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A Cuckoo Search Detector Generation-based Negative Selection Algorithm

computer Systems science & Engineering 2021年第8期38卷 183-195页

作者： Ayodele Lasisi Ali M.Aseere Department of Mathematical Sciences Faculty of ScienceAugustine UniversityIlara-EpeLagosNigeria Department and College of Computer Science King Khalid UniversityAbhaKingdom of Saudi Arabia

The negative selection algorithm(NSA)is an adaptive technique inspired by how the biological immune system discriminates the self from *** asserts itself as one of the most important algorithms of the artificial immune system.A key element of the NSA is its great dependency on the random detectors in monitoring for any ***,these detectors have limited *** detectors are generated,leading to difficulties for detectors to effectively occupy the non-self *** alleviate this problem,we propose the nature-inspired metaheuristic cuckoo search(CS),a stochastic global search algorithm,which improves the random generation of detectors in the *** characteristics such as mutation,crossover,and selection operators make the CS attain global *** the use of Lévy flight and a distance measure,efficient detectors are *** results show that integrating CS into the negative selection algorithm elevated the detection performance of the NSA,with an average increase of 3.52%detection rate on the tested *** proposed method shows superiority over other models,and detection rates of 98%and 99.29%on Fisher’s IRIS and Breast Cancer datasets,***,the generation of highest detection rates and lowest false alarm rates can be achieved.

关键词： Negative selection algorithm detector generation cuckoo search optimization

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A high-order predictor–corrector method for initial value problems with fractional derivative involving Mittag–Leffler kernel

A high-order predictor–corrector method for initial value p...

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作者： Aljhani, Sami Noorani, Mohd Salmi Md Douaifia, Redouane Abdelmalek, Salem Department of Mathematical Sciences Faculty of Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi Malaysia Process Engineering Department Faculty of Technology University of Saad Dahlab Blida 1 Blida Algeria Echahid Cheikh Larbi Tebessi University Tebessa Algeria Department of Mathematics and Computer Science Echahid Cheikh Larbi Tebessi University Tebessa Algeria

In this paper, we propose a numerical scheme of the predictor–corrector type for solving nonlinear fractional initial value problems;the chosen fractional derivative is called the Atangana–Baleanu derivative defined in Caputo sense (ABC). This proposed method is based on Lagrangian quadratic polynomials to approximate the nonlinearity implied in the Volterra integral, which is obtained by reducing the given fractional differential equation via the properties of the ABC-fractional derivative. Through this technique, we get corrector formula with high accuracy, which is implicit, as well as predictor formula, which is explicit and has the same precision order as the corrective formula. On the other hand, the so-called memory term is computed only once for both prediction and correction phases, which indicates the low cost of the proposed method. Also, the error bound of the proposed numerical scheme is offered. Furthermore, numerical experiments are presented in order to assess the accuracy of the new method on two differential equations. Moreover, a case study is considered where the proposed predictor–corrector scheme is used to obtain approximate solutions of a coupled nonautonomous ABC-fractional differential system. © 2023 John Wiley & Sons, Ltd.

关键词： Initial value problems

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ElHa Net: A Pattern Recognition Neural Network

ElHa Net: A Pattern Recognition Neural Network

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Innovative Approaches in Smart Technologies (ISAS), International Symposium on

作者： Elsadig Ahmed Mohamed Babiker Hanan Hassan Ali Adlan Dept. of Computer Science College of Computer Engineering and Science Prince Sattam Bin Abdulaziz University Kharj KSA Dept. of Computer Science Faculty of Mathematical Sciences Uof K Dept. of Computer Science College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh KSA

ISBN: (数字)9798331540104

ISBN: (纸本)9798331540111

Neural Networks are the state-of-the-art models that derive intelligent systems. Generalization of the neural network model is a judge of how well an architecture mimic human intelligence. Self-extraction neural networks become the dominant neural networks, especially in autonomous systems such as intelligent robotics, autonomous vehicles, ⃛ etc. This paper presents a neural network based on distance measure, ElHaNet Neural Network. ElHaNet network is a self-extraction neural network and is capable of pattern recognition. ElHaNet architecture composed of an extraction phase and a classification phase. Neurons in the extraction network capture patterns in the presented input. The classification network performs on the input patterns to be classified according to the problem in hand. The developed architecture is bench marked against well-known architectures for hand written digit recognition. The performance of the network is found to compete favorably with state-of-the-art models in the literature.

关键词： Hands Human intelligence Neurons Pattern recognition Biological neural networks Robots Autonomous vehicles

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AI-Powered Resilience: Addressing the Mental Health Impact of Mass Layoffs in the Digital Age 2

AI-Powered Resilience: Addressing the Mental Health Impact o...

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2nd International Conference on Emerging Trends in Information Technology and Engineering, ic-ETITE 2024

作者： Avik, Suranjeet Chowdhury Chowdhury, Abdullahi Moriwam Maruf, Md Abdullah Al Naha, Ranesh Ahammad, Imtiaz Bangladesh University of Textiles Dept. of Textile Engineering Dhaka Bangladesh School of Comp. and Mathematical Sciences The University of Adelaide Adelaide Australia University of Bangladesh Faculty of Computer Science & Eng. World Dhaka Bangladesh East West University Bangladesh Dept. of Computer Science & Engineering Dhaka Bangladesh Centre for Smart Analytics Federation University Australia Churchill Australia

ISBN: (纸本)9798350328202

In recent years, the technology sector has witnessed substantial disruptions, leading to major layoffs within toptier tech conglomerates. As the momentum of the digital age continues, there's an emerging concern regarding the psycho-logical impact of these layoffs on individual employees. This research delves deep into understanding the repercussions of these widespread job losses on mental health, emphasizing the increased intensity of symptoms like depression and anxiety. Utilizing the Beck Depression Inventory (BDI) for its methodology, the study scrupulously evaluates the depth of these mental health symptoms before and subsequent to the implementation of AI-driven mental health solutions. The conclusive data robustly establishes that when AI-centric interventions are combined with holistic mental health practices, there's a marked reduction in depression and anxiety symptoms, particularly those originating from job-related insecurities. As the world grapples with the swift changes in employment dynamics in this digital age, the study accentuates the significance of AI. Tapping into its vast potential to enhance mental resilience can be seen as an avant-garde strategy, presenting a pragmatic way to address the looming mental health challenges arising from unpredictable economic transitions. © 2024 IEEE.

关键词： Employment

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PASCAL DETERMINANTAL ARRAYS AND A GENERALIZATION OF RAHIMPOUR’S DETERMINANTAL IDENTITY

arXiv

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arXiv 2023年

作者： Teimoori, H. Khodakarami, H. Department of Computer Science Faculty of Mathematical and Computer Sciences Allameh Tabataba’i University Tehran Iran Zanjan Iran

In this paper, we shall first introduce the Pascal determinantal array of order k as a generalization of the standard Pascal array. We also present an algorithm to produce determinantal arrays of any order. Then we investigate geometric properties of these new determinantal arrays. As a by product, we give a proof of the correctness of our proposed algorithm. Then we give a geometric interpretation of the determinant of any k by k subarray of the Pascal array. Finally, we will give a generalization of Rahimpour’s determinantal identity, using the above geometric interpretation. © 2023, CC BY-NC-SA.

关键词： Geometry

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KHAYYAM-PASCAL DETERMINANTAL ARRAYS, STAR OF DAVID RULE AND LOG-CONCAVITY

arXiv

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arXiv 2023年

作者： Faal, Hossein Teimoori Khodakarami, Hasan Department of Computer Science Faculty of Mathematical and Computer Sciences Allameh Tabataba’i University Tehran Iran Zanjan Iran

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The Role of Artificial Intelligence in Improving Histopathological Diagnosis of Prostate Cancer: A Review

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Journal of Engineering and science in Medical Diagnostics and Therapy 2025年第2期8卷 020801页

作者： Afifi, Shereen Faragallah, M. Hamdy Taha, Radwa Baig, Mirza Ullah, Ehsan Gholam Hosseini, Hamid Hassanein, Sally I. Computer Science and Engineering Department Faculty of Media Engineering and Technology German University Cairo11835 Egypt Biochemistry Department Faculty of Science Helwan University Cairo11795 Egypt Digital Media and Engineering Department Faculty of Media Engineering and Technology German University Cairo11835 Egypt Digital Front Door Orion Health Auckland1010 New Zealand Department of Pathology and Laboratory Medicine Health New Zealand Te Toka Tumai Auckland1061 New Zealand School of Engineering Computer and Mathematical Sciences Auckland University of Technology Private Bag 92006 Auckland1142 New Zealand Biochemistry Department Faculty of Pharmacy and Biotechnology German University Cairo11835 Egypt

This review investigates the effectiveness of exploiting the massive Artificial Intelligence (AI) technology in the diagnosis of prostate cancer histopathological images. It focuses on studying and analyzing the current state and practice for utilizing AI tools, including significant machine learning and deep learning models in the histopathological image analysis process. The preferred reporting items for systematic reviews and meta-Analyses (PRISMA) methodology was adopted for conducting this systematic review to include recent research articles that have been published since 2017. Leveraging novel deep learning models and advanced imaging techniques, AI demonstrates promising capabilities in improving accuracy and efficiency in detecting and classifying prostate cancer. A comprehensive comparison of existing works has been presented with in-depth discussions around current limitations and key challenges while proposing some future advancements. This study aims to pave the way for future research and further integration of AI into the diagnostic processes toward early detection, personalized treatment strategies, and enhanced patient outcomes in the context of a prostate cancer diagnosis. Copyright © 2025 by ASME.

关键词： Contrastive Learning

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COVID-19 Vaccination Data Visualization: Issues and Challenges

COVID-19 Vaccination Data Visualization: Issues and Challeng...

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Visualization, Informatics and Technology Conference (IVIT), International

作者： Izzatul Syahirah Ismail Siti Hajar Aishah Samsudin Muhammad Adam Sani Mohd Sofian Hamidah Jantan Computer Science Programme Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA (UiTM) Cawangan Terengganu Kuala Terengganu Malaysia Department of Computer Science Faculty of Computer and Mathematical Sciences Universiti Teknologi MARA Cawangan Terengganu Kuala Terengganu Malaysia

ISBN: (纸本)9781665486491

Data Visualization plays an important role for patterns and trends analysis in trillion of data rows Big Data analysis, where the data can be represented in some graphical forms. Hence, the data could be more comprehensible in its visual summary in dashboards and storyboards. This study aims to discuss some issues and challenges in visualizing COVID-19 vaccination datasets. There are some possible issues in data visualization, as it is not easy and may be challenging to produce a good dashboard that are interesting and easy for viewers to understand. Therefore, this study focuses on some issues that may arise during performing a data visualization on the COVID-19 dataset. In this study, there are three dashboards have been studied, which are the COVID-19 tracker, its effectiveness, and its acceptance. The first two dataset are derived from Ministry of Health Malaysia bank data, whereas the third dataset is from a survey to support this analysis. The selected attributes are states, the number of people who have received the vaccine as adults, children, and teenagers, and the number of people who already received boosters, and reasons to not get a booster. The visualization issues found within the dashboard are mis-choice of colors, mis-choice of visual object type, lack of interactivity, and plotting too much data. As a result, this proposed alternative solutions for those issues such as color deliberately, pick a suitable visual object, create an interactive dashboard, and reduce the information overload in visualizing the data.

关键词： COVID-19 Visualization Pediatrics Image color analysis Data visualization Big Data Market research

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Multi-Label Learning Model for Diabetes Disease Comorbidity

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Journal of The Institution of Engineers (India): Series B 2023年第5期104卷 1133-1145页

作者： Folorunso, Sakinat Oluwabukonla Awotunde, Joseph Bamidele Adigun, Adepeju Abeke Panigrahi, Ranjit Garg, Amik Bhoi, Akash Kumar Department of Mathematical Sciences Olabisi Onabanjo University Ago-Iwoye Nigeria Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin240003 Nigeria Department of Information and Communication Technology Osun State University Osogbo Nigeria Department of Computer Applications Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India KIET Group of Institutions Delhi-NCR Ghaziabad201206 India Directorate of Research Sikkim Manipal University Sikkim Gangtok737102 India

Multi-label modeling of clinical data is a challenging classification problem especially for diseases with comorbidities. The complexity of the dataset makes it difficult to detect hidden pattern and infer useful information about disease classes that can occur simultaneously or successively. The presence of comorbidities has a significant impact on the treatment and management of diseases like diabetes. Hence, this research aims to build an intelligent clinical prediction model tool built with multi-label classification (MLC) algorithm for medical professionals to find trends in the patient’s data that show threats related to specific chronic illnesses. Patient’s clinical information consisting of 150,137 anonymized records with 214 but regulated to 147 attributes and 8 labels: ‘lymphoma,’ ‘aids,’ ‘leukemia,’ ‘cirrhosis,’ ‘immunosuppression,’ ‘diabetes mellitus,’ ‘solid tumor with metastases’ and ‘hepatic failure.’ The dataset was split into 70:30 train-test ratio. Tenfold cross-validation was used to assess the projection accuracy with ranking, example and label-based metrics. This research proposed BaggingML, PS, PSt, DeePML and RAkEL MLC models with decision tree (DT) as base classifier to learn the study dataset and compare their results based on standard metrices. BaggingML model gave the utmost performance based on lowest score on Hamming loss (0.034), highest accuracy (0.7081), AUROC (0.627), F1-Micro (0.843) and Precision Micro (0.843). © 2023, The Institution of Engineers (India).

关键词： Diseases

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Protein Secondary Structure Prediction using Topological Data Analysis 7

Protein Secondary Structure Prediction using Topological Dat...

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7th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2021

作者： Hassanpour, Amir Izadkhah, Habib Isazadeh, Ayaz University of Tabriz Faculty of Mathematical Sciences Department of Computer Science Tabriz Iran

ISBN: (纸本)9781665409384

Topological data analysis (TDA) is a novel and rapidly growing area of modern data science that uses topological, geometric, and algebraic tools to extract structural features from very complex and large-scale data that are usually incomplete and noisy. The primary motivation for studying this method was to study the shape of data, which has been connected to branches of pure mathematics such as homology, cohomology, and algebraic topology. In this method, the topological space obtained from cloud data can give it an interpretation of distance, continuity, and connectedness so patterns and relationships between the data are discovered quickly. In other words, using this method, the original information can be obtained from the sample or accidental information that was lost or messed up during sampling. Persistent homology is One of the essential tools of TDA. In this paper, after introducing the necessary mathematical concepts, through computing persistent homology and extracting appropriate features, we provide a new dataset, and we then develop a deep learning architecture to predict the protein secondary structure from the constructed dataset. The accuracy of the proposed method is at least 5% higher than the accuracy of the previous methods. © 2021 IEEE.

关键词： Topology

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