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Adaptive Dynamic Dipper Throated Optimization for Feature Selection in Medical Data

computers, Materials & Continua 2023年第4期75卷 1883-1900页

作者： Ghada Atteia El-Sayed M.El-kenawy Nagwan Abdel Samee Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Ahmad Taher Azar Nima Khodadadi Reham A.Ghanem Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBenha13518Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Civil Engineering University of Science and TechnologyMiami33101USA Department of Civil and Environmental Engineering Florida International UniversityMiamiUSA Oral Biology Department Faculty of Oral and Dental MedicineDelta University for Science and TechnologyGamasaEgypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

The rapid population growth results in a crucial problem in the early detection of diseases inmedical *** all the cancers unveiled,breast cancer is considered the second most severe ***,an exponential rising in death cases incurred by breast cancer is expected due to the rapid population growth and the lack of resources required for performing medical *** recent advances in machine learning could help medical staff in diagnosing diseases as they offer effective,reliable,and rapid responses,which could help in decreasing the death *** this paper,we propose a new algorithm for feature selection based on a hybrid between powerful and recently emerged optimizers,namely,guided whale and dipper throated *** proposed algorithm is evaluated using four publicly available breast cancer *** evaluation results show the effectiveness of the proposed approach from the accuracy and speed *** prove the superiority of the proposed algorithm,a set of competing feature selection algorithms were incorporated into the conducted *** addition,a group of statistical analysis experiments was conducted to emphasize the superiority and stability of the proposed *** best-achieved breast cancer prediction average accuracy based on the proposed algorithm is 99.453%.This result is achieved in an average time of 3.6725 s,the best result among all the competing approaches utilized in the experiments.

关键词： Medical dataset breast cancer guided whale optimizer dipper throated optimizer feature selection meta-heuristics

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A FRAMEWORK FOR RANKING CRITICAL SUCCESS FACTORS OF BUSINESS INTELLIGENCE BASED ON ENTERPRISE ARCHITECTURE AND MATURITY MODEL

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Interdisciplinary Journal of Information, Knowledge, and Management 2022年 17卷 543-575页

作者： Farshadi, Ramina Nazemi, Eslam Abdolvand, Neda Faculty of Computer Science and Computer Engineering Shahid Beheshti University Tehran Iran Department of Management Faculty of Social Sciences and Economics Alzahra University Tehran Iran

Aim/Purpose The aim of this study is to identify Critical Success Factors (CSF) of Business Intelligence (BI) and provide a framework to classify CSF into layers or perspectives using an enterprise architecture approach, then rank CSF within each perspective and evaluate the importance of each perspective at different BI maturity levels as well. Background Although the implementation of the BI project has a significant impact on creating analytical and competitive capabilities, the lack of evaluation of CSF holistically is still a challenge. Moreover, the BI maturity level of the organization has not been considered in the BI implementation project. Identifying BI critical success factors and their importance can help the project team to move to a higher maturity level in the organization. Methodology First, a list of distinct CSF is identified through a literature review. Second, a framework is provided for categorizing these CSF using enterprise architecture. Interviewing is the research method used to evaluate the importance of CSF and framework layers with two questionnaires among experts. The first questionnaire was done by Analytical Hierarchy Process (AHP), a quantitative method of decision-making to calculate the weight of the CSF according to the importance of CSF in each of the framework layers. The second one was conducted to evaluate framework layers at different BI maturity levels using a Likert scale. Contribution This paper contributes to the implementation of BI projects by identifying a comprehensive list of CSF in the form of a holistic multi-layered framework and ranking the importance of CSF and layers at BI maturity levels. Findings The most important CSF in BI implementation projects include senior management support, process identification, data quality, analytics quality, hardware quality, security standards, scope management, documentation, project team skills, and customer needs transformation, which received the highest scores in framework

关键词： Decision making

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Comparison of feature selection methods in Kurdish text classification

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Iran Journal of computer science 2024年第1期7卷 55-64页

作者： Saeed, Ari M. Badawi, Soran Ahmed, Sara A. Hassan, Diyari A. Computer Science Department University of Halabja KRG Kurdistan Halabja Iraq Language Center Charmo University KRG Chamchamal Kurdistan Iraq Department of Computer Science Komar University of Science and Technology Kurdistan Region Sulaymaniyah Iraq Faculty of Engineering and Computer Science Qaiwan International University Kurdistan Region Sulaymaniyah Iraq

The aim of this study is to investigate the impact of feature selection (FS) on the performance of classifiers for text classification (TC) in Kurdish. TC accuracy can be adversely affected by the high dimensionality of the feature space. Hence, FS is employed to reduce the feature space and improve accuracy. This study evaluates several FS methods, including discriminative feature selection (DFSS), Chi-squared (CHI2), Discriminative power measure (DPM), Gini index, Distinguishing feature selector (DFS), Comprehensively measure feature selection (CMFS), and Correlation coefficient (CC), on two Kurdish datasets (KDC-4007 and KNDH). Multinomial naive Bayes (MNB) and Support vector machines (SVMs) are employed to evaluate the accuracy and F measure of FS. The experiment tests nine subsets of features (50, 100, 250, 500, 750, 1000, 2000, 3000, and 4000). The study finds that the FS methods CHI2 and DPM exhibit superior F measure and accuracy scores for SVM, while the CHI2 and CMF methods are superior for MNB. Importantly, most FS methods have only been applied to English texts, with little or no investigation of the Kurdish language. Therefore, this study fills an important gap in the literature by evaluating the effectiveness of various FS methods for Kurdish language TC. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Feature selection Kurdish language Multinomial naive Bayes Support vector machine Text classification

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A Well Structured Design for Individualized Healthcare System via Fuzzy Logic System (FLS) 4

A Well Structured Design for Individualized Healthcare Syste...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Raman, Ramakrishnan Verma, Apurv Kumar, Vikram Rastogi, Shailesh Pillai, Biju G. Meenakshi, R. India Parul Institute of Technology Parul University Computer Science and Engineering Faculty of Engineering and Technology India Srv Media Pvt. Ltd India Pune India Faculty of Management It & Business Analytics India Institute of Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9798350360165

Personalized medicine is driving the need for new and customized biopharmaceuticals using proteins that have been changed. As such, it is critical to evaluate these items for allergenicity before introducing them as treatments. Even though there are a number of techniques available to predict the allergenicity of proteins, it may still be difficult to discriminate between allergens and nonallergens, particularly when the allergens have a considerable sequence resemblance to their known counterparts. To address this, we provide 'FuzzyApp,' a novel fuzzy rule-based system that evaluates a query protein's potential for allergenicity. FuzzyApp uses fuzzy IF-THEN rules that come from five different modules to determine how allergenic a protein is. FuzzyApp outperforms the state-of-the-art techniques on a variety of datasets, attaining a pleasing trade-off between specificity and sensitivity, as shown by an upbeat Mathew's correlate coefficient. FuzzyApp's capacity to distinguish allergens from allergen-like potential nonallergens (APN) is highlighted by its notable high specificity in this regard. The efficacy and uniformity of this suggested approach are further supported by error analysis and analysis of the whole proteome dataset. Furthermore, Tropomyosin from a variety of allergic and nonallergenic sources is properly predicted by FuzzyApp. The companion online service helps users comprehend the reasoning behind predictions by facilitating batch sequence filing and presenting findings in coherent words. © 2024 IEEE.

关键词： Personalized medicine

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Literature Review on Packet Sniffing: Essential for Cybersecurity & Network Security 5

Literature Review on Packet Sniffing: Essential for Cybersec...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Shaw, Ranju Parveen, Suraiya Jamia Hamdard University Dept. of Computer Science Engineering with Specialization in Cyber Forensic Information Security Delhi India Faculty at Jamia Hamdard University Sest Department of Computer Science Engineering Delhi India

ISBN: (纸本)9798350385649

In the realm of cybersecurity and network security, the significance of monitoring and analyzing network traffic cannot be overstated Network packet sniffing, a technique employed to capture and inspect data packets traversing a network, has emerged as an imperative tool for understanding network behavior, identifying vulnerabilities, and enhancing overall security. This study analyzes packet sniffing by exploring its role in modern cybersecurity practices. By analyzing packet sniffing mechanisms, including its methodologies and potential applications, this res earch sheds light on its effectiveness as a proactive security solution. Furthermore, the study examines the ethical considerations surrounding packet sniffing, addressing concerns related to user privacy and data protection. It also investigates the legal aspects that govern the use of packet sniffing techniques, ensuring compliance with regulations while maximizing its benefits. Through case studies and real-world examples, this research illustrates how packet sniffing can be leveraged to detect network anomalies, thwart malicious activities, and fortify network infrastructures. By presenting a comprehensive overview of packet sniffing's capabilities and limitations, this study equips cybersecurity practitioners with the knowledge needed to make informed decisions about its integration into their defensive strategies. © 2024 IEEE.

关键词： Cybersecurity

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Survey on IoT Multi-Factor Authentication Protocols: A Systematic Literature Review 11

Survey on IoT Multi-Factor Authentication Protocols: A Syste...

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11th International Symposium on Digital Forensics and Security, ISDFS 2023

作者： Mohammad Fneish, Zein Al-Abidin El-Hajj, Mohammad Samrouth, Khouloud Arab Open University Faculty of Computer Studies Beirut Lebanon University of Twente Faculty of Electrical Engineering Mathematics and Computer Science Enschede Netherlands

ISBN: (纸本)9798350336986

The Internet of Things (IoT) is a network of physical objects that is equipped with computers, electronics, applications, sensors, and other devices. As the number of IoT devices and apps increases, a range of security techniques should be applied to strengthen and enhance their immunity to cyber attacks as attackers have more capabilities and tools now focused on targeting the IoT networks. In this context, we study the nature of the Internet of Things infrastructure, especially some different Iot architecture layers, their security challenges and brief proposed solutions. For different IoT layers, authentication is a main security aspect affecting each layer separately. We provide a systematic literature review (SLR) methodology to locate existing multi-factor authentication protocols in the literature and evaluate their effectiveness. Then we provide an analysis of available multi-factor authentication protocols that are intended for IoT environments. Then some recommendations were provided based on the analysis. In conclusion, since the large range of IoT devices are resource-and power-limited, the nature of IoT settings requires lightweight protocols. Any of those proposed protocols can call for ongoing follow-up in the future to figure out upcoming weaknesses. © 2023 IEEE.

关键词： Authentication

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Optimized Deep Learning Approach for Efficient Diabetic Retinopathy Classification Combining VGG16-CNN

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computers, Materials & Continua 2023年第11期77卷 1855-1872页

作者： Heba M.El-Hoseny Heba F.Elsepae Wael A.Mohamed Ayman S.Selmy Department of Computer Science the Higher Future Institute for Specialized Technological StudiesObour11828Egypt Department of Electrical Engineering Benha Faculty of EngineeringBenha UniversityBenha13511Egypt

Diabetic retinopathy is a critical eye condition that,if not treated,can lead to vision *** methods of diagnosing and treating the disease are time-consuming and ***,machine learning and deep transfer learning(DTL)techniques have shown promise in medical applications,including detecting,classifying,and segmenting diabetic *** advanced techniques offer higher accuracy and *** Diagnosis(CAD)is crucial in speeding up classification and providing accurate disease ***,these technological advancements hold great potential for improving the management of diabetic *** study’s objective was to differentiate between different classes of diabetes and verify the model’s capability to distinguish between these *** robustness of the model was evaluated using other metrics such as accuracy(ACC),precision(PRE),recall(REC),and area under the curve(AUC).In this particular study,the researchers utilized data cleansing techniques,transfer learning(TL),and convolutional neural network(CNN)methods to effectively identify and categorize the various diseases associated with diabetic retinopathy(DR).They employed the VGG-16CNN model,incorporating intelligent parameters that enhanced its *** outcomes surpassed the results obtained by the auto enhancement(AE)filter,which had an ACC of over 98%.The manuscript provides visual aids such as graphs,tables,and techniques and frameworks to enhance *** study highlights the significance of optimized deep TL in improving the metrics of the classification of the four separate classes of *** manuscript emphasizes the importance of using the VGG16CNN classification technique in this context.

关键词： No diabetic retinopathy(NDR) convolution layers(CNV layers) transfer learning data cleansing convolutional neural networks a visual geometry group(VGG16)

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Dipper Throated Optimization for Detecting Black-Hole Attacks inMANETs

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computers, Materials & Continua 2023年第1期74卷 1905-1921页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

In terms of security and privacy,mobile ad-hoc network(MANET)continues to be in demand for additional debate and *** more MANET applications become data-oriented,implementing a secure and reliable data transfer protocol becomes a major concern in the ***,MANET’s lack of infrastructure,unpredictable topology,and restricted resources,as well as the lack of a previously permitted trust relationship among connected nodes,contribute to the attack detection burden.A novel detection approach is presented in this paper to classify passive and active black-hole *** proposed approach is based on the dipper throated optimization(DTO)algorithm,which presents a plausible path out of multiple paths for statistics transmission to boost MANETs’quality of service.A group of selected packet features will then be weighed by the DTO-based multi-layer perceptron(DTO-MLP),and these features are collected from nodes using the Low Energy Adaptive Clustering Hierarchical(LEACH)clustering *** is a powerful classifier and the DTO weight optimization method has a significant impact on improving the classification process by strengthening the weights of key features while suppressing the weights ofminor *** hybridmethod is primarily designed to combat active black-hole *** the LEACH clustering phase,however,can also detect passive black-hole *** effect of mobility variation on detection error and routing overhead is explored and evaluated using the suggested *** diverse mobility situations,the results demonstrate up to 97%detection accuracy and faster execution ***,the suggested approach uses an adjustable threshold value to make a correct conclusion regarding whether a node is malicious or benign.

关键词： Black-hole attack mobile ad-hoc network optimization dipper throated optimization

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Improving Energy Efficiency in Medical Edge Devices for ECG Feature Detection via Approximate Computing 5

Improving Energy Efficiency in Medical Edge Devices for ECG ...

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5th IEEE Eurasia Conference on IOT, Communication and engineering, ECICE 2023

作者： Nagatomo, Taiki Sato, Toshinori Fukuoka University Department of Electronics Engineering and Computer Science Graduate School of Engineering Fukuoka Japan Fukuoka University Department of Electronics Engineering and Computer Science Faculty of Engineering Fukuoka Japan

ISBN: (纸本)9798350314694

Improving the energy efficiency of battery-powered medical edge devices is a challenge. In this study, we explored electrocardiogram (ECG) feature detection using the Pan-Tompkins algorithm and integrated approximate computing techniques involving addition and multiplication into the process to enhance power efficiency. We adopted eight approximate multipliers from EvoApproxLib along with a Carry-Maskable Adder (CMA). We evaluated the impact of applying approximate computing on feature detection accuracy, as well as its influence on enhancing the power efficiency of the arithmetic circuits. When replacing just one of the arithmetic circuits with its approximate counterpart, the most power-efficient multiplier had dynamic power reduced by 48.3% in multiplications while maintaining a detection accuracy of 99.96%, while the CMA reduced dynamic power reduction by 95.2% while maintaining a detection accuracy 99.4%. When the CMA and approximate multiplication were applied, detection accuracy was maintained at 99.4%. In terms of power efficiency, the multiplier played a predominant role and the overall dynamic power consumption was reduced by 48.7%. © 2023 IEEE.

关键词： approximate computing ECG medical edge devices Pan-Tompkins algorithm signal processing

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Development of Dementia Care Training System Using AR and Large Language Model

Development of Dementia Care Training System Using AR and La...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Miyawaki, Tomoya Nishiura, Yuuki Fukuda, Ryouta Nakashima, Kazuto Kurazume, Ryo Graduate School of Information Science and Electrical Engineering Kyushu University Japan Kyushu University Faculty of Engineering Department of Electrical Engineering and Computer Science Japan Kyushu University Faculty of Information Science and Electrical Engineering Japan

ISBN: (纸本)9781665410205

We have developed HEARTS, a dementia care training system using augmented reality based on Humanitude. Humanitude is a multimodal comprehensive care technique for dementia, and has attracted attention as a method to reduce the burden on both caregivers and patients. However, the HEARTS developed so far could not evaluate 'speaking' skills based on the content of conversations among 'seeing,' 'touching,' and 'speaking,' all of which are fundamental skills in Humanitude. Therefore, we attempted a new quantitative evaluation of trainees' 'speaking' skills by estimating the emotional value of conversational content using GPT-4 named HEARTS 5. A survey of caregivers was conducted using the developed system and was well received by the participants. We also developed a HEARTS 5 conversational version based on HEARTS 5, with the addition of GPT-4 conversation generation and Azure Text-to-Speech. © 2024 IEEE.

关键词： Augmented reality

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