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10th Workshop on software Quality Analysis, Monitoring, Improvement, and Applications, SQAMIA 2023

作者： Kataieva, Yevheniia Leontiev, Oliver Institute of Informatics Information Systems and Software Engineering Faculty of Informatics and Information Technologies Slovak University of Technology in Bratislava Ilkovičova 2 Bratislava842 16 4 Slovakia

The tourism sector is currently witnessing a significant transformation driven by technological automation. As this evolution unfolds, it is crucial to harness intelligent technologies not only to enhance the industry but also to simplify the experience for tourists themselves. One aspect of simplification that would greatly benefit every tourist exploring a new city is a tool capable of suggesting an optimal sequence for visiting the city's best attractions. This tool would take into account the personal preferences of the tourist as well as the spatial context of the city. In this research, we present findings that are pertinent to the development of such a tool and propose an initial approach to provide these functionalities. Our proposed solution involves applying a relevant variant of the Vehicle Routing Problem with Pickup and Delivery (VRPP) or the Traveling Salesman Problem (TSP) algorithm. By leveraging user-supplied preferences and extracting relevant information from external factors, we aim to assist users in planning their optimal viewing experiences. To realize this solution, we envision creating a modern web application utilizing cutting-edge technologies. The primary focus of this application would be to generate optimal tourist routes on a per-city basis. This means that the tool would prioritize suggesting viewing sequences for tourist attractions within the city where the user is staying or traveling through, as well as attractions in close proximity to the city. Our research aims to contribute to the ongoing wave of technological automation in the tourism sector by developing a tool that simplifies the planning process for tourists. By leveraging advanced algorithms and a modern web application, we strive to provide an optimal viewing sequence of attractions based on individual preferences and the spatial context of the city. © 2023 Copyright for this paper by its authors.

关键词： information and Communication Technology Tourist Trip Design Problem Traveling Salesman Problem Virtual Travel Community

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An Effective Detection of Brain Tumor Detection Using ResNet-50 Model

An Effective Detection of Brain Tumor Detection Using ResNet...

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2024 International Conference on Advancement in Renewable Energy and Intelligent systems, AREIS 2024

作者： Raji, V. Maheswari, S. Dharinya, S. Sree Chinniah, P. Kavin, K.S. Raja, Kiruba Thangam S.K.P. Engineering College Dept. of Information Technology Tiruvannamalai India Tamilnadu Erode India School of Computer Science Engineering and Information Systems Vit University Dept. of Software and Systems Engineering Vellore India ST. Joseph College of Engineering Dept. of Ece Tamilnadu Sriperumbudur India Government College of Engineering Dept. of Eee Tamil Nadu Tirunelveli India Vit University Dept. of Software and Systems Engineering Vellore India

ISBN: (纸本)9798350387230

Rapid proliferation of aberrant neurons in brain tumors significantly damages organ function or causes death, endangering adult health. These tumors vary widely in terms of their locations, sizes, and textures. Finding malignant tumors requires the use of Magnetic Resonance Imaging (MRI). However, physically diagnosing brain tumors is a timeconsuming and potentially unpredictable technique. Cropping and resizing to fixed dimensions ensures consistency before submitting brain MRI images for analysis using deep learning models such as modified ResNet-50. This study uses the modified ResNet-50 model, a deep Convolutional Neural Network (CNN) renowned for its accuracy and resilience in image recognition tasks, to provide an efficient method for brain tumor diagnosis. Initially trained on Image Net, the modified ResNet-50 model further improved using transfer learning on many annotated brain MRI scan datasets. By adding variation to the dataset, data augmentation makes the model more robust and helps it to generalize. Deeper networks are trained without sacrificing performance to the model's architecture, which addresses the vanishing gradient problem. This work classification model implemented using Python software, which facilitated the development, training, and evaluation processes, allowing for an analysis of their performance. The optimized model outperformed conventional Machine Learning (ML) techniques, demonstrating over 94% accuracy in tumor identification and classification tests. With the potential to enhance patient outcomes and diagnostic efficiency, the integration of modified ResNet-50 in brain tumor identification offers a significant development towards accurate and dependable automated diagnostic systems. The accuracy of the modified Resnet-50 is 94%, and the specificity comparison of the modified ResNet-50 is 93.5%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Feed-Forward Probabilistic Error Cancellation with Noisy Recovery Gates 17

Feed-Forward Probabilistic Error Cancellation with Noisy Rec...

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17th IEEE International Symposium on Embedded Multicore/Many-core systems-on-Chip, MCSoC 2024

作者： Kurosawa, Leo Saito, Yoshiyuki Lee, Xinwei Yan, Xinjian Xie, Ningyi Cai, Dongsheng Shin, Jungpil Asai, Nobuyoshi University of Aizu School of Computer Science and Engineering Aizu-Wakamatsu Fukushima Japan Singapore Management University School of Computing and Information Systems Singapore University of Tsukuba School of Science and Technology Tsukuba Japan University of Tsukuba Faculty of Engineering Information and Systems Tsukuba Japan

ISBN: (纸本)9798331530471

Probabilistic Error Cancellation (PEC) aims to improve the accuracy of expectation values for observables. This is accomplished using the probabilistic insertion of recovery gates, which correspond to the inverse of errors. However, the inserted recovery gates also induce errors. Thus, it is difficult to obtain accurate expectation values with PEC since the estimator of PEC has a bias due to noise induced by recovery gates. To address this challenge, we propose an improved version of PEC that considers the noise resulting from gate insertion, called Feed-Forward PEC (FFPEC). FFPEC provides an unbiased estimator of expectation values by cancelling out the noise induced by recovery gates. We demonstrate that FFPEC yields more accurate expectation values compared to the conventional PEC method through analytical evaluations. Numerical experiments are used to evaluate analytical results. © 2024 IEEE.

关键词： Inverse problems

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Yolo Based License Plate Detection of Triple Riders and Violators

Yolo Based License Plate Detection of Triple Riders and Viol...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Nandhakumar, R.G. Devi, K. Nirmala Krishnamoorthy, N. Shanthi, S. Pranesh, V.R. Nikhalyaa, S. Kongu Enginering College Perundurai Department of Computer Science and Engineering Tamilnadu Erode India Vellore Institutte of Technology School of Information and Engineering Department of Software Systems and Engineering Vellore India

ISBN: (纸本)9798350348217

Road safety is of prime importance as road accidents are among the biggest causes of deaths in the country. Road Accidents are majorly due to violators and lawbreakers of road safety rules like not wearing helmets, triple riding etc. Even though there are many smart systems to monitor these violations, it is complicated to keep a track of the data and view it efficiently anywhere. Also monitoring the permit details of other state vehicles is hard. A method was proposed to address this issue. based on Deep learning and Optical Character Recognition (OCR). Here we detect the riders not wearing helmet and triple riders using object detection by comparing YOLOv2 and YOLOv3. This simplifies the task of traffic police who can't continuously and efficiently monitor all the violators. We gathered our data from three different sources. Kaggle was used to collect the data set. A number of items can be recognized by the real-time object recognition system YOLO in a single frame. It recognizes object more precisely and faster than other recognition systems. It can predict up to 9000 classes and even unseen classes. It divides the image into m∗m matrix and each grid detects objects within itself. The trained model is tested using a test dataset set after training in order to measure the performance. For Number plate detection we use OCR based character recognition. The recognized license plate images of violators are captured and stored in a database which is then sent to the concerned department. The stored data can be easily accessed using the developed mobile app. Metrics considered for evaluation is Accuracy. The average is utilized to assess object detection models like R-CNN and YOLO. The average calculates a score by comparing the detected box to the ground-truth bounding box. The better the score, the more precise the model's detections are. When compared to YOLOv2, YOLOv3 exhibits strong performance in terms of accuracy with 95.5%. © 2023 IEEE.

关键词： Object detection

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Enhancing Intrusion Detection systems Using RNN, LSTM, and Hybrid RNN-LSTM Models 2

Enhancing Intrusion Detection Systems Using RNN, LSTM, and H...

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2nd International Conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Ebadinezhad, Sahar Nia, Nooshin Nooraei Shirzad, Nasratullah Osemeha, Nwabueze Kenneth Near East University Department of Computer Information Systems Nicosia99138 Cyprus Artificial Intelligence Near East University Nicosia Cyprus Near East University Software Engineering Department Nicosia Cyprus

ISBN: (纸本)9798331505745

Due to The lack of comparison studies and practical applications of RNN, LSTM, and hybrid RNN-LSTM models for intrusion detection systems, especially when managing class imbalances in complex network datasets, represents a gap in the literature. This study investigates the application of deep learning techniques to improve the detection capabilities of IDS. For this study, we applied and assessed Recurrent Neural Network, Long Short-Term Memory, and Hybrid RNN-LSTM models on a UNSW-NB15 dataset, and to redress class imbalance we trained and validated our models using synthetic minority over-sampling. The RNN-LSTM model was shown to perform the best with a 9 4. 0 0 % accuracy in comparison to other models. These experimental results signify the possibility of enhancing IDS performance and a dependable method of detecting various network intrusions with the use of hybrid models comprised of RNN & LSTM. © 2025 IEEE.

关键词： Recurrent neural networks

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A Systematic Review of Approaches for Reviewing Security-Related Aspects in Agile Requirements Specification of Web Applications

A Systematic Review of Approaches for Reviewing Security-Rel...

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2023 Congress in computer Science, computer engineering, and Applied Computing, CSCE 2023

作者： Munir, Aqsa Assiri, Mohammed Alam, Saba Naveed Khan, Muzna Butt, Wasi Haider Humayun, Mamoona Department of Computer and Software Engineering Islamabad Pakistan College of Sciences and Humanities Aflaj Prince Sattam Bin Abdulaziz University Department of Computer Science Saudi Arabia College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Department of Software Engineering Saudi Arabia College of Computer and Information Sciences Jouf University Department of Information Systems Al-Jouf Saudi Arabia

ISBN: (纸本)9798350327595

Imperfections in requirement specification can cause serious issues during software development life cycle. It might bring about inferior quality products due to missing attributes, for example, security. Specifically, Web Applications are considered as obvious target for getting significant information. Security aspects have gotten hard to manage in web applications because security requirements are not regularly seen appropriately and often details are missing which lead into ill-characterized security-related aspects. With the help of systemic literature review (SLR), we have identified 32 major research works which were published in period of year 2010 to 2020. We identified 38 security-related aspects of web applications and also 13 techniques and tools for security related aspects in agile requirement specification. We have also analyzed 22 challenges in reviewing security related aspects in agile development life cycle. © 2023 IEEE.

关键词： Agile software development Quality requirements Requirement engineering Security specification Web applications

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Performance Improvement in Blood Cell Segmentation with Deep Learning-based Image Fusion Approach 8

Performance Improvement in Blood Cell Segmentation with Deep...

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8th International Artificial Intelligence and Data Processing Symposium, IDAP 2024

作者： Dogu, Fatma Tuana Dogan, Hulya Dogan, Ramazan Ozgur Sezen, F. Sena Karadeniz Technical University Faculty of Engineering Department of Electrical and Electronics Engineering Trabzon Turkey Karadeniz Technical University Faculty of Engineering Department of Software Engineering Trabzon Turkey Trabzon University Faculty of Computer and Information Sciences Department of Artificial Intelligence Engineering Trabzon Turkey Karadeniz Technical University Faculty of Pharmacy Department of Pharmacology Trabzon Turkey

ISBN: (纸本)9798331531492

To overcome the limitations of examining blood cells under a microscope, several investigations have been carried out using artificial intelligence techniques. These investigations aim to automate the processes of detecting, counting, segmenting, and classifying blood cells. Typically, these investigations employ two-dimensional (2D) images to apply artificial intelligence and image processing techniques. Nevertheless, these 2D images occasionally lack optimal focus as a result of the microscope's depth of focus (DOF). Inadequate achievement of optimally focused imaging has a detrimental impact on the efficiency of automated processes. In this context, the objective of this study is founded around two primary roles. The first of these is the performance improvement of blood cell segmentation by developing a deep learning-based image fusion approach. The second is the preparation of new data sets containing images with different focuses for optimally focused imaging of blood cells. In the study, four different performance evaluation criteria, namely Accuracy (ACC), Sensitivity (SEN), Specificity (SPE), and Jaccard, are utilized to demonstrate the efficacy of the deep learning-based image fusion approach in blood cell segmentation. Both the experimental results of the performance evaluation criteria and the visual findings obtained in the study indicate that the performance in blood cell segmentation is improved with a deep learning-based approach. © 2024 IEEE.

关键词： Image segmentation

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Enhancing handwritten text feature extraction through key point detection and graph representation

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Soft Computing 2024年第21期28卷 12977-12990页

作者： Rahman, Atta Ur Alahmadi, Tahani Jaser Alsenani, Yousef S. Ali, Sania Faculty of Computer Science and Engineering Ghulam Ishaq Khan Institute of Engineering Sciences and Technologies P.O. Box 23460 Topi Pakistan P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah21589 Saudi Arabia Department of Computer Science University of Science and Technology Bannu P.O. Box 28100 Khyber Pakhtunkhwa Bannu Pakistan

Handwriting typically consists of a wide range of writing forms with substantial differences in the placements and size of those writing shapes. The arrangement, organization, and spatial association of individual letters, words, and phrases on a written page are all examples of handwritten text structure. This study proposed a novel approach in which a handwritten text structure is primarily transformed into a Cartesian XY-coordinate system while maintaining its shape characteristics. The points on the contour of handwritten text with local maximum bending values are considered hypothetical breakdown points called Key Points (KPs). From the skeletal representation of handwriting, this approach first identifies KPs and their coordinate positions. After that, KPs are transformed into a graph-based representation with vertices and edges. With the use of geometric graphs, this representation attempts to capture the spatial and temporal organization of handwriting. Previous techniques focused on statistical methods, which offer fixed-size descriptions;however, graph-based representations are flexible in size and reveal the relationship between text structure. Graph representations enable the incorporation of contextual information by integrating extra features which improve text accuracy and understanding. The proposed approach is tested for writer identification and verification tasks on four benchmark datasets (CERUG-EN, CVL, Firemaker, and IAM) and one custom-built dataset. The findings demonstrated that the proposed approach obtained state-of-the-art accuracies across all the mentioned datasets. Particularly, it achieved the highest accuracy of 99.75% for identification and 99.81% for verification on the Firemaker dataset. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Feature extraction

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Biometric Verification System UsingHyperparameter Tuned Deep Learning Model

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computer systems Science & engineering 2023年第7期46卷 321-336页

作者： Mohammad Yamin Saleh Bajaba Sarah B.Basahel E.Laxmi Lydia Department of Management Information Systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Business Administration Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Management Information Systems Faculty of Economics and AdministrationKing Abdulaziz UniversityJeddahSaudi Arabia Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India

Deep learning(DL)models have been useful in many computer vision,speech recognition,and natural language processing tasks in recent *** models seem a natural fit to handle the rising number of biometric recognition problems,from cellphone authentication to airport security *** approaches have recently been utilized to improve the efficiency of various biometric recognition *** recognition was considered the more reliable and accurate biometric detection method *** recognition has been an active research region in the last few decades due to its extensive applications,from security in airports to homeland security border *** article presents a new Political Optimizer with Deep Transfer Learning Enabled Biometric Iris Recognition(PODTL-BIR)*** presented PODTL-BIR technique recognizes the iris for biometric *** the presented PODTL-BIR model,an initial stage of pre-processing is carried *** addition,the MobileNetv2 feature extractor is utilized to produce a collection of feature *** PODTL-BIR technique utilizes a bidirectional gated recurrent unit(BiGRU)model to recognise iris for biometric ***,the political optimizer(PO)algorithm is used as a hyperparameter tuning strategy to improve the PODTL-BIR technique’s recognition ***-ranging experimental investigation was executed to validate the enhanced performance of the PODTL-BIR *** experimental outcome stated the promising performance of the PODTL-BIR system over other existing algorithms.

关键词： Biometric verification iris recognition political optimizer deep learning feature extraction

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