 1-14页

作者： Zhang, Dan Zhang, Jiafu Wei, Tianyu He, Zongxin Lin, Zifan School of Computer Science and Engineering Huizhou University Hui Zhou China School of Electronics and Information Engineering Liaoning Technical University Huludao China Faculty of Innovation Engineering Macau University of Science and Technology Macau China Department of Electrical and Electronic Engineering University of Western Australia Perth Australia

With the rapid advancement of Internet of Things (IoT) technology, underwater sensor networks have shown significant potential in marine resource exploration, environmental monitoring, and related fields. However, detecting small objects in underwater images remains challenging due to complex conditions and the diminutive size of targets. The sparse features of small objects, combined with the pyramid structure of pooling and down-sampling, lead to the gradual loss of pixel-level features during propagation, thereby limiting detection performance. Moreover, the pyramid-style feature extraction network introduces information conflicts between features of different scales when integrating multi-density information, further degrading the detection accuracy of small objects in underwater images. To address these issues, we propose a novel feature pyramid network, termed AENI-USOD, which leverages neighborhood information and generates conflict suppression information to enhance the network’s focus on underwater objects. Specifically, we design a Neighborhood Information Fusion Module (NIFM) that employs multi-scale dilated convolutions to fuse features and generate neighborhood information for more precise object detection. To mitigate information conflicts during multi-scale feature fusion, we introduce a Conflict Suppression Module (CSM) that filters out conflicting interference, preventing small objects from being obscured by redundant information. Additionally, we implement the NMS-Circle post-processing method, which utilizes a circular feature attenuation function to reduce reliance on threshold parameters in soft non-maximum suppression. Our method is extensively evaluated on the UDD, RUOD, and URPC2019 datasets, demonstrating superior accuracy compared to state-of-the-art methods. This work provides robust technical support for intelligent object recognition within underwater IoT systems.

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