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A Deep Learning-Based Approach for Mammographic Architectural Distortion Classification 1

5th International Conference on Innovative Computing, IC 2022

作者： ur Rehman, Khalil Li, Jianqiang Pei, Yan Yasin, Anaa Ali, Saqib School of Software Engineering Beijing University of Technology Beijing China Beijing Engineering Research Center for IoT Software and Systems Beijing China Computer Science Division University of Aizu Aizuwakamatsu Japan

ISBN: (数字)9789811941320

ISBN: (纸本)9789811941313

Breast cancer is the most deadly cancer in females globally. Architectural distortion is the third most often reported irregularity on digital mammograms among the masses and microcalcification. Physically identifying architectural distortion for radiologists is problematic because of its subtle appearance on the dense breast. Automatic early identification of breast cancer using computer algorithms from a mammogram may assist doctors in eliminating unwanted biopsies. This research presents a novel diagnostic method to identify AD ROIs from mammograms using computer vision-based depth-wise CNN. The proposed methodology is examined on private PINUM 2885 and public DDSM 3568 images and achieved a 0.99 and 0.95 sensitivity, respectively. The experimental findings revealed that the proposed scheme outperformed SVM, KNN, and previous studies. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mammography

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Understanding, Implementing, and Supporting Security Assurance Cases in Safety-Critical Domains

arXiv

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arXiv 2025年

作者： Mohamad, Mazen Department of Computer Science & Engineering Division of Interaction Design and Software Engineering Chalmers University of Technology University of Gothenburg Gothenburg Sweden

The increasing demand for connectivity in safety-critical domains has made security assurance a crucial consideration. In safety-critical industry, software, and connectivity have become integral to meeting market expectations. Regulatory bodies now require security assurance cases (SAC) to verify compliance, as demonstrated in ISO/SAE-21434 for automotive. However, existing approaches for creating SACs do not adequately address industry-specific constraints and requirements. In this thesis, we present CASCADE, an approach for creating SACs that aligns with ISO/SAE-21434 and integrates quality assurance measures. CASCADE is developed based on insights from industry needs and a systematic literature review. We explore various factors driving SAC adoption, both internal and external to companies in safety-critical domains, and identify gaps in the existing literature. Our approach addresses these gaps and focuses on asset-driven methodology and quality assurance. We provide an illustrative example and evaluate CASCADE's suitability and scalability in an automotive OEM. We evaluate the generalizability of CASCADE in the medical domain, highlighting its benefits and necessary adaptations. Furthermore, we support the creation and management of SACs by developing a machine-learning model to classify security-related requirements and investigating the management of security evidence. We identify deficiencies in evidence management practices and propose potential areas for automation. Finally, our work contributes to the advancement of security assurance practices and provides practical support for practitioners in creating and managing SACs. Copyright © 2025, The Authors. All rights reserved.

关键词： Automotive industry

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Incremental Instance Segmentation for Cluttered Baggage Threat Detection

Incremental Instance Segmentation for Cluttered Baggage Thre...

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2023 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2023

作者： Nasim, Ammara Velayudhan, Divya Ahmed, Abdelfatah Hassan Hassan, Taimur Akcay, Samet Akram, Muhammad Usman Werghi, Naoufel National University of Sciences and Technology Department of Computer and Software Engineering Pakistan Khalifa University KUCARS and C2PS Department of Electrical Engineering and Computer Science United Arab Emirates Abu Dhabi University Department of Electrical and Computer Engineering Abu Dhabi United Arab Emirates Intel Corporation RandD Division United Kingdom

ISBN: (纸本)9781665434454

Identification of contraband items from highly oc-cluded baggage of air travelers is a challenging task even for human experts with very high experience. Many researchers have been working rigorously to develop computer vision-based techniques for baggage screening through X-ray images. Nu-merous machine learning and deep learning-based frameworks have been proposed by researchers in the last two decades. However, all of these techniques face limitations in segmenting prohibited items from highly occluded and cluttered baggage. In this paper, we propose a novel framework based on semantic segmentation to automatically detect concealed prohibited items from X-ray baggage scans. Furthermore, to detect different overlapping instances of the same contraband item, we propose an instance-Aware segmentation model that enables the semantic segmentation model to identify multiple instances of the same threat category through incremental learning without requiring additional overhead. The proposed framework is computationally lighter compared to other similar approaches as it requires min-imal training examples and leverages previous knowledge. The proposed model has outperformed state-of-The-Art instance seg-mentation techniques when tested on publicly available GDXray and SIXray datasets, giving mean average precision scores of 0.50 and 0.47, respectively. In addition, the proposed framework leads other instance segmentation baseline models in terms of mean inference time. © 2023 IEEE.

关键词： Semantic Segmentation

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The Future of Differentiated Thyroid Cancer Recurrence Prediction Using a Machine Learning Framework Advancements, Challenges, and Prospects 4

The Future of Differentiated Thyroid Cancer Recurrence Predi...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Imtiaz, Irsa Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Mohammad Mir, Azka Masud, Mehedi Nirsanametla, Yadaiah University of Sialkot Department of Software Engineering Sialkot Pakistan GULF University for Sciences and Technology Department of Software Engineering Mubarak Al-Abdullah Kuwait Taif University Department of Computer Science Taif Saudi Arabia Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR institute of technology and sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

Differentiated thyroid cancer originates in the thyroid gland, which is positioned in the front of the neck. The thyroid gland generates hormones that control metabolism, heart rate, and other bodily functions. Differentiated thyroid carcinoma (DTC) recurrence is a major challenge in clinical therapy. Early detection and treatment play an important role in reducing the impact of thyroid cancer recurrence. The development of precise prediction algorithms is demanding. The prognosis, diagnosis, and treatment of differentiated thyroid carcinoma have been the subject of extensive investigation. Scholars have investigated diverse methodologies to forecast the likelihood of getting thyroid cancer, refine early identification techniques, and augment therapeutic results. Machine learning (ML) frameworks have emerged as useful tools in this setting, with the potential to improve prediction accuracy and patient outcomes. This paper provides a detailed evaluation of the current state and future directions of DTC recurrence prediction using machine learning. We examine current advances in machine learning techniques, data sources, and feature selection approaches used in DTC recurrence prediction models. Several machine-learning algorithms have been applied. We have suggested the model containing the classifier with the highest accuracy after comparing the accuracy percentage of the various classifiers that were produced. Our suggested model has a 98.17% accuracy rate with Bagging. Finally, we propose solutions to these difficulties and emphasize ML's potential to revolutionize the landscape of DTC recurrence prediction. We hope that this analysis will provide insights into the developing role of machine learning in DTC management and motivate more research in this crucial area. © 2024 IEEE.

关键词： Diseases

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Google Scholar University Ranking Algorithm to Evaluate the Quality of Institutional Research

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computers, Materials & Continua 2023年第6期75卷 4955-4972页

作者： Noor Ul Sabah Muhammad Murad Khan Ramzan Talib Muhammad Anwar Muhammad Sheraz Arshad Malik Puteri Nor Ellyza Nohuddin Department of Computer Science Government College UniversityFaisalabad 38000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Software Engineering Government College UniversityFaisalabad 38000Pakistan Institute of IR4.0 Universiti Kebangsaan MalaysiaBangiSelangor43600Malaysia

Education quality has undoubtedly become an important local and international benchmark for education,and an institute’s ranking is assessed based on the quality of education,research projects,theses,and dissertations,which has always been ***,this research paper is influenced by the institutes ranking all over the *** data of institutes are obtained through Google Scholar(GS),as input to investigate the United Kingdom’s Research Excellence Framework(UK-REF)*** this purpose,the current research used a Bespoke Program to evaluate the institutes’ranking based on their *** bespoke program requires changes to improve the results by addressing these methodological issues:Firstly,Redundant profiles,which increased their citation and rank to produce false ***,the exclusion of theses and dissertation documents to retrieve the actual publications to count for ***,the elimination of falsely owned articles from scholars’*** accomplish this task,the experimental design referred to collecting data from 120 UK-REF institutes and GS for the present year to enhance its correlation analysis in this new *** data extracted from GS is processed into structured data,and afterward,it is utilized to generate statistical computations of citations’analysis that contribute to the ranking based on their *** research promoted the predictive approach of correlational ***,experimental evaluation reported encouraging results in comparison to the previous modi-fication made by the proposed *** paper discussed the limitations of the current evaluation and suggested the potential paths to improve the research impact algorithm.

关键词： Google scholar institutes ranking research assessment exercise research excellence framework impact evaluation citation data

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Towards Understanding and Applying Security Assurance Cases for Automotive Systems

arXiv

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arXiv 2024年

作者： Mohamad, Mazen Department of Computer Science & Engineering Division of Interaction Design and Software Engineering Chalmers University of Technology Gothenburg University Gothenburg Sweden

Security Assurance Cases (SAC) are structured bodies of arguments and evidence used to reason about security properties of a certain artefact. SAC are gaining focus in the automotive domain as the need for security assurance is growing due to software becoming a main part of vehicles. Market demands for new services and products in the domain require connectivity, and hence, raise security concerns. Regulators and standardisation bodies started recently to require a structured for security assurance of products in the automotive domain, and automotive companies started, hence, to study ways to create and maintain these cases, as well as adopting them in their current way of working. In order to facilitate the adoption of SAC in the automotive domain, we created CASCADE, an approach for creating SAC which have integrated quality assurance and are compliant with the requirements of ISO/SAE-21434, the upcoming cybersecurity standard for automotive systems. CASCADE was created by conducting design science research study in two iterative cycles. The design decisions of CASCADE are based on insights from a qualitative research study which includes a workshop, a survey, and one-to-one interviews, done in collaboration with our industrial partners about the needs and drivers of work in SAC in industry, and a systematic literature review in which we identified gaps between the industrial needs and the state of the art. The evaluation of CASCADE was done with help of security experts from a large automotive OEM. It showed that CASCADE is suitable for integration in industrial product development processes. Additionally, our results show that the elements of CASCADE align well with respect to the way of working at the company, and has the potential to scale to cover the requirements and needs of the company with its large organization and complex products Copyright © 2024, The Authors. All rights reserved.

关键词： Automotive industry

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Optimizing Heart Failure Predictive Accuracy: An Effective Approach Using SMOTE Techniques 4

Optimizing Heart Failure Predictive Accuracy: An Effective A...

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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Baber, Ahmed Ahmed, Faizan Rehman, Attique Ur Javaid, Sabeen Alshammeri, Menwa Mir, Azka Kumar, Deepak University of Sialkot Department of Software Engineering Sialkot Pakistan College of Computer and Information Sciences Al Jouf University Department of Computer Science Saudi Arabia Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR institute of technology and sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

Heart failure occurs when the heart's lower cham-bers (ventricles) weaken and can't pump blood effectively. The heart contains 2 ventricles that are left and right. When left ventricular failure occurs, it causes shortness of breath and fatigue, and when right ventricular failure occurs, it causes peripheral and abdominal fluid accumulation. Heart failure is a disorder in breathing and oxygen supply to the whole body. As we know, Heart is responsible for circulating blood all over the body parts. When it stops pumping, blood can no longer circulate effectively causing fluid to build up in the lungs causing difficulty in breathing and in the legs, causing swelling. The fluid buildup can cause shortness of breath and swelling of the legs and feet. As a result, fluctuation in breathing takes place and, in the end, the patient dies. This study investigates the use of machine learning tools, like k-Nearest Neighbors, Decision Trees, Naïve Bayes, and Random Forests, to identify heart failure. We are proposing a model for Heart Failure Prediction using a machine learning algorithm and a dataset to determine the performance in terms of accuracy. As a result, the Random Forest classifier along with the SMOTE operator provides the highest accuracy of 91.12%, precision of 87.88%, recall of 95.39%, and F1 measure of 91.48% among the models considered. This research is a very important contribution to healthcare for effective predictions of this disease, as the number of patients is increasing day by day. The results hold significance for future modifications too in medical diagnostics and helping material for more accurate detection methodologies. © 2024 IEEE.

关键词： Nearest neighbor search

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Left and Right Lung-Specific Method of Segmentation in Chest Radiographs

Left and Right Lung-Specific Method of Segmentation in Chest...

引用

Applications of Machine Learning 2024

作者： Marsh, John J. Narayanan, Barath Narayanan Hardie, Russell C. Electrical and Computer Engineering Department University of Dayton 300 College Park DaytonOH45469 United States Sensor and Software Systems Division University of Dayton Research Institute 300 College Park DaytonOH45469 United States

ISBN: (纸本)9781510679368

Lung segmentation supports essential functionality within the realm of computer-aided detection and diagnosis using chest radiographs. In this research, we present a novel bifurcation approach of segmenting the lungs by separating them down the spinal column and having separate networks for the right and the left lungs respectively. Results from the right lung and left lung networks are then merged to form the overall lung. We utilize DeepLabV3+ network with ResNet50 as the backbone for both left and right lung networks. Results are presented for publicly available datasets such as Shenzhen Dataset and Japanese Society of Radiological Technology (JSRT) dataset. Our proposed bifurcation approach achieved an overall accuracy of 98.8% and an IoU (Intersection over Union) of 0.977 for a set of 100 cases in Shenzhen dataset. We conducted an additional robustness study of this method by training and testing on an independent dataset utilizing a hold-out methodology. We utilize a private dataset for the training and testing occurs on an independent JSRT dataset comprising 140 cases and our algorithm achieved an overall IoU of 0.945 thereby demonstrating its efficacy against other whole lung models and setting a new benchmark for future research works. © 2024 SPIE.

关键词： Image segmentation

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Optimizing Generative Adversarial Networks Models for Non-Pneumatic Tire Design: A Comparative Analysis and Evaluation 23

Optimizing Generative Adversarial Networks Models for Non-Pn...

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2023 Research in Adaptive and Convergent Systems, RACS 2023

作者： Seong, Ju-Yong Ji, Seung-Min Choi, Dong-Hyun Lee, Sungchul Division of Computer Science and Engineering Sunmoon University Chungcheongnam-do Asan-si Korea Republic of Department of Artificial Intelligence and Software Technology Sunmoon University Chungcheongnam-do Asan-si Korea Republic of

ISBN: (纸本)9798400702280

Pneumatic tires1 are used in a wide range of industries. However, they are difficult to design and rely on the knowledge of experienced designers. To aid in the design of pneumatic tires, this paper suggests the use of Generative Adversarial Networks (GAN) models. The 2000 created images for training were used after removing highly sim-ilar images using Mean Squared Error (MSE) and Structural Similarity Index (SSIM). To find the best model for generating patterns of regularly shaped non-pneumatic tires, GAN, Deep Convolu-tional Generative Adversarial Networks (DCGAN), StyleGANv2 ADA and ProjectedGAN were compared and analyzed. In the qualitative evaluation, the GAN, DCGAN, and StyleGAN v2-ADA models showed that the circle shape was distorted and did not maintain a consistent pattern, but ProjectedGAN showed that the circle remained consistent and the pattern was less distorted than the other GAN models. When evaluating quantitative metrics, ProjectedGAN performed best among several techniques that measure the difference be-tween the generated and actual image distributions. © 2023 ACM.

关键词： Generative adversarial networks

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POSTER: High Performance GPU Concurrent B+tree 27

POSTER: High Performance GPU Concurrent B+tree

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27th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2022

作者： Zhang, Weihua Zhao, Chuanlei Peng, Lu Lin, Yuzhe Zhang, Fengzhe Jiang, Jinhu Software School Fudan University Shanghai China Division of Electrical and Computer Engineering Louisiana State University Baton RougeLA United States

ISBN: (纸本)9781450392044

Concurrent B+trees have been widely used in many systems from file systems to databases. With the volume of data requests expanding exponentially, the systems are facing tremendous performance pressure. GPUs have shown their potential to accelerate the concurrent B+trees operations with their high volume of parallel computing resources and large memory bandwidth. In concurrent B+tree, the conflicts should be detected and resolved when multiple concurrent requests are traversing and operating on the tree. However, conflict detection and handling in concurrent B+tree complicates the request processing logic, increases the number of memory accesses and leads to execution path divergence. That leads to performance degradation and increased response time variance. We present a high performance GPU concurrent B+tree. To reduce the performance impact introduced by conflict detection, an optimization is proposed to detect and resolve key conflicts before concurrent requests are issued to the execution phase. Then another optimization is used to reduce structure conflicts and concurrent control overheads. Finally, an optimization is used to reduce structure conflicts and improve memory performance by exploiting the locality among requests. Evaluations on a TITAN V GPU show that our GPU concurrent B+tree is efficient with a throughput of 1.23 billion per second. Compared to a state-of-the-art GPU B+tree system, it can achieve 5.1X speedup and reduce the response time variance from 38% to 4%. © 2022 Owner/Author.

关键词： Graphics processing unit

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