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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Wieckowski, Jakub Gajewski, Pawel Swaldek, Krzysztof Salabun, Wojciech National Institute of Telecommunications ul. Szachowa 1 Warsaw04-894 Poland Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Zolnierska 49 Szczecin71-210 Poland Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland

This paper provides a comparative analysis of three selected Multi-Criteria Decision Analysis (MCDA) methods in a practical decision problem to examine the robustness of the obtained results. The Complex Proportional Assessment (COPRAS), Preference Ranking Organization METHod for Enrichment of Evaluations (PROMEHTEE) II, and Evaluation based on Distance from Average Solution (EDAS) methods were utilized for the evaluation purposes, while the Ranking Comparison (RANCOM) method was applied to determine criteria weights. The application was directed toward sustainable development management in selected European countries considering different energy sources in the consumption share per capita. The obtained results showed, that depending on the used MCDA method, the ranking of countries differed, thus showing that the evaluation process is sensitive to changing assessment methods. © 2024 The Authors.

关键词： Sustainable development goals

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Empowering Breast Cancer Detection with AI: A Modified Support Vector Machine Approach for Improved Classification Accuracy 4

Empowering Breast Cancer Detection with AI: A Modified Suppo...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Palarimath, Suresh Maran, Pyingkodi Thenmozhi, K. Shiny, K.V. Sujatha, T. N.r, Wilfred Blessing College of Computing and Information Sciences University of Technology and Applied Information Technology Department Sciences Salalah Oman Kongu Engineering College Department of Computer Applications Erode India Department of Compute Science Bengaluru India Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India Sri Krishna College of Engineering and Technology Department of Artificial Intelligence and Data Science Coimbatore India College of Computing and Information Sciences Univesity of Technology and Applied Sciences Information Technology Department Ibri Oman

ISBN: (纸本)9798350385793

Breast cancer poses a significant threat to women's health, being a leading cause of cancer-related mortality among female population. In recent years, machine learning has emerged as a promising approach in medical field, particularly in detection and classification tasks. However, existing algorithms often exhibit suboptimal accuracy, necessitating improved methodologies. This research presents a novel approach using a Modified Support Vector Machine (MSVM) for breast cancer classification into benign and malignant categories. Leveraging the Wisconsin Breast Cancer Dataset (WBCD), preprocessing techniques are applied to enhance data quality. Principle Component Analysis (PCA) reduces dimensionality, while linear Discriminant Analysis (LDA) extracts discriminative features crucial for classification. The proposed MSVM classifier achieves exceptional performance, with a classification accuracy of 99.42%, outperforming other existing methods such as Deep Convolutional Neural Networks and Fuzzy Rule-based Systems. These results highlight the efficacy of the MSVM approach in accurately distinguishing between benign and malignant breast cancer cases, showcasing its potential as a reliable tool for medical image analysis and cancer diagnosis. © 2024 IEEE.

关键词： Convolutional neural networks

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Drug Consumption Detection by Eye Using Image Processing Techniques

Drug Consumption Detection by Eye Using Image Processing Tec...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Pyingkodi, M. Thenmozhi, K. Wilfred Blessing, N.R. Archana, K. Arun Hiruthik, X. Dinesh Kumar, V. Kongu Engineering College Department of Computer Applications Erode India Kristu Jayanti College Department of Computer Science Bengaluru India University of Technology and Applied Sciences-Ibri Department of IT Oman Kongu Engineering College Computer Applications Erode India

ISBN: (纸本)9798350348217

Drug usage is a significant global economic issue that results in countless losses. This study suggests a convolutional neural network-based image processing method for identifying drugged eyes. The front-end packages already in use for this article collect information from eye images using the CNN algorithm. However, it can take some time. As a result, the suggested approach may be used to swiftly and automatically identify drugged eyes. The acquisition of the input image, picture pre-processing, and image processing are the main processes in the suggested *** the reddish regions, emphasising the afflicted regions, confirming the training set, and presenting the findings. Some eyes may go unnoticed, such those that have been socially poisoned. This technique was tried on drugged eyes at different phases, both with and without drug consumption. The technique was used to find a white region of the eye in a given input picture. Images of both drugged and unaffected eyes were presented for training purposes. In order to choose the optimum colour model for this strategy, photos were first transformed to colour models. The Support Erosion technique and the Local Binary Pattern were both used in the model construction for feature extraction. This technique can detect drugged eyes with an average accuracy of 95%. © 2023 IEEE.

关键词： Local binary pattern

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A generative adversarial network-based data augmentation approach with transient vibration data 27

A generative adversarial network-based data augmentation app...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Koenig, Timo Cadau, Luca Wagner, Fabian Kley, Markus Institute for Drive Technology University of Applied Sciences Aalen Aalen73430 Germany Faculty for Electronics and Computer Science University of Applied Sciences Aalen Aalen73430 Germany

In order to perform fault classification using machine learning algorithms, sufficient and balanced data is required. The paper presents a novel approach for data augmentation using a Generative Adversarial Network (GAN) with transient, time-dependent vibration data. The proposed approach aims to generate synthetic data in form of spectrograms that closely resemble the characteristics of the real data sets. Synthetic data generation can be used to improve the training performance of neural networks for vibration analysis if not enough or unbalanced real data is available. The authors demonstrate the effectiveness of this approach by training a vibration-based fault detection model using synthetic data and comparing its performance to a model trained on real data only as well as with a t-distributed stochastic neighbour embedding (t-SNE). Real data is acquired on a bearing test rig and measurements are carried out on bearings with four different system states. The results show that the model trained with the synthetic data set outperforms the model trained with real data only, indicating that the synthetic data generated by the proposed approach can improve the training performance and accuracy of the machine learning model. Overall, the paper highlights the potential of GAN-based data augmentation approach via spectrograms for vibration analysis and offers insights into its practical application to bearings. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Bearing Condition Monitoring Data Generation Generative Adversarial Network (GAN) Spectrogram

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Traffic Sign Recognition Using an Attentive Context Region-Based Detection Framework

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Chinese Journal of Electronics 2021年第6期30卷 1080-1086页

作者： LIU Zhigang DU Juan TIAN Feng WEN Jiazheng School of Computer and Information Technology Northeast Petroleum University Postdoctoral Workstation on Applied Technology Research Institute Northeast Petroleum University Department of Computer Science for Artificial Intelligence University of Nottingham Ningbo

Accurate small traffic sign recognition is more important for the safety of intelligent transportation systems. A recognition framework named attentive context region-based detection framework(AC-RDF) is proposed in this paper. We construct the attentive context feature for the recognition of small traffic signs,which combines the target information and the contextual information by the concatenation operation following a pointwise convolutional layer. The proposed attentive context feature exploits the surrounding information for a given object proposal. Next, we propose a novel attentive loss function to replace the original crossentropy function. It distinguishes hard negative samples from easy positive ones in the total loss, allows the proposed framework to obtain enough training, and further improve the recognition accuracy. The proposed method is evaluated on the challenging Tsinghua-Tencent 100 K dataset. The experimental results indicate that the attentive context region-based detection framework is superior at detecting small traffic signs and achieves stateof-the-art performance compared with other methods.

关键词： attentive context region-based detection framework intelligent transportation systems convolutional neural nets attentive context feature AC-RDF framework attentive loss function traffic signs learning (artificial intelligence) Tsinghua-Tencent 100K dataset object recognition feature extraction traffic sign recognition object detection entropy recognition framework

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The Impact of the Work Environment in the Organization on Job Security for Employees: An applied Study on Al-Rawabi Private School in the Kingdom of Bahrain

The Impact of the Work Environment in the Organization on Jo...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ateeq, Ali Alaghbari, Mohammed Abdulrazzaq Milhem, Marwan Ateeq, Ranyia A. Ahmed Yusuf, Ammar Yusuf College of Administrative and Financial Science Administrative Science Department Gulf University Sanad26489 Bahrain College of Administrative Sciences Department of Business Administration Applied Science University Bahrain College of Information Technology University of Bahrain Bahrain College of Arts and Science Department of Computer Science Applied Science University Bahrain

ISBN: (纸本)9798350369106

The present study investigates the influence of work environment factors - including incentives and rewards, work relations, and working conditions - on job security at Al-Rawabi Private School in Bahrain. A structured questionnaire was employed to survey a sample of 160 employees. SPSS was employed to analyze the data, which included Pearson correlation and regression analysis, in order to ascertain the relationships between variables. The results indicate that job security is most significantly influenced by work relations, incentives and rewards, and working conditions. The critical role of these factors in assuring employee stability is underscored by the model explains 75.5% of the variance in job security. According to the research, it is imperative to improve working conditions, ensure an equitable distribution of rewards, and enhance work relations in order to promote job security. The study provides valuable insights into the literature, particularly in the educational sector, and offers actionable recommendations for improving the work environment to increase employment security in similar institutions. The results' generalizability may be impacted by the focus on a particular institution, which is one of the limitations. © 2024 IEEE.

关键词： Regression analysis

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Detection of Stage of Cancer Using Machine Learning and Deep Learning 5th

Detection of Stage of Cancer Using Machine Learning and Deep...

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5th International Conference on Advances in Information Communication technology and Computing, AICTC 2024

作者： Ashtagi, Rashmi Akkalkot, Afsha Imran Kotecha, Ketan Maginmani, Umme Habiba Patil, Pushpalata Patil, Abhijeet Computer Engineering Department Vishwakarma Institute of Technology Pune India Computer Engineering Department College of Engineering Pune Technological University Pune India Symbiosis Centre for Applied Artificial Intelligence Symbiosis Institute of Technology Symbiosis International University Pune411045 India Computer Science and Engineering SECAB Institute of Engineering and Technology Affiliated to Visvesvaraya Technological University Vijayapur India Computer Science and Engineering Faculty of Engineering and Technology Exclusively for Women Sharnbasva University Kalburagi India RPD PU College of Arts and Commerce Belagavi India

ISBN: (纸本)9789819761029

Melanoma, an aggressive form of skin cancer known for its rapid spread, poses a significant threat and is responsible for a substantial number of fatalities. The accurate classification of cancer stages is crucial for effective diagnosis, particularly during surgical treatment. This paper introduces two methods for categorizing melanoma cancer stages. Initial system distinguishes between stage 1 and stage 2, while the second system extends the classification to include stage 3 melanoma. Leveraging a Convolutional Neural Network (CNN) algorithm, the proposed system incorporates the Similarity Measure for Text Processing (SMTP) as a loss function. The experimental results showcase the efficacy of different loss functions, with a particular focus on the proposed SMTP loss function, which outperforms several others designed for classification problems. This algorithm stands out as a more efficient approach for melanoma stage classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Automating Weed Detection Through Hyper Spectral Image Analysis

Automating Weed Detection Through Hyper Spectral Image Analy...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Bharadwaj, Shambhu Prabhu, A. Solanki, Vipin Teetthanker Mahaveer University Department of Computing Sciences and Information Technology Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Sanskriti University Department of Applied Science Uttar Pradesh Mathura India

ISBN: (纸本)9798350383348

Weed detection is an essential assignment in agricultural settings. Negative environmental results, crop yield loss, and mechanical weeding hard work prices related to weed management have necessitated the improvement of automation solutions to discover and treat weeds. Hyperspectral imaging (HSI) is a promising method that can produce abundant statistics about the spectral residences of flowers. This generation has been used in weed detection packages to classify weeds from crops, and these days deep mastering has been used to provide high accuracy prices in this discipline. In this abstract, we explore the utility of HSI for weed detection. We define current demanding situations that require further research earlier than automatic weed detection structures using HSI grow to be widely available. In particular, the presently available algorithms lack robustness and scalability, and further improvements in gadget-gaining knowledge of algorithms and techniques are wished to conquer those constraints, in addition to advancing the computational capabilities of these structures. Moreover, we discuss the potential of HSI as a weed detection answer in various contexts, including agroforestry and precision farming. In conclusion, we advise that the software of HSI for automatic weed detection has the massive capability to reduce labor fees related to weed manipulation, improve farming performance, and in the end, boom crop yields. Weed detection is a first-rate challenge inside the agricultural enterprise, as guide weed control is costly, time-consuming, and the correct identity of weeds is rigid. Hyper Spectral photo evaluation (HSIA) offers an alternative to guide weed detection, considering the rapid and effective mapping of weed-infested land without the need for guide labor. HSIA may be used to routinely detect the spectral signature of weed species, allowing for correct identity and brief remedy. This method uses hyperspectral scanners to gather spectral records, whic

关键词： accuracy crops harm measures promising

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Analyzing the function of smart security in the context of the internet of things 3

Analyzing the function of smart security in the context of t...

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3rd International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2023

作者： Naidu, K. Venkatagurunatham Pillai, Anchu Krishnan Rajaprabha, C.E. Sharma R, Rajesh Ul Islam, Atowar Khasimbee, Shaik Dr M.G.R Educational and Research Institute Dept. of Computer Science and Engineering Chennai India Xavier Institute of Engineering Applied Science and Humanities Maharashtra Mumbai India Hindusthan Institute of Technology Department of Computer Science and Engineering Coimbatore India Adama Science and Technology University School of Electrical Engineering and Computing Department of Computer Science and Engineering Nazareth1888 Ethiopia University of Science and Technology Meghalaya Department of Computer Science and Electronics Meghalaya Baridua793101 India Aditya College of Engineering Department of Computer Science and Engineering Andhra Pradesh Surampalem533437 India

ISBN: (纸本)9798350399264

technology is getting better day by day and human beings are using the latest technology possible for their needs and safety. Many devices have been invented that use the internet and with this human safety issues are also taken care of up to a great extent. GPS tracking devices, SHE (Society Harnessing Equipment), AESHS (Advanced Electronic System for Human Safety), ILA Systems are one of the most useful applications and devices that are used for human safety. Human safety and especially the safety of girls have become an emerging and sensitive issue in the present world scenario. This article deals with the role of the Internet of Things in advancing smart security. The issues are taken care of with the help of these upgraded devices that are easily accessible from a remote location with an internet connection. © 2023 IEEE.

关键词： Internet of things

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A Severity Grading Framework for Diabetic Retinopathy Detection using Transfer Learning

A Severity Grading Framework for Diabetic Retinopathy Detect...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Akhtar, Samia Aftab, Shabib Kousar, Shagufta Rehman, Ambreen Ahmad, Munir Saeed, Ali Q Virtual University of Pakistan Department of Computer Science Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan College of Dentistry University of Science and Technology of Fujairah Fujairah United Arab Emirates College of Informatics Korea University Seoul02841 Korea Republic of NCBA&E Lahore54000 Pakistan Computer Centre Northern Technical University Ninevah Iraq

ISBN: (纸本)9798350369106

Diabetic retinopathy is one of the primary reasons of blindness among individuals. It is a threatening visual disorder that affects delicate blood vessels present within the retina. Early identification of this condition is beneficial for preserving vision and managing it effectively. Detecting diabetic retinopathy manually takes great amount of time and is susceptible to errors. This research aims to propose an automated and efficient framework for grading the severity of diabetic retinopathy with higher accuracy using transfer learning techniques. For this purpose, we have used 3 distinct diabetic retinopathy datasets;Messidor-1, Messidor-2 and IDRiD, containing fundus images. Preprocessing techniques including cropping, denoising, CLAHE, and image resizing are applied to each dataset. Data augmentation is used to tackle class imbalance issues in order to avoid biased results. The dataset was split into three subsets for training, validation, and testing, using a split of 70:10:20. Pre-trained model EfficientNetB5 is fine-tuned and hyper-parameters are adjusted. This model is used to train and test each dataset separately. Results show that our model attained test accuracies of 98.43% for Messidor-1, 97.36% for Messidor-2, and 97.67% for the IDRiD dataset. On comparing the best results of this research with previous state of the art techniques, EfficientNetB5 outperformed those existing methods. © 2024 IEEE.

关键词： Transfer learning

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