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5th IEEE Eurasia Conference on IOT, Communication and engineering, ECICE 2023

作者： Nagatomo, Taiki Sato, Toshinori Fukuoka University Department of Electronics Engineering and Computer Science Graduate School of Engineering Fukuoka Japan Fukuoka University Department of Electronics Engineering and Computer Science Faculty of Engineering Fukuoka Japan

ISBN: (纸本)9798350314694

Improving the energy efficiency of battery-powered medical edge devices is a challenge. In this study, we explored electrocardiogram (ECG) feature detection using the Pan-Tompkins algorithm and integrated approximate computing techniques involving addition and multiplication into the process to enhance power efficiency. We adopted eight approximate multipliers from EvoApproxLib along with a Carry-Maskable Adder (CMA). We evaluated the impact of applying approximate computing on feature detection accuracy, as well as its influence on enhancing the power efficiency of the arithmetic circuits. When replacing just one of the arithmetic circuits with its approximate counterpart, the most power-efficient multiplier had dynamic power reduced by 48.3% in multiplications while maintaining a detection accuracy of 99.96%, while the CMA reduced dynamic power reduction by 95.2% while maintaining a detection accuracy 99.4%. When the CMA and approximate multiplication were applied, detection accuracy was maintained at 99.4%. In terms of power efficiency, the multiplier played a predominant role and the overall dynamic power consumption was reduced by 48.7%. © 2023 IEEE.

关键词： approximate computing ECG medical edge devices Pan-Tompkins algorithm signal processing

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An Automated Framework to Detect Emotions from Contextual Corpus

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International Journal of Advanced computer science and Applications 2023年第3期14卷 865-874页

作者： Thallapalli, Ravikumar Narsimha, G. Faculty of Informatics-Department of CSE Osmania University Telangana Hyderabad India Department of Computer Science and Engineering JNTUH University College of Engineering Telangana SultanpurSangareddy India

The emotion extraction or opinion mining is one of the key tasks for any text processing frameworks. In recent times, the use of opinion mining has gained a lot of potential due to the application of the potential customized aspects of the consumer relations and other customized applications. However, the application of sentiment analysis or opinion mining is highly challenging as the accuracy of the sentiment analysis depends on the input text corpus. The input text corpus can be highly fluctuating due to the inclusion of emojis or local language influences and finally the use of a wide variety of the regional languages. A good number of parallel research outcomes have aimed to solve these challenges in the recent time. However, most of the parallel research outcomes have primarily three challenges kept unsolved as firstly, the emojis in the text corpus is mainly removed but not translated into sentiment scores, secondly, the translation of the texts from various regional languages and the translation is mainly true translations rather than the contextual translation. Finally, the use of the dictionaries in the actual translation tasks takes a lot of time to process and must be reduced. Henceforth, in order to solve these challenges, this work proposed a framework to automate the weighted emoji-based sentiment analysis, Unicode based translation process to reduce the time complexity and finally use the collaborative sentiment analysis scores to build the final sentiment models. This work results into nearly 97% accuracy and nearly 50% reduction in the time complexity © 2023, International Journal of Advanced computer science and *** Rights Reserved.

关键词： Sentiment analysis

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Intelligent Control of Air Combat Maneuvers Using Fuzzy Logic 11

Intelligent Control of Air Combat Maneuvers Using Fuzzy Logi...

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11th International Conference on Mechatronics and Robotics engineering, ICMRE 2025

作者： Deif, Mohanad A. Hafez, Mohamed A. Attar, Hani Rezeka, S.F. Awad, T. Attia, E.M. Badawi, M.B. Research Institute of Sciences and Engineering University of Sharjah United Arab Emirates INTI-IU Universi Faculty of Engineering Feqs Nilai Malaysia Zarqua University Department of Energy Engineering Zarqa Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia Alexandria University Faculty of Engineering Mechanical Engineering Department Alexandria Egypt Alexandria University Faculty of Engineering Mechanical Engineering Department El-Chatby Alexandria21544 Egypt Faculty of Computer Science and Engineering Department of Computer Science South Sinai Governorate El Tor Egypt

ISBN: (纸本)9798331509293

With the expanding capabilities of fighter aircraft in gauging their targets with great accuracy, comes the necessity that fighter pilots possess higher talents in making the correct decision. Because human error is frequently a barrier to making precise and timely decisions, a control mechanism is required to regulate the trajectory of aircraft during combat maneuvers. In this paper, a combat-maneuver fuzzy program is developed and applied to the inverse kinematics of the F-16 aircraft. The program produces a new path for the plane to achieve the maneuver such that the minimum distance between the resulting path and the object aircraft path is larger than a specified distance (critical distance). Also, spatial defensive maneuvers against spatial offensive maneuvers of bandits were realized, accordingly, this research emphasizes the importance of process innovation in the design of adaptive air combat control systems, enabling more effective response strategies. © 2025 IEEE.

关键词： Adaptive control systems

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Neutrosophic Price Indices with an Applied Study on Pesticide Prices

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Neutrosophic Sets and Systems 2023年 58卷 603-610页

作者： Alhabib, Rafif Alhabib, Amani Salama, A.A. Department of Mathematics Faculty of Science Hama University Hama Syria Faculty of Agricultural Engineering Albaath University Homs Syria Department of Mathematics and Computer Science Faculty of Science Port Said University Port Said Egypt

An index is a tool for comparing a phenomenon or several phenomena dating back to different periods to know the amount of change in the phenomena or the difference between them. For example, we compare the price of a commodity in a particular year with its price in a given year, compare the prices of a group of commodities in one year with their prices in another year, or compare the price of a commodity in two different places. For example, we compare the price of a commodity in one city with its price in another city. This paper presents a study of these Indices from a neutrosophic point of view. Which allows us to study Indices with uncertain or non-welldefined price data. In addition, access to results that determine the extent of the change in prices as accurately as possible, and thus the optimal planning for the next stage and the development of appropriate solutions. An applied example was presented to study the change in the prices of pesticides used to combat the "vine stem borer" insect using the neutrosophic Indices. © (2023). All Rights Reserved.

关键词： Pesticides

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Adaptive Boosting Method for Mitigating Ethnicity and Age Group Unfairness

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SN computer science 2024年第1期5卷 10页

作者： Colakovic, Ivona Karakatič, Sašo Faculty of Electrical Engineering and Computer Science University of Maribor Koroška cesta 46 Maribor 2000 Slovenia

Machine learning algorithms make decisions in various fields, thus influencing people’s lives. However, despite their good quality, they can be unfair to certain demographic groups, perpetuating socially induced biases. Therefore, this paper deals with a common unfairness problem, unequal quality of service, that appears in classification when age and ethnicity groups are used. To tackle this issue, we propose an adaptive boosting algorithm that aims to mitigate the existing unfairness in data. The proposed method is based on the AdaBoost algorithm but incorporates fairness in the calculation of the instance’s weight with the goal of making the prediction as good as possible for all ages and ethnicities. The results show that the proposed method increases the fairness of age and ethnicity groups while maintaining good overall quality compared to traditional classification algorithms. The proposed method achieves the best accuracy in almost every sensitive feature group. Based on the extensive analysis of the results, we found that when it comes to ethnicity, interestingly, White people are likely to be incorrectly classified as not being heroin users, whereas other groups are likely to be incorrectly classified as heroin users. © 2023, The Author(s).

关键词： Boosting Classification Fairness Machine learning

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A Survey on the Role of Complex Networks in IoT and Brain Communication

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computers, Materials & Continua 2023年第9期76卷 2573-2595页

作者： Vijey Thayananthan Aiiad Albeshri Hassan A.Alamri Muhammad Bilal Qureshi Muhammad Shuaib Qureshi Cybersecurity Department Faculty of ComputingEngineering and ScienceUniversity of SouthWalesWalesUK Department of Computer Science Faculty of Computing and Information Technology(FCIT)King Abdulaziz UniversityJeddah21589Saudi Arabia Computer Science Department Umm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Science&IT University of Lakki MarwatLakki Marwat28420KPKPakistan Department of Computer Engineering Chungnam National UniversityDaejeon34134Republic of Korea

Complex networks on the Internet of Things(IoT)and brain communication are the main focus of this *** benefits of complex networks may be applicable in the future research directions of 6G,photonic,IoT,brain,etc.,communication *** data traffic,huge capacity,minimal level of dynamic latency,*** some of the future requirements in 5G+and 6G communication *** emerging communication,technologies such as 5G+/6G-based photonic sensor communication and complex networks play an important role in improving future requirements of IoT and brain *** this paper,the state of the complex system considered as a complex network(the connection between the brain cells,neurons,etc.)needs measurement for analyzing the functions of the neurons during brain ***,we measure the state of the complex system through *** 5G+/6G-based photonic sensor nodes,finding observability influenced by the concept of contraction provides the stability of *** IoT or any sensors fail to measure the state of the connectivity in the 5G+or 6G communication due to external noise and attacks,some information about the sensor nodes during the communication will be ***,neurons considered sing the complex networks concept neuron sensors in the brain lose communication and ***,affected sensor nodes in a contraction are equivalent to compensate for maintaining stability *** this compensation,loss of observability depends on the contraction size which is a key factor for employing a complex *** analyze the observability recovery,we can use a contraction detection algorithm with complex network *** survey paper shows that contraction size will allow us to improve the performance of brain communication,stability of neurons,etc.,through the clustering coefficient considered in the contraction detection *** addition,we discuss the scalability of IoT communication using 5G+/6G

关键词： Complex networks emerging communication IoT based on 6G systems neuroscience photonic technology

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Paddy Leaf Disease Identification on Infrared Images based on CNN with Auto Encoders 2

Paddy Leaf Disease Identification on Infrared Images based o...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Kandukuri, Prabhakar Sowrab, B. Sameer Ramesh, G. Singamaneni, Kranthi Kumar Koneru Lakshmaiah Education Foundation Computer Science and Engineering Guntur India Gitam Deemed to Be University Computer Science and Engineering Visakhapatnam India Gokaraju Rangaraju Institute of Engineering and Technology Computer Science and Engineering Hyderabad India Jain Deemed-to-be University Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350325799

The main food that people in India consume on a daily basis is paddy. According to data, the stress caused by rice illnesses, which reduce yields by 70%, was felt by the paddy farmers. If not controlled within a certain time frame, these plant diseases, which are typically brought on by pests, insects, and pathogens, have a significant negative impact on productivity. This research proposed auto encoders rooting convolutional neural network model to control these paddy diseases, and it has been implemented with various per-trained models like Vgg16, Resnet50, and inceptionV6. This model accurately determines whether a paddy plant is healthy or has one of four different types of paddy diseases. These models were developed, tested using auto encoders and a dataset of thermal pictures that was made available to the public, and they achieved the maximum model accuracy of 90.6%. A 84.8% was attained in the prior iteration without the use of auto encoders. With the use of this model, people can quickly detect paddy illness and assist farmers in raising productivity and yield. © 2023 IEEE.

关键词： Neural network models

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Deep Learning Approaches for Precision Agriculture: Weed and Cotton Crop Classification Using YOLO and Faster RCNN

Deep Learning Approaches for Precision Agriculture: Weed and...

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2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Majeed, Asad Faridoon, Fatima Irfan, Muhammad Rashid, Ahmar Faculty of Computer Science and Engineering GIK Institute of Engineering Sciences and Technology Topi Pakistan Department of Electrical Engineering Sukkur IBA University Sukkur Pakistan

ISBN: (纸本)9798331504465

Weed infestation in cotton fields significantly challenges agricultural productivity by competing for essential nutrients and water resources. This study presents a comprehensive comparative analysis of two deep learning-based object detection models - YOLO and Faster R-CNN - for automated weed detection in cotton fields. We developed and annotated a custom dataset comprising field images captured under various environmental conditions, labelled in both COCO and YOLO formats using Label Studio. Performance evaluation revealed the YOLO model’s superior capabilities, achieving a mAP50 of 0.775, precision scores of 0.859 for cotton and 0.656 for weed detection, and recall rates of 0.849 and 0.543 for cotton and weed respectively on the test dataset. In comparison, Faster R-CNN showed lower performance with AP scores of 0.708 for cotton and 0.269 for weed detection, particularly struggling with objects of varying sizes. The YOLO model maintained consistent performance across both validation and test datasets, with validation metrics showing mAP50 of 0.982 and mAP50-95 of 0.829. These results establish YOLO’s effectiveness as a reliable tool for automated weed detection in cotton fields, offering a practical solution for precision agriculture applications. The developed model demonstrates the potential for integration into automated crop management systems, contributing to more efficient and targeted weed control strategies. © 2024 IEEE.

关键词： Cotton

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In Search for Model-Driven eXplainable Artificial Intelligence 1

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10th IFIP International Workshop on Artificial Intelligence for Knowledge Management, AI4KMES 2023

作者： Ligęza, Antoni Sepiolo, Dominik Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Applied Computer Science AGH University of Krakow al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (数字)9783031610691

ISBN: (纸本)9783031610684

This paper reports on ongoing and innovative research in the area of eXplainable Artificial Intelligence (XAI). A classical XAI task is considered as finding an explanation of the model generated via Machine Learning by identifying the most influential variables for local decision-making. Such an approach suffers from severe limitations. The proposed approach moves the explanatory process to a new, knowledge-level dimension. It is oriented towards Model Discovery, i.e. the internal structure and functions of the components. The concept of Model-Driven XAI is put forward and explained with examples. An experiment on Function Discovery via Grammatical Evolution is reported in brief. © IFIP International Federation for Information Processing 2024.

关键词： Decision making

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Assessing the Performance of PINN and CNN Approaches in Solving the 1D Burgers' Equation with Deep Learning Architectures 4

Assessing the Performance of PINN and CNN Approaches in Solv...

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4th International Conference on Machine Learning and computer Application, ICMLCA 2023

作者： Li, Cheng School of Computer Science and Engineering Faculty of Innovation Engineering Macau University of Science and Technology Macau999078 China

ISBN: (纸本)9798400709449

Deep learning has emerged as a promising approach for solving complex partial differential equations (PDEs) using data-driven methods, particularly in scenarios where traditional numerical techniques face limitations. The 1D Burgers' Equation, which combines convection and diffusion terms, is a fundamental PDE used to model various physical phenomena, including fluid dynamics and shock waves. While analytical solutions for this equation are challenging due to its nonlinearity, specific cases do have exact solutions. This research systematically evaluates the effectiveness of two popular deep learning methodologies, Physics-Informed Neural Networks (PINNs) and Convolutional Neural Networks (CNNs), in solving the 1D Burgers' Equation. A particular focus of the assessment is to explore potential bias in the training dataset. The neural networks are trained using biased samples, and their results are rigorously compared with those obtained from unbiased samples. This investigation provides insights into the performance of these deep learning architectures and their sensitivity to the quality of training data. The research reveals significant sample bias effects on neural network (NN) models. Primitive bias notably impacts Convolutional Neural Networks (CNNs) more than Physics-Informed Neural Networks (PINNs). This is attributed to differences in loss function calculation, making bias selection more straightforward for CNNs. © 2023 Owner/Author.

关键词： Deep learning

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