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RISS 인기검색어
- 검색키워드
- 저자 : Muhammad Ubaidullah (검색결과 3 건)
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Evaluation of Various Heuristics Techniques for Home Energy Management Using Smart Grid
Taher M. Ghazal,Shahan Yamin Siddiqui,Muhammad Ubaidullah,Hafiz Muhammad Usama,Sidra Khan,Muhammad Adnan Khan 한국차세대컴퓨팅학회 2022 한국차세대컴퓨팅학회 학술대회 Vol.2022 No.10
Electric energy is the basic need for human survival on this earth as these needs increase with the rapid increase in population. It’s become a challenge to manage home energy with the current situation. Smart grid provided different techniques to overcome these challenges to meet the need. This paper presents the result of the different optimization techniques that give the best performance in reducing cost, PAR, and user discomfort. Based on results the best result techniques are also combined to make a hybrid model for more accuracy. This paper not only describes optimization techniques but also the limitations and features of these techniques.
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Using Growth and Ionic Contents of Wheat Seedlings as Rapid Screening Tool for Salt Tolerance
Wajid Mahboob,Muhammad Athar Khan,Muhammad Ubaidullah Shirazi,Saba Mumtaz,Aisha Shereen 한국작물학회 2018 Journal of crop science and biotechnology Vol.21 No.2
High germination percentage with vigorous early growth is preferred for harvesting good wheat stand under saline soils. Therefore, an attempt for rapid screening of wheat genotypes for salt tolerance was made in this study. Eleven wheat genotypes including salt tolerant check Kiran-95were subjected to salinity (120 and 160 mMNaCl) along with non-saline,control. Results showed a gradual decrease in seed germination and restricted seedling growth in tested wheat genotypes inㅡresponse to increasing NaCl concentration in nutrient solution. Among the genotypes, NIA-AS-14-6 and NIA-AS-14-7 exhibited more sensitivity towards the salt stress at the germination stage but NIA-AS-14-6 performed quite satisfactorily later on at the seedling stage. Wheat genotypes NIA-AS-14-2, NIA-AS-14-4, NIA-AS-14-5, NIA-AS-14-10, and Kiran-95 showed better performance in term of root-shoot length, plant biomasses (fresh and dry), K+:Na+ ratio with least Na+ content, and high accumulation of K+ at higher levels of NaCl stress. On the basis of overall results, the categorization of genotypes was carried out as sensitive, moderately tolerant, and tolerant. Wheat genotypes NIA-AS-14-2, NIA-AS-14-4, NIA-AS-14-5, NIA-AS-14-10, and Kiran-95 grouped as tolerant, moderately salt tolerant group comprised of NIA-AS-14-1, NIA-AS-14-3, NIA-AS-14-6, and NIA-AS-14-8, whereas, NIA-AS-14-7 and NIA-AS-14-9 were found sensitive to salt stress. Principal component analysis revealed that components I and II contributed 70 and 16.5%, respectively. All growth parameters are associated with each other except RDW. In addition to growth traits, low Na+ and improved K+ content with better K+:Na+ ratio may be used for screening of salt tolerance in wheat as potential physiological criteria.
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Cloud Security Issues Detection Using Fuzzy Logic
Taher M. Ghazal,Shahan Yamin Siddiqui,Muhammad Ubaidullah,Hafiz Muhammad Usama,Ali Younas,Atif Ali 한국차세대컴퓨팅학회 2022 한국차세대컴퓨팅학회 학술대회 Vol.2022 No.10
Cloud computing is computing that provides, storage, databases, networking, intelligence, software, and analytics over the internet. Cloud services are delivered remotely and almost always from an offsite data center. Cloud services manage a better computing infrastructure efficiently. This study presents security issues & challenges in cloud computing and tries to find out the possible solution for some of the problems. It also discusses some solutions that deal with cloud computing-related to its privacy and security challenges. The proposed Intelligent Cloud Security Issues Detection using Multilayer Mamdani Fuzzy Inference System (ICSID-ML-MFIS) Expert System, can classify the different types of threats. The Expert System has eight input variables at layer-I, three input variables at layers-II, three input variables at layers-III, and six input variables at layers-IV. At layer-I input variables are threat-to-software, Traffic Monitoring, Networking Threat, Resource Availability, Platform availability, Trusted-Service-Availability, Device Availability, and Network Availability that detects output condition of threats to be affected or Not-Affected. At layer-II input variables are Detect SAAS Threats, Detect PAAS Threat, and Detect IAAS Threats, which determine the output condition as Yes or No. At layer-III input variables are Monitoring, Gaining, and managing which determine the output condition as cloud security type DCST. At layer-IV input variables are security incident response (SR), privilege identity management, locate current security problem (SP), super user account, a factor leading to inability to control traffic, and locate social engineering attacks. At last output, the layer consists of eight output types to detect the cloud security issues such as lack of visibility of data, theft of data, inability to control data, hijacking, system vulnerability, social engineering attacks, data breaches, and no-security issues. The proposed model based on Fuzzy reached 91.5% of true positive cases.
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