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Staging of Different Tumor by Utilizing Laser Guidance (@405 nm) in CT Scan Images Along with Statistical Analysis

Lasers in engineering 2025年第4-6期59卷 309-325页

作者： Amjad, M. Ullah, H. Siddique, M.A. Majid, M.A. Aleem, A. Akhtar, M. Gilanie, G. Abdullah-Al-Wadud, M. Farhan, M. Akram, A. Batool, Z. Nazir, A. Biophotonics Imaging Techniques Laboratory Institute of Physics The Islamia University of Bahawalpur 63100 Pakistan Department of Computer Science Faculty of Computing The Islamia University of Bahawalpur Bahawalpur Pakistan Dept. of Software Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia School of Earth Sciences and Engineering Hohai University Nanjing China

Early detection of lung cancer, liver cancer (HCC), and pancreatitis is crucial for effective treatment. This study assessed the use of laser guidance CT scans for diagnosis and staging. 26 patients of CT scan which were collected from BVH and civil hospital Bahawalpur, Pakistan. Visible light with a 405 nm wavelength is used. Results showed staging of lung cancer (2 at stage 1, 2 at stage 2, 3 at stage 4) and HCC (stages 1 to 4a). Pancreatic diseases detected included acute pancreatitis (44%), chronic pancreatitis (22%), gallstones (22%), and pancreatic tumors (11%). Alkaline phosphatase, SGOT, and blood urea levels were notably elevated in all three cancers compared to controls. CT scans show promise in identifying cancer stages and diagnosing pancreatitis, aiding in tailored treatment. Statistical analysis shows that alkaline phosphatase emerges as a potential biomarker for these conditions. ©2025 Old City Publishing, Inc.

关键词： Image analysis

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Influence of Key Nodes in Industrial Control Malicious Code Modeling 3

Influence of Key Nodes in Industrial Control Malicious Code ...

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3rd International Conference on Electronic Information engineering and computer science, EIECS 2023

作者： Yang, Wei Yan, Hongwei Xu, Chao Yao, Yu Zhou, Jinlei Wang, Baile Wang, Danni Northeastem University Software College Shenyang China Northeastem University School of Computer Science and Engineering Shenyang China State Grid Liaoning Information and Communication Company Department of Cybersecurity Monitoring Shenyang China

ISBN: (纸本)9798350316674

Key nodes in complex networks play an important role in the structure of the entire network. Different nodes in the network often have different degrees, and the node with the highest degree in the network is usually called a key node. However, the heterogeneity of nodes is mostly neglected in the research of malware propagation models by scholars at home and abroad. Therefore, based on the SUIQMR model proposed in the industrial control coupling network and considering the role of key nodes in the network, an improved malware suppression strategy for key nodes in the immune network is obtained. The simulation results verify the effectiveness of the improved malware suppression strategy, showing that the immune key nodes can effectively suppress the spread of malware. © 2023 IEEE.

关键词： industrial control network key nodes Malware Propagation Model Suppression strategy

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A Proposal for a New E-Waste Image Dataset Based on the UNU-KEYS Classification 23

A Proposal for a New E-Waste Image Dataset Based on the UNU-...

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23rd International Symposium on Electrical Apparatus and Technologies, SIELA 2024

作者： Iliev, Dimitar I. Marinov, Marin B. Ortmeier, Frank Technical University of Sofia Faculty of German Engineering and Industrial Management Sofia Bulgaria Technical University of Sofia Faculty of Electronic Engineering and Technologies Department of Electronics Sofia Bulgaria Otto-von-Guericke-University Magdeburg Faculty of Computer Science Software Engineering Magdeburg Germany

ISBN: (纸本)9798350382365

This paper introduces the largest and most diverse e-waste dataset to date, consisting of 19,613 pictures, 28,941 annotations, and 77 classes, where each class represents one visually distinctive type of electronic device. Furthermore, our dataset was structured using the United Nations University's UNU-KEYS classification, which ensures the compatibility of our dataset with the different e-waste classifications used worldwide and allowed us to choose more relevant dataset classes. The proposed methodology for image collection, annotation, and labeling is explained. The advantages and disadvantages of this dataset and how it can be improved in the future are presented. This dataset has been made open source with a permissive CC by 4.0 license so that it can be used to stimulate scientific progress in the field of automatic e-waste recognition, with the end goal of solving the global e-waste problem. The dataset is available online at https://***/electronic-waste-detection/e-waste-dataset-r0ojc. © 2024 IEEE.

关键词： dataset e-waste object detection UNU-KEYS WEEE

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Digital Evidence and Forensic Tools in Cyber Law: A Comprehensive Analysis 25

Digital Evidence and Forensic Tools in Cyber Law: A Comprehe...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Al-Sherideh, Ala'A Saeb Al-Zou'bi, Samah Suleiman Alshraiedeh, Fuad Elsoud, Esraa Abu Afaneh, Suha Al-Mousa, Mohammad Rasmi Alazaidah, Raed Khouj, Mohammed Samara, Ghassan Zarqa University Department of Cybersecurity Zarqa Jordan Zarqa University Department of Software Engineering Zarqa Jordan Zarqa University Department of Computer Science Zarqa Jordan Zarqa University Department of Data Science and Ai Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

The exponential rise in hacking, phishing, information theft, and electronic crime has become an actual concern with the increased use of digital devices and technology. By 2023 the number of IoT devices expectation will be 32.1 billion devices, leading to a significant increase in cybercrimes. These illegal behaviors are specifically connected to computers and communication technology. Consequently, there has been an urgent need for the development of cyber law which is a legal system closely related to digital evidence. This paper aim to provides delve into the significance and multifaceted applications of digital evidence within the context of cyber law, shedding light on the complexities and nuances inherent in its acceptance and reliability. The paper explores the importance and numerous uses of digital evidence in the context of cyber law, highlighting the challenges associated with its legitimacy and acceptance. Additionally, it focuses on the challenges that prevent digital evidence from being seamlessly incorporated into court processes and offers solutions and strategic advice to overcome these challenges. © 2024 IEEE.

关键词： Phishing

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Discovering Authorization Business Rules toward Detecting Web Applications Logic Flaws 23

Discovering Authorization Business Rules toward Detecting We...

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23rd International Arab Conference on Information Technology, ACIT 2022

作者： Alkofahi, Hamza Umphress, David Alawneh, Heba Jordan University of Science and Technology Department of Software Engineering Irbid Jordan Auburn University Department of Computer Science and Software Engineering Auburn United States Al Hussein Technical University Department of Cybersecurity Amman Jordan

ISBN: (纸本)9798350320244

Aggressive integration of validation checks into web framework software has altered the attack surface of web applications by reducing the opportunity for traditional injection flaws. The hacking community's reaction has shifted to a more subtle - and more challenging to detect - form of attacks, that of discovering and exploiting underlying application business logic. The lack of accurate business rules defining the final application product extends its logical vulnerability surface. We propose a novel black-box approach for discovering authorization business rules in web applications through users' dynamic behavior. Allowing applications that lack formal specifications to be better tested for logic vulnerabilities. Our approach discovers groups using agglomerative hierarchical clustering based on different profiling techniques that capture users' actions and privileges. We also automated the process of identifying the optimal number of roles. The results indicated high quality and stability in discovering business rules, even in smaller datasets. © 2022 IEEE.

关键词： Authorization

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Can end-user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

Can end-user feedback in social media be trusted for softwar...

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作者： Khan, Javed Ali Ullah, Tahir Khan, Arif Ali Yasin, Affan Akbar, Muhammad Azeem Aurangzeb, Khursheed Department of Computer Science Faculty of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom School of Computer & Technology Beijing Institute of Technology Beijing China M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland School of Software Northwestern Polytechnical University Shaanxi Xian China Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta Finland Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia

End-user feedback in social media platforms, particularly in the app stores, is increasing exponentially with each passing day. software researchers and vendors started to mine end-user feedback by proposing text analytics methods and tools to extract useful information for software evolution and maintenance. In addition, research shows that positive feedback and high-star app ratings attract more users and increase downloads. However, it emerged in the fake review market, where software vendors started incorporating fake reviews against their corresponding applications to improve overall software ratings. For this purpose, we conducted an exploratory study to understand how end-users register and write fake reviews in the Google Play Store. We curated a research data set containing 68,000 end-user comments from the Google Play Store and a fake review generator, that is, the Testimonial generator (TG). Its purpose is to understand fake reviews on these platforms and identify the common patterns potential end-users and professionals use to report fake reviews by critically analyzing the end-user feedback. We conducted a detailed survey at the University of science and Technology Bannu, Pakistan, to identify the intelligence and accuracy of crowd-users in manually identifying fake reviews. In addition, we developed a ground truth to be compared with the results obtained from the automated machine and deep learning (M&DL) classifier experiment. In the survey, 512 end-users participated and recorded their responses in identifying fake reviews. Finally, various M&DL classifiers are employed to classify and identify end-user reviews into real and fake to automate the process. Unlike humans, the M&DL classifiers performed well in automatically classifying reviews into real and fake by obtaining much higher accuracy, precision, recall, and f-measures. The accuracy of manually identifying fake reviews by the crowd-users is 44.4%. In contrast, the M&DL classifiers obtained an

关键词： Application programs

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Research and development of an information system for speech recognition based on neural network algorithms metrics 4

Research and development of an information system for speech...

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4th IEEE International Conference on Smart Information Systems and Technologies, SIST 2024

作者： Kanashev, Maksat Mukasheva, Assel Yedilkhan, Didar Rakhmetulaeva, Sabina Gnatyuk, Sergiy Nazym, Mirambayeva Almaty University of Power Engineering and Telecommunications Almaty Kazakhstan The School of Information Technology and Engineering Kazakh-British Technical University Almaty Kazakhstan The Department of Computer Engineering Astana IT University Astana Kazakhstan The Department of Information Systems International University of Information Technologies Almaty Kazakhstan The Faculty of Cybersecurity Computer and Software Engineering National Aviation University National Aviation University Kyiv Ukraine

ISBN: (纸本)9798350374865

This article examines the parameters of speech recognition in audio recordings containing a variety of phrases uttered by different people with different accents and intonations. The aim of the study is to evaluate the recognition speed, the accuracy of audio-to-text conversion and resource consumption when using SpeechRecognition and Levenshtein libraries. During the analysis, the above libraries were used to record and recognize audio files, as well as to calculate the distance between two lines and determine the recognition accuracy. The findings of this study can be useful for improving speech recognition systems and creating more adaptive and efficient solutions for audio data processing. The results of comparing the accuracy of speech recognition models are recorded in JSON format, after which, using the matplotlib library, they are visualized as graphs, comparing the speed and accuracy of recognition. Visualization of data in this way contributes to their improvement and more effective integration into educational systems. In conclusion, the authors emphasize the prospects for further development of speech recognition technologies in different fields. © 2024 IEEE.

关键词： Audio recordings

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A modified grey wolf optimizer with multi-solution crossover integration algorithm for feature selection

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Journal of Ambient Intelligence and Humanized Computing 2025年第1期16卷 329-345页

作者： Ihsan, Muhammad Din, Fakhrud Zamli, Kamal Z. Ghadi, Yazeed Yasin Alahmadi, Tahani Jaser Innab, Nisreen Faculty of IT Department of Computer Science and IT University of Malakand Khyber Pakhtunkhwa Chakdara18800 Pakistan Faculty of Computing College of Computing and Applied Sciences Universiti Malaysia Pahang Pahang Pekan26600 Malaysia Department of computer science and software engineering Al Ain University Abu Dhabi12555 United Arab Emirates Department of Information SystemsCollege of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh84428 Saudi Arabia Department of Computer Science and Information SystemsCollege of Applied Sciences Al-Maarefa University Diriyah Riyadh13713 Saudi Arabia

Feature selection helps eradicate redundant features which is essential to mitigate the curse of dimensionality when a machine-learning model deals with high-dimensional datasets. Grey Wolf Optimizer (GWO) is a swarm-based algorithm that simulates the wolves’ hunting behavior. Although very efficient, GWO faces some limitations which may cause premature convergence and/or local optima trapping. Moreover, GWO relies mainly on the three best wolves, limiting its potential for diverse exploration and exploitation. This work proposes an improved version of GWO namely, a modified grey wolf optimizer with multi-solution crossover integration (MGWO-MCI) algorithm. MGWO-MCI algorithm incorporates a multi-solution strategy that evolves new potential solutions in the optimization process. A crossover operation is performed between the new wolves and the existing hierarchy, reforming the position-updating process. MGWO-MCI utilizes this position-updating process using two different approaches. The first approach named MGWO-MCI-I expands the additional wolves’ role to both exploration and exploitation whereas the second approach named MGWO-MCI-II incorporates their role to exploration only. These approaches are evaluated and tested using 18 datasets and an Intrusion detection dataset NSL-KDD for feature selection. Statistically, the results are analyzed through the Wilcoxon test, which shows the superiority of MGWO-MCI-II. MGWO-MCI-II outperforms others with an accuracy of 98.6% on NSL-KDD and achieves 55.5% overall best outcomes on other datasets. Moreover, the MGWO-MCI was evaluated on two constrained optimization problems, the pressure vessel and welded beam design validating its effectiveness and adaptability in solving different optimization problems. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Constrained optimization

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PraK Tool V3: Enhancing Video Item Search Using Localized Text and Texture Queries 31st

PraK Tool V3: Enhancing Video Item Search Using Localized Te...

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31st International Conference on Multimedia Modeling, MMM 2025

作者： Stroh, Michael Kloda, Vojtěch Verner, Benjamin Vopálková, Zuzana Buchmüller, Raphael Jäckl, Bastian Hajko, Jakub Lokoč, Jakub Visual Computing Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany SIRET Research Group Department of Software Engineering Faculty of Mathematics and Physics Charles University Prague Czech Republic Data Analysis and Visualization Department of Computer Science Faculty of Natural Sciences Konstanz University Konstanz Germany

ISBN: (纸本)9789819620739

We present a third version of the PraK system designed around an effective text-image and image-image search model. The system integrates sub-image search options for localized context search for CLIP and image color/texture and interfaces tailored to specific task types. A user study shows the benefits of sub-image search in homogeneous video collections. We further enhance the user experience of the PraK framework by re-designing the frontend and introducing task-specific views. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Video analysis

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Comparison of IDS in IoT, Mobile and Cloud based Systems 12

Comparison of IDS in IoT, Mobile and Cloud based Systems

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Gok, Gorkem Boyaci, Aytug Ulas, Mustafa Firat University Faculty of Technology Department of Software Engineering Elaziǧ Turkey Air Force Academy National Defence University Department of Computer Engineering İstanbul Turkey Firat University Faculty of Engineering Department of Artificial Intelligence and Data Science Engineering Elaziǧ Turkey

ISBN: (纸本)9798350330366

This study is meant to research the evolution of intrusion detection and network monitoring within computer, cloud-based systems, IIoT, and mobile environments. The source has outlined the novel technologies in IDS, from 'Fast Learning Network' to hybrid classifiers aiming at protecting cloud computing. The findings highlight innovation IDS technologies that provide flexible and peculiar solutions to ward off potential cyber threats. In addition, it presented with the use of Nagios, NConf, the state of the art in network traffic monitoring, and deep learning as tools for advanced management and network protection. Besides, the research also reviewed concepts of Big Data Analytics and Federated Learning in bettering direct integration and anomaly detection in IIoT. A good comparison of the two would show that recent technological advancements are much needed for network efficiency and its cybersecurity. Based on the findings, the scope of networked system security develops dynamically amid current challenges. © 2024 IEEE.

关键词： cybersecurity

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