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Performance Analysis of Multi-Channel CR Enabled IoT Network with Better Energy Harvesting

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Computers, Materials & Continua 2022年第4期71卷 183-197页

作者： Afiya Kiran ahmad karim Yasser Obaid Alharbi Diaa Mohammed Uliyan Department of Computer Science BZ UniversityMultan6000Pakistan Department of Information Technology BZ UniversityMultanPakistan College of Computer Science and Engineering University of Ha’ilHailSaudi Arabia

Wireless Sensor Networks(WSNs)can be termed as an autoconfigured and infrastructure-less wireless networks to monitor physical or environmental conditions,such as temperature,sound,vibration,pressure and motion *** may comprise thousands of Internet of Things(IoT)devices to sense and collect data from its surrounding,process the data and take an automated and mechanized *** the other side the proliferation of these devices will soon cause radio spectrum ***,to facilitate these networks,we integrate Cognitive Radio(CR)functionality in these *** can sense the unutilized spectrum of licensed users and then use these empty bands when *** order to keep the IoT nodes functional all time,continuous energy is *** this reason the energy harvested techniques are preferred in IoT *** it is preferred to harvest Radio Frequency(RF)energy in the *** this paper a region based multi-channel architecture is *** which the coverage area of primary node is divided as Energy Harvesting Region and Communication *** Secondary User(SU)that are the licensed user is IoT enabled with Cognitive Radio(CR)techniques so we call it CR-enabled IoT node/device and is encouraged to harvest energy by utilizing radio frequency *** harvest energy efficiently and to reduce the energy consumption during sensing,the concept of overlapping region is given that supports to sense multiple channels simultaneously and help the SU to find best channel for transmitting data or to harvest energy from the ideal *** the experimental analysis,it is proved that SU can harvest more energy in overlapping region and this architecture proves to consume less energy during data transmission as compared to single *** also show that channel load can be highly reduced and channel utilization is proved to be more ***,this proves the proposed architecture cost-effective and energy-efficient.

关键词： Wireless sensor network multi-channel sensing energy harvesting cognitive radio IoT network

A Learning Based Brain Tumor Detection System

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Computers, Materials & Continua 2019年第6期59卷 713-727页

作者： Sultan Noman Qasem Amar Nazar Attia Qamar Shahaboddin Shamshirband ahmad karim Computer Science Department College of Computer and information SciencesAl Imam Mohammad Ibn Saud Islamic University(IMSIU)RiyadhSaudi Arabia Computer Science Department Faculty of Applied SciencesTaiz UniversityTaizYemen Department of Computer Science Bahauddin Zakariya UniversityPakistan Department of Information Technology Bahauddin Zakariya UniversityPakistan Department for Management of Science and Technology Development Ton Duc Thang UniversityHo Chi Minh CityVietnam Faculty of Information Technology Ton Duc Thang UniversityHo Chi Minh CityVietnam

Brain tumor is one of the most dangerous disease that causes due to uncontrollable and abnormal cell *** this paper,we have used MRI brain scan in comparison with CT brain scan as it is less harmful to detect brain *** considered watershed segmentation technique for brain tumor *** proposed methodology is divided as follows:pre-processing,computing foreground applying watershed,extract and supply features to machine learning ***,this study is tested on big data set of images and we achieved acceptable accuracy from K-NN classification algorithm in detection of brain tumor.

关键词： Magnetic resonance imaging brain tumor watershed segmentation K-NN classification

Detecting Android Botnet Applications Using Convolution Neural Network

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Computers, Materials & Continua 2023年第11期77卷 2123-2135页

作者： Mamona Arshad ahmad karim Salman Naseer Shafiq ahmad Mejdal Alqahtani Akber Abid Gardezi Muhammad Shafiq Jin-Ghoo Choi Department of Information Technology Bahauddin Zakariya UniversityMultan60000Pakistan Department of Information Technology University of the Punjab Gujranwala CampusGujranwala52250Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityP.O.Box 800Riyadh11421Saudi Arabia Department of Computer Science COMSATS University IslamabadIslamabad45550Pakistan Department of Information and Communication Engineering Yeungnam UniversityGyeongsan38541Korea

The exponential growth in the development of smartphones and handheld devices is permeated due to everyday activities i.e.,games applications,entertainment,online banking,social network sites,etc.,and also allow the end users to perform a variety of *** of activities,mobile devices attract cybercriminals to initiate an attack over a diverse range of malicious activities such as theft of unauthorized information,phishing,spamming,Distributed Denial of Services(DDoS),and malware *** applications are a type of harmful attack that can be used to launch malicious activities and has become a significant threat in the research area.A botnet is a collection of infected devices that are managed by a botmaster and communicate with each other via a command server in order to carry out malicious *** the rise in malicious attacks,detecting botnet applications has become more ***,it is essential to investigate mobile botnet attacks to uncover the security issues in severe financial and ethical damages caused by a massive coordinated command *** state of the art,various solutions were provided for the detection of botnet applications,but in general,the researchers suffer various techniques of machine learning-based methods with static features which are usually ineffective when obfuscation techniques are used for the detection of botnet *** this paper,we propose an approach by exploring the concept of a deep learning-based method and present a well-defined Convolutional Neural Network(CNN)*** the visualization approach,we obtain the colored images through byte code files of applications and perform an *** analysis of the results of an experiment,we differentiate the performance of the model from other existing research ***,our method outperforms with 94.34%accuracy,92.9%of precision,and 92%of recall.

关键词： CNN botnet applications machine learning image processing

Big data storage technologies： a survey

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Frontiers of Information Technology & Electronic Engineering 2017年第8期18卷 1040-1070页

作者： Aisha SIDDIQA ahmad karim Abdullah GANI Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur 50603 Malaysia Department of Information Technology Bahauddin Zakariya University Multan 60000 Pakistan

There is a great thrust in industry toward the development of more feasible and viable tools for storing fast-growing volume, velocity, and diversity of data, termed ＇big data＇. The structural shift of the storage mechanism from traditional data management systems to NoSQL technology is due to the intention of fulfilling big data storage requirements. However, the available big data storage technologies are inefficient to provide consistent, scalable, and available solutions for continuously growing heterogeneous data. Storage is the preliminary process of big data analytics for real-world applications such as scientific experiments, healthcare, social networks, and e-business. So far, Amazon, Google, and Apache are some of the industry standards in providing big data storage solutions, yet the literature does not report an in-depth survey of storage technologies available for big data, investigating the performance and magnitude gains of these technologies. The primary objective of this paper is to conduct a comprehensive investigation of state-of-the-art storage technologies available for big data. A well-defined taxonomy of big data storage technologies is presented to assist data analysts and researchers in understanding and selecting a storage mecha- nism that better fits their needs. To evaluate the performance of different storage architectures, we compare and analyze the ex- isling approaches using Brewer＇s CAP theorem. The significance and applications of storage technologies and support to other categories are discussed. Several future research challenges are highlighted with the intention to expedite the deployment of a reliable and scalable storage system.

关键词： Big data Big data storage NoSQL databases Distributed databases CAP theorem Scalability Consistency-partition resilience Availability-partition resilience

Review:Botnet detection techniques: review, future trends, and issue

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Journal of Zhejiang University-Science C(Computers and Electronics) 2014年第11期15卷 943-983页

作者： ahmad karim Rosli Bin SALLEH Muhammad SHIRAZ Syed Adeel Ali SHAH Irfan AWAN Nor Badrul ANUAR Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur Malaysia Department of Computer Science University of Bradford Bradford BD7 1DP UK

In recent years, the Internet has enabled access to widespread remote services in the distributed computing environment; however, integrity of data transmission in the distributed computing platform is hindered by a number of security issues. For instance, the botnet phenomenon is a prominent threat to Internet security, including the threat of malicious codes. The botnet phenomenon supports a wide range of criminal activities, including distributed denial of service(DDoS) attacks, click fraud, phishing, malware distribution, spam emails, and building machines for illegitimate exchange of information/materials. Therefore, it is imperative to design and develop a robust mechanism for improving the botnet detection, analysis, and removal process. Currently, botnet detection techniques have been reviewed in different ways; however, such studies are limited in scope and lack discussions on the latest botnet detection techniques. This paper presents a comprehensive review of the latest state-of-the-art techniques for botnet detection and figures out the trends of previous and current research. It provides a thematic taxonomy for the classification of botnet detection techniques and highlights the implications and critical aspects by qualitatively analyzing such techniques. Related to our comprehensive review, we highlight future directions for improving the schemes that broadly span the entire botnet detection research field and identify the persistent and prominent research challenges that remain open.

关键词： Botnet detection Anomaly detection Network security Attack Defense Taxonomy

Discovering optimal features using static analysis and a genetic search based method for Android malware detection

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Frontiers of Information Technology & Electronic Engineering 2018年第6期19卷 712-736页

作者： ahmad FIRDAUS Nor Badrul ANUAR ahmad karim Mohd Faizal Ab RAZAK Department of Computer System and Technology University of Malaya Faculty of Computer System & Software Engineering University Malaysia Pahang Department of Information Technology Bahauddin Zakariya University

Mobile device manufacturers are rapidly producing miscellaneous Android versions worldwide. Simultaneously, cyber criminals are executing malicious actions, such as tracking user activities, stealing personal data, and committing bank fraud. These criminals gain numerous benefits as too many people use Android for their daily routines, including important communications. With this in mind, security practitioners have conducted static and dynamic analyses to identify malware. This study used static analysis because of its overall code coverage, low resource consumption, and rapid processing. However, static analysis requires a minimum number of features to efficiently classify malware. Therefore, we used genetic search（GS）, which is a search based on a genetic algorithm（GA）, to select the features among 106 strings. To evaluate the best features determined by GS, we used five machine learning classifiers, namely, Na？ve Bayes（NB）, functional trees（FT）, J48, random forest（RF）, and multilayer perceptron（MLP）. Among these classifiers, FT gave the highest accuracy（95%） and true positive rate（TPR）（96.7%） with the use of only six features.

关键词： Genetic algorithm Static analysis Android Malware Machine learning

Non-invasive splenic parameters of portal hypertension:Assessment and utility

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World Journal of Hepatology 2020年第11期12卷 1055-1066页

作者： Ayesha karim ahmad Sebastiana Atzori James Maurice Simon D Taylor-Robinson Adrian KP Lim Liver Unit Department of DigestionMetabolism&ReproductionFaculty of MedicineImperial College LondonLondon W21NYUnited Kingdom Liver Unit Department of DigestionMetabolism&ReproductionImperial College LondonLondon W21NYUnited Kingdom Liver Unit and Imaging Department of DigestionMetabolism&ReproductionImperial College LondonLondon W21NYUnited Kingdom

BACKGROUND Portal hypertension is a major complication of cirrhosis that is associated with significant morbidity and *** present gold-standard method to risk stratify and observe cirrhosis patients with portal hypertension is hepatic venous pressure gradient measurement or ***,these methods are invasive,carry a risk of complications and are associated with significant patient ***,non-invasive splenic parameters are of clinical interest as potential useful markers in determining the presence of portal ***,diagnostic accuracy and reproducibility remains *** To assess the diagnostic accuracy of spleen stiffness,area and diameter in predicting the presence of portal *** Of 50 patients with varying liver disease pathologies were prospectively recruited from the ***’s Hospital Liver Unit in London;25 with evidence of portal hypertension and 25 with no evidence of portal *** stiffness,spleen stiffness,spleen diameter and spleen area were measured using the Philips Affiniti 70 elastography point quantification point shear wave elastography *** aspartate aminotransferase-to-platelet-ratio-index(APRI)score was also *** measures,univariate and multivariate logistic regression were used to evaluate demographic,clinical and elastography *** correlation coefficient was used to determine the reproducibility of splenic area and *** On univariate and individual performance,platelet count[area under the receiver operating characteristic(AUROC)0.846,P value<0.001],spleen area(AUROC 0.828,P value=0.002)and APRI score(AUROC 0.827,P value<0.001)were the most accurate variables in identifying the presence of portal *** multivariate logistic regression models constructed,the combination of spleen area greater than 57.90 cm2 and platelet count less than 126×10^9 had 63.2%sensitivity and 100%specificity

关键词： Portal hypertension Esophageal varices Point shear wave elastography Spleen stiffness Spleen area Non-invasive

Steel Surface Defect Detection Using Learnable Memory Vision Transformer

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Computers, Materials & Continua 2025年第1期82卷 499-520页

作者： Syed Tasnimul karim Ayon Farhan Md.Siraj Jia Uddin Department of Computer Science and Engineering BRAC UniversityDhaka1212Bangladesh Department of AI and Big Data Endicott CollegeWoosong UniversityDaejeon34606Republic of Korea

This study investigates the application of Learnable Memory Vision Transformers(LMViT)for detecting metal surface flaws,comparing their performance with traditional CNNs,specifically ResNet18 and ResNet50,as well as other transformer-based models including Token to Token ViT,ViT withoutmemory,and Parallel *** awidely-used steel surface defect dataset,the research applies data augmentation and t-distributed stochastic neighbor embedding(t-SNE)to enhance feature extraction and *** techniques mitigated overfitting,stabilized training,and improved generalization *** LMViT model achieved a test accuracy of 97.22%,significantly outperforming ResNet18(88.89%)and ResNet50(88.90%),aswell as the Token to TokenViT(88.46%),ViT without memory(87.18),and Parallel ViT(91.03%).Furthermore,LMViT exhibited superior training and validation performance,attaining a validation accuracy of 98.2%compared to 91.0%for ResNet 18,96.0%for ResNet50,and 89.12%,87.51%,and 91.21%for Token to Token ViT,ViT without memory,and Parallel ViT,*** findings highlight the LMViT’s ability to capture long-range dependencies in images,an areawhere CNNs struggle due to their reliance on local receptive fields and hierarchical feature *** additional transformer-based models also demonstrate improved performance in capturing complex features over CNNs,with LMViT excelling particularly at detecting subtle and complex defects,which is critical for maintaining product quality and operational efficiency in industrial *** instance,the LMViT model successfully identified fine scratches and minor surface irregularities that CNNs often *** study not only demonstrates LMViT’s potential for real-world defect detection but also underscores the promise of other transformer-based architectures like Token to Token ViT,ViT without memory,and Parallel ViT in industrial scenarios where complex spatial relationships are *** research m

关键词： Learnable Memory Vision Transformer(LMViT) Convolutional Neural Networks(CNN) metal surface defect detection deep learning,computer vision image classification learnable memory gradient clipping label smoothing t-SNE visualization

The Prospects of Blue Economy to Promote Bangladesh into a Middle-Income Country

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Open Journal of Marine Science 2018年第3期8卷 355-369页

作者： Md. Monjur Hasan B. M. Sajjad Hossain Md. Jobaer Alam K. M. Azam Chowdhury ahmad Al karim Nuruddin Md. Khaled Chowdhury School of Law and Political Science Ocean University of China Qingdao China Department of Law Gono Bishwabidyalay (University) Savar Dhaka Bangladesh University of Dhaka Dhaka Bangladesh Department of Oceanography University of Dhaka Dhaka Bangladesh College of Oceanic and Atmospheric Sciences Ocean University of China Qingdao China Ministry of Foreign Affairs Government of the People’s Republic of Bangladesh Dhaka Bangladesh Department of Geology University of Dhaka Dhaka Bangladesh Department of Geosciences Texas Tech University Lubbock USA

This paper draws attention to the prospects of sea-based economy to promote Bangladesh in a middle-income country through the sustainable use of marine resources. About three-fourths of the earth is covered by the seas. It plays the vital role in two important functions from ancient time known as the means of communication and the source of huge living and non-living natural resources. At present, the countries are becoming very much concerned about their marine resources to resolve many of the present and future challenges of their economies. Generally for Bangladesh, ocean is contributing a significant role to its overall socio-economic progress through rising up the economic activities across the country and especially to the coastal zone at southern part. This paper investigates how much Bangladesh is capable to take of or handle the challenges to become a middle income country through the Sustainable Development Goals (SDGs). In addition, it has attempted with a closer-look to find out the barriers or limitations of these activities from different angles if exist.

关键词： Blue Economy Sustainable Development Marine Resources Middle-Income Country Bangladesh