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- 저자 : Ali Younas (검색결과 4 건)
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An Artificial Intelligence Approach for Word Semantic Similarity Measure of Hindi Language
( Farah Younas ),( Jumana Nadir ),( Muhammad Usman ),( Muhammad Attique Khan ),( Sajid Ali Khan ),( Seifedine Kadry ),( Yunyoung Nam ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.6
AI combined with NLP techniques has promoted the use of Virtual Assistants and have made people rely on them for many diverse uses. Conversational Agents are the most promising technique that assists computer users through their operation. An important challenge in developing Conversational Agents globally is transferring the groundbreaking expertise obtained in English to other languages. AI is making it possible to transfer this learning. There is a dire need to develop systems that understand secular languages. One such difficult language is Hindi, which is the fourth most spoken language in the world. Semantic similarity is an important part of Natural Language Processing, which involves applications such as ontology learning and information extraction, for developing conversational agents. Most of the research is concentrated on English and other European languages. This paper presents a Corpus-based word semantic similarity measure for Hindi. An experiment involving the translation of the English benchmark dataset to Hindi is performed, investigating the incorporation of the corpus, with human and machine similarity ratings. A significant correlation to the human intuition and the algorithm ratings has been calculated for analyzing the accuracy of the proposed similarity measures. The method can be adapted in various applications of word semantic similarity or module for any other language.
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Waqas Ahmad Wattoo,Ghulam Sarwar Kaloi,Muhammad Yousif,Mazhar Hussain Baloch,Baqar Ali Zardar,Jehangir Arshad,Ghulam Farid,Talha Younas,Sohaib Tahir 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.1
The escalating energy demand across the globe has intensifed the electricity production. Owing to the unavailability of the reliable techniques for electricity storage for a long duration, it is consumed immediately after its production. Therefore, electricity markets can’t be handled like the conventional stock markets. Power companies are facing immense price and delivery risks owing to the increasing competition in the electricity markets. As a result, risk management is the fundamental concern to be addressed in order to achieve the optimum proft targets. Consequently, the power generation organizations need to allocate their generation in bilateral contracts and spot market. For this purpose, an optimal theory of portfolio selection is proposed in this study for electricity generation by forming a reliable prototype and applying the proposed scheme to obtain the suitable outcomes. The Paris Accord on environmental safety from carbon dioxide and NOx gases is especially considered during the modeling of the proposed technique. The credibility of the proposed scheme is validated by using the real-time market data from the PJM market. Various risk-return tradeofs are implemented, and their corresponding solutions are acquired for portfolio optimization as corroborated by the results. The suggested technique is found reliable and adequate for the carbon tax paying suppliers around the world for allocating their respective generation based on the demand of the consumers.
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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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Aesculus indica‑derived heteroatom‑doped carbon as an electrode material for super‑capacitor
Fakhar Zaman,Muhammad Waqas Ishaq,Aisha Munawar,Umer Younas,Zahid Ali 한국탄소학회 2023 Carbon Letters Vol.33 No.7
Energy storage for sustainable development and progress of power production industries is vitally important. The energy storage devices are under extensive research from last three decades to ensure the hand-on-hand coordination with power supply phenomenon and to reduce the energy loses in lines. The cost-effective materials are still highly demanding as an electrode material for energy storage devices. Biomass-derived carbon materials are best candidates due to their low cost, relatively high abundance, pollution-free nature. Here, we are reporting a facile two-step green approach to convert Himalayan horse chestnuts (HHCNs) into activated carbon materials. In first step, grinding and pyrolysis of the HHCNs were carried out, and then activation was performed using KOH to enhance the pore density and surface area. HHCNs-derived carbon was utilized as an electrode in electrical double-layer capacitors (EDLCs) with 1 M H2SO4 as an electrolyte. The macroporous structure along with hierarchical porous network acts as an efficient source of transportation of charges across the electrode and separator. Cyclic voltammetry test was taken from 10 to 100 mV/s current and within a range of 0–1 V applied potential; approximately rectangular CV shown mirror response towards current and shown typical EDLCs properties. The proximate analysis confirms the presence of heteroatoms like sulfur, oxygen, and nitrogen which act as carbon dopants. The wettability of HHCNs-derived carbon enhanced due to the various types of oxygen functionalities inherited from the lignin skeletal part. The nitrogen content is primarily responsible for the pseudo-capacitive behavior of HHCNs-codoped carbon. HHCNs-derived activated carbon materials has emerged as a promising electrode material for energy storage applications.
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