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Automatic Finding of Brain-Tumour Group Using CNN Segmentation and Moth-Flame-Algorithm,Selected Deep and Handcrafted Features

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Computers, Materials & Continua 2024年第5期79卷 2585-2608页

作者： Imad Saud Al Naimi syed Alwee Aljunid syed Junid Muhammad lmran ahmad K.Suresh Manic Faculty of Electronic Engineering Technology University Malaysia Perils(UniMAP)Pauh Putra CampusArauPerlis02600Malaysia Department of Electrical and Communication Engineering National University of Science and TechnologyAL HailAL Seeb130Sultanate of Oman Department of Research and Innovation Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(SIMATS)ChennaiTamiNadu602105India

Augmentation of abnormal cells in the brain causes brain tumor(BT),and early screening and treatmentwill reduce its harshness in ***’s clinical level screening is usually performed with Magnetic Resonance Imaging(MRI)due to its multi-modality *** overall aims of the study is to introduce,test and verify an advanced image processing technique with algorithms to automatically extract tumour sections from brain MRI scans,facilitating improved *** research intends to devise a reliable framework for detecting the BT region in the twodimensional(2D)MRI slice,and identifying its class with improved *** methodology for the devised framework comprises the phases of:(i)Collection and resizing of images,(ii)Implementation and Segmentation of Convolutional Neural Network(CNN),(iii)Deep feature extraction,(iv)Handcrafted feature extraction,(v)Moth-Flame-Algorithm(MFA)supported feature reduction,and(vi)Performance *** study utilized clinical-grade brain MRI of BRATS and TCIA datasets for the *** framework segments detected the glioma(low/high grade)and glioblastoma class *** work helped to get a segmentation accuracy of over 98%with VGG-UNet and a classification accuracy of over 98%with the VGG16 *** study has confirmed that the implemented framework is very efficient in detecting the BT in MRI slices with/without the skull section.

关键词： Brain tumour VGG-UNet VGG16 moth-flame-algorithm classification

Classification and Comprehension of Software Requirements Using Ensemble Learning

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Computers, Materials & Continua 2024年第8期80卷 2839-2855页

作者： Jalil Abbas Arshad ahmad syed Muqsit Shaheed Rubia Fatima Sajid Shah Mohammad Elaffendi Gauhar Ali School of Computer Science and Technology Anhui UniversityHefei230039China School of Computing Sciences Pak Austria FachhochschuleInstitute of Applied Sciences and TechnologyHaripur22620Pakistan Department of Computer Science and IT University of LahoreLahore55150Pakistan Department of Computer Science Emerson UniversityPunjabMultan60000Pakistan EIAS(Emerging Intelligent Autonomous Systems)Data Science Lab Prince Sultan UniversityRiyad12435Saudi Arabia

The software development process mostly depends on accurately identifying both essential and optional ***,user needs are typically expressed in free-form language,requiring significant time and human resources to translate these into clear functional and non-functional *** address this challenge,various machine learning(ML)methods have been explored to automate the understanding of these requirements,aiming to reduce time and human ***,existing techniques often struggle with complex instructions and large-scale *** our study,we introduce an innovative approach known as the Functional and Non-functional Requirements Classifier(FNRC).By combining the traditional random forest algorithm with the Accuracy Sliding Window(ASW)technique,we develop optimal sub-ensembles that surpass the initial classifier’s accuracy while using fewer *** results demonstrate that our FNRC methodology performs robustly across different datasets,achieving a balanced Precision of 75%on the PROMISE dataset and an impressive Recall of 85%on the CCHIT *** datasets consistently maintain an F-measure around 64%,highlighting FNRC’s ability to effectively balance precision and recall in diverse *** findings contribute to more accurate and efficient software development processes,increasing the probability of achieving successful project outcomes.

关键词： Ensemble learning machine learning non-functional requirements requirement engineering accuracy sliding window

Developing Lexicons for Enhanced Sentiment Analysis in Software Engineering:An Innovative Multilingual Approach for Social Media Reviews

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Computers, Materials & Continua 2024年第5期79卷 2771-2793页

作者： Zohaib ahmad Khan Yuanqing Xia Ahmed Khan Muhammad Sadiq Mahmood Alam fuad AAwwad Emad A.A.Ismail School of Automation Beijing Institute of TechnologyBeijing100081China Department of Computer Science and Technology University of Science and Technology BannuKPKBannu28100Pakistan School of Computer Science and Engineering Central South UniversityChangsha410083China Department of Quantitative Analysis College of Business AdministrationKing Saud UniversityP.O.Box 71115Riyadh11587Saudi Arabia

Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.

关键词： Emotional assessment regional dialects SentiWordNet naive bayesian technique lexicons software engineering user feedback

Enhancing Mild Cognitive Impairment Detection through Efficient Magnetic Resonance Image Analysis

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Computers, Materials & Continua 2024年第8期80卷 2081-2098页

作者： Atif Mehmood Zhonglong Zheng Rizwan Khan ahmad Al Smadi Farah Shahid Shahid Iqbal Mutasem K.Alsmadi Yazeed Yasin Ghadi syed Aziz Shah Mostafa M.Ibrahim Department of Computer Science and Technology Zhejiang Normal UniversityJinhua321002China Zhejiang Institute of Photoelectronics&Zhejiang Institute for Advanced Light Source Zhejiang Normal UniversityJinhua321004China Department of Data Science and Artificial Intelligence Zarqa UniversityZarqa13100Jordan School of Chemical and Environmental Engineering College of ChemistryChemical Engineering andMaterials ScienceSoochow UniversitySuzhou215123China Department of Management Information Systems College of Applied Studies and Community ServiceImam Abdulrahman Bin Faisal UniversityDammam32244Saudi Arabia Department of Computer Science Al Ain UniversityAbu Dhabi999041United Arab Emirates Department of Electrical Engineering Faculty of EngineeringMinia UniversityMinia61519Egypt Healthcare Technology and Innovation Theme Faculty Research Centre for Intelligent HealthcareCoventry UniversityCoventryCV48BWUK

Neuroimaging has emerged over the last few decades as a crucial tool in diagnosing Alzheimer’s disease(AD).Mild cognitive impairment(MCI)is a condition that falls between the spectrum of normal cognitive function and ***,previous studies have mainly used handcrafted features to classify MCI,AD,and normal control(NC)*** paper focuses on using gray matter(GM)scans obtained through magnetic resonance imaging(MRI)for the diagnosis of individuals with MCI,AD,and *** improve classification performance,we developed two transfer learning strategies with data augmentation(i.e.,shear range,rotation,zoom range,channel shift).The first approach is a deep Siamese network(DSN),and the second approach involves using a cross-domain strategy with customized *** performed experiments on the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset to evaluate the performance of our proposed *** experimental results demonstrate superior performance in classifying the three binary classification tasks:NC ***,NC ***,and MCI ***,we achieved a classification accuracy of 97.68%,94.25%,and 92.18%for the three cases,*** study proposes two transfer learning strategies with data augmentation to accurately diagnose MCI,AD,and normal control individuals using GM *** findings provide promising results for future research and clinical applications in the early detection and diagnosis of AD.

关键词： Alzheimer’s disease mild cognitive impairment normal control transfer learning classification augmentation

Unraveling the therapeutic mechanisms of myristic acid and luteolin 7-rutinoside in oral cancer: insights from network pharmacology and molecular docking analysis

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Pharmacology Discovery 2024年第2期4卷 1-9页

作者： Ansari Vikhar Danish ahmad Misba Ruhi syed Ayaz Ali Qazi Yasar Mohd.Mukhtar Khan Subur W Khan Mohammed Imran Anees Y.B.Chavan College of Pharmacy Dr.Babasaheb Ambedkar Marathwada UniversityAurangabad 431001India Maulana Azad College Dr.Babasaheb Ambedkar Marathwada UniversityAurangabad 431001India

Background:The compound Luteolin-7-rutinoside(L7R)is a flavone derivative of luteolin,predominantly identified in plant species belonging to the families ***,Myristic acid is characterized by its structure as a 14-carbon,unsaturated fatty *** this investigation,we endeavor to elucidate the putative mechanisms underlying the therapeutic effects of Myristic Acid and Luteolin 7-rutinoside in the context of oral cancer treatment,employing network pharmacology coupled with molecular docking ***:The protein targets of Myristic Acid and Luteolin 7-rutinoside were identified through a search on the Swiss Target ***,a compound-target network was constructed using Cytoscape *** associated with OC were retrieved from the OMIM and GeneCards *** overlap between compound targets and OC-related targets was determined,and the resulting shared targets were subjected to protein-protein interaction(PPI)network analysis using the STRING ***,gene ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses were conducted on the identified *** docking were performed to investigate the interactions between the core target and the active ***:The component target network comprises 103 nodes and 102 *** the proteins in the protein-protein interaction(PPI)network,those with higher degrees are TNF,PPARG,and *** through Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathways indicates that the treatment of OC with Myristic Acid and Luteolin 7-rutinoside primarily involves the regulation of miRNA transcription and inflammatory *** identified signaling pathways include Pathways in cancer,PPAR signaling pathway,EGFR signaling pathway,and TNF signaling *** docking studies reveal that Luteolin 7-rutinoside and Myristic acid exhibit higher affinity towards TNF,PPARG,TP53,and ***:This study

关键词： myristic acid luteolin 7-rutinoside network pharmacology oral cancer molecular docking

Magnetoelectric effect in multiferroic NdMn2O5

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Chinese Physics B 2017年第1期26卷 526-529页

作者： syed Hamad Bukhari Javed ahmad Department of Physics Bahauddin Zakariya University Multan 60800 Pakistan

We have measured the dielectric constant for NdMn2O5 in an external magnetic field to map out the magnetoelectric phase diagram. The phase diagram corresponds well with the previously reported data of neutron diffraction and magnetic susceptibility. Our main finding is the observation of a dielectric anomaly in the low temperature phase with a strong magnetoelectric effect, which is attributed to the independent Nd^3＋ ordering. Moreover, the absence of the dielectric anomaly in the paramagnetic phase is discussed, keeping in view the exchange interaction and its dependence on the rareearth R^3＋ ionic radius.

关键词： sol–gel synthesis dielectric constant magnetoelectric effect phase transition

Carbon stocks of different land uses in the Kumrat valley, Hindu Kush Region of Pakistan

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Journal of Forestry Research 2015年第1期26卷 57-64页

作者： Adnan ahmad syed Moazzam Nizami Shaheed Benazir Bhutto University Arid Agriculture University

Changes in land use cover, particularly from forest to agriculture, is a major contributing factor in increasing carbon dioxide（CO2） level in the *** satellite images of 1999 and 2011, land use and land use changes in the Kumrat valley KPK, Pakistan, were determined： a net decrease of 11.56 and 7.46 % occurred in forest and rangeland, while 100 % increase occurred in agriculture land（AL）. Biomass in different land uses,forest land（FL）, AL, and range land（RL） was determined by field inventory. From the biomass data, the amount of carbon was calculated, considering 50 % of the biomass as carbon. Soil carbon was also determined to a depth of 0–15and 16–30 cm. The average carbon stocks（C stocks） in all land uses ranged from 28.62 ± 13.8 t ha-1in AL to486.6 ± 32.4 t ha-1in pure Cedrus deodara forest. The results of the study confirmed that forest soil and vegetation stored the maximum amount of carbon followed by RL. Conversion of FL and RL to AL not only leads to total loss of about 56 %（from FL conversion） and 37 %（RL conversion） of soil carbon in the last decades but also the loss of a valuable carbon sink. In order to meet the emissions reduction obligations of the Kyoto Protocol, Conservation of forest and RL in the mountainous regions of the Hindu Kush will help Pakistan to meet its emissions reduction goals under the Kyoto Protocol.

关键词： Hindu Kush Kumrat valley Land uses Biomass Carbon stocks

Machine learning empowered COVID-19 patient monitoring using non-contact sensing:An extensive review

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Journal of Pharmaceutical Analysis 2022年第2期12卷 193-204页

作者： Umer Saeed syed Yaseen Shah Jawad ahmad Muhammad Ali Imran Qammer H.Abbasi syed Aziz Shah Research Centre for Intelligent Healthcare Coventry UniversityCoventryCV15FBUK School of Computing Engineering and Built EnvironmentGlasgow Caledonian UniversityGlasgowG40BAUK School of Computing Edinburgh Napier UniversityEdinburghEH114BNUK James Watt School of Engineering University of GlasgowGlasgowG128QQUK

The severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),which caused the coronavirus disease 2019(COVID-19)pandemic,has affected more than 400 million people *** the recent rise of new Delta and Omicron variants,the efficacy of the vaccines has become an important *** goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions,particularly for healthcare *** this paper,we discuss the current literature on invasive/contact and non-invasive/noncontact technologies(including Wi-Fi,radar,and software-defined radio)that have been effectively used to detect,diagnose,and monitor human activities and COVID-19 related symptoms,such as irregular *** addition,we focused on cutting-edge machine learning algorithms(such as generative adversarial networks,random forest,multilayer perceptron,support vector machine,extremely randomized trees,and k-nearest neighbors)and their essential role in intelligent healthcare ***,this study highlights the limitations related to non-invasive techniques and prospective research directions.

关键词： Artificial intelligence Non-invasive healthcare Machine learning Non-contact sensing COVID-19

Fractional Processing Based Adaptive Beamforming Algorithm

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Computers, Materials & Continua 2023年第7期76卷 1065-1084页

作者： syed Asghar Ali Shah Tariqullah Jan syed Muslim Shah Ruhul Amin Khalil ahmad Sawalmeh Muhammad Anan Department of Electrical Engineering University of Engineering and TechnologyPeshawar25120Pakistan Department of Electrical Engineering Capital University of Science and TechnologyIslamabad44000Pakistan Department of Computer Science Northern Border UniversityArar91431Saudi Arabia Department of Software Engineering Alfaisal UniversityRiyadh11533Saudi Arabia

Fractional order algorithms have shown promising results in various signal processing applications due to their ability to improve performance without significantly increasing *** goal of this work is to inves-tigate the use of fractional order algorithm in the field of adaptive beam-forming,with a focus on improving performance while keeping complexity *** effectiveness of the algorithm will be studied and evaluated in this *** this paper,a fractional order least mean square(FLMS)algorithm is proposed for adaptive beamforming in wireless applications for effective utilization of *** algorithm aims to improve upon existing beam-forming algorithms,which are inefficient in performance,by offering faster convergence,better accuracy,and comparable computational *** FLMS algorithm uses fractional order gradient in addition to the standard ordered gradient in weight *** derivation of the algorithm is provided and supported by mathematical convergence *** is evaluated through simulations using mean square error(MSE)minimization as a metric and compared with the standard LMS algorithm for various *** results,obtained through Matlab simulations,show that the FLMS algorithm outperforms the standard LMS in terms of convergence speed,beampattern accuracy and scatter *** outperforms LMS in terms of convergence speed by 34%.From this,it can be concluded that FLMS is a better candidate for adaptive beamforming and other signal processing applications.

关键词： Adaptive beamforming adaptive array fractional processing least mean square fractional least mean square