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

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

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Selectivity of Three Miticides to Spider Mite Predator,Phytoseius plumifer(Acari:Phytoseiidae) Under Laboratory Conditions

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Agricultural Sciences in China 2009年第3期8卷 326-331页

作者： ahmad Nadimi karim Kamali Masoud Arbabi Fateme Abdoli Department of Entomology Tarbiat Modares University Department of Agricultural Research Zoology Iranian Plant Protection Research Institute Department of Biology Guilan University

The predatory mite, Phytoseius plumifer （Canestrini ＆ Fanzago）, is one of the most abundant natural enemies and efficient predator of phytophagous mites in Iran. The miticides hexythiazox （Nisorun, EC 10%）, fenpyroximate （Ortus, SC 5%）, and abamectin （Vertimec, EC 1.8%） were tested in the laboratory for their side effects on P. plumifer. The miticides were applied at the highest field recommended concentration （N） to detached leaves using a Potter Tower to deposit 2 mg spray solution per cm2. Percent predator mortality was evaluated from the protonymph up to the adult stage including the first five days of the oviposition period. Analysis of data showed that the total effect values of hexythiazox were below the lower threshold, and thus it could be considered to be a harmless miticide to P. plumifer. In contrast, the total effects of fenpyroximate and abamectin were found harmful to the predatory mite at the highest field recommended concentrations.

关键词： Phytoseius plumifer selectivity side effect miticide predatory mite

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The UAE’s energy system and GHG emissions: pathways to achieving national goals by 2050

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Clean Energy 2023年第5期7卷 962-980页

作者： Ammar Hummieda ali Bouabid karim Moawadand ahmad Mayyas Engineering Systems and Management Khalifa UniversityAbu DhabiUAE Institute of Education Sciences Mohammed VI Polytechnic UniversityRabatMorocco

In recent years,the United Arab Emirates(UAE)has developed strategies to increase renewable power generation and reduce emis-sions to net zero by *** generation and energy-intensive industries(EII)have the largest potentials for emission ***,an up-to-date inventory of greenhouse gas emissions and a study of the pathways to achieving the 2050 targets are *** study focuses on power production and EII(aluminium,steel and cement).The structure of these sectors is modelled and simulated up to 2050 using a system dynamics(SD)*** SD model is validated to reflect the real-world state of the system using the emissions inventory projections as reference *** mitigation policies are considered in the selected sectors and four policy scenarios were *** results show that implementing the Energy Strategy 2050 in the power sector can result in a reduction of 63.5%in emissions in that sector,which translates into a reduction of 33.5%overall by ***,implementing all identified mitigation strategies to full utilization in EII yields a 94%reduction in that sector,which translates into a 78%reduction *** the aluminium industry yields the highest emissions reductions,followed by power produc-tion,then cement and finally *** the best-case scenario,22.1%of the business-as-usual emissions are still released and further decarbonization—mainly in the power sector—will be *** is achievable given the trajectory of the UAE’s successful nuclear energy programme and the prospect of utilizing carbon capture,utilization and storage even further.

关键词： climate change emissions UAE energy strategy energy-intensive industry net zero

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Optimization of Head Cluster Selection in WSN by Human-Based Optimization Techniques

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Computers, Materials & Continua 2022年第9期72卷 5643-5661页

作者： Hajer Faris Musaria karim Mahmood Osama ahmad alomari Ashraf Elnagar Department of Electrical&Electronics Engineering Istanbul Gelisim UniversityIstanbul34310Turkey Department of Energy Systems Engineering Ankara Yildirim Beyazit UniversityAnkara06010Turkey MLalP Research Group University of SharjahSharjah27272UAE Department of Computer Science University of SharjahSharjah27272UAE

Wireless sensor networks(WSNs)are characterized by their ability to monitor physical or chemical phenomena in a static or dynamic location by collecting data,and transmit it in a collaborative manner to one or more processing centers wirelessly using a routing *** dissipation is one of the most challenging issues due to the limited power supply at the sensor *** routing protocols are large consumers of energy,as they represent the main source of energy cost through data exchange *** hierarchical routing algorithms are known for their good performance in energy conservation during active data exchange in *** most common of this type of protocol is the Low-Energy Adaptive Clustering Hierarchy(LEACH),which suffers from the problem of the pseudo-random selection of cluster head resulting in large power *** critical issue can be addressed by using an optimization algorithm to improve the LEACH cluster heads selection process,thus increasing the network *** paper proposes the LEACH-CHIO,a centralized cluster-based energyaware protocol based on the Coronavirus Herd Immunity Optimizer(CHIO)*** is a newly emerging human-based optimization algorithm that is expected to achieve significant improvement in the LEACH cluster heads selection ***-CHIO is implemented and its performance is verified by simulating different wireless sensor network scenarios,which consist of a variable number of nodes ranging from 20 to *** evaluate the algorithm performances,three evaluation indicators have been examined,namely,power consumption,number of live nodes,and number of incoming *** simulation results demonstrated the superiority of the proposed protocol over basic LEACH protocol for the three indicators.

关键词： WSN LEACH coronavirus herd immunity optimizer cluster head selection

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A Learning Model to Detect Android C&C Applications Using Hybrid Analysis

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Computer Systems Science & Engineering 2022年第12期43卷 915-930页

作者： Attia Qammar ahmad karim Yasser alharbi Mohammad alsaffar Abdullah alharbi Department of Information Technology Bahauddin Zakariya UniversityMultan60000Pakistan College of Computer Science and Engineering University of HailHa’il81451Saudi Arabia

Smartphone devices particularly Android devices are in use by billions of people everywhere in the ***,this increasing rate attracts mobile botnet attacks which is a network of interconnected nodes operated through the command and control(C&C)method to expand malicious *** present,mobile botnet attacks launched the Distributed denial of services(DDoS)that causes to steal of sensitive data,remote access,and spam generation,***quently,various approaches are defined in the literature to detect mobile botnet attacks using static or dynamic *** this paper,a novel hybrid model,the combination of static and dynamic methods that relies on machine learning to detect android botnet applications is ***,results are evaluated using machine learning *** Random Forest(RF)classifier outperform as compared to other ML techniques i.e.,Naïve Bayes(NB),Support Vector Machine(SVM),and Simple Logistic(SL).Our proposed framework achieved 97.48%accuracy in the detection of botnet ***,some future research directions are highlighted regarding botnet attacks detection for the entire community.

关键词： Android botnet botnet detection hybrid analysis machine learning classifiers mobile malware

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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

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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

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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

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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

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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

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