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您的检索式：作者名="Khalid Chishti"
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| 1 | A Comprehensive Approach to Well -Integrity Management in Adma- Opco显示文摘 | Ihab Tarmoom Hussain Bin Thabet Saleh Samad Khalid Chishti Ashiq Hussain Mohamed Arafat Adma - Opco | | SPE0，，： | 1 |
| 2 | A remotely sensed tracking of forest cover and associatedtemperature change in Margalla hills显示文摘Worldwide, forest degradation is a serious environmental issue, and inPakistan, forest wealth is depleting at the highest rate in South Asia. Toensure sustainable development goals of environmental stewardship,social development and economic growth, a sound monitoring andregulatory mechanism is essential for tracking forest cover changes. Thisstudy aims to quantify the decline of forest reserves and associatedtemperature variations in a relatively unexplored biodiversity hotspot ofIslamabad, Margalla Hills National Park (MHNP). Imagery acquired byLandsat TM (Thematic Mapper) for the year 1992, 2000 and 2011 areused to assess the spatial and temporal changes occurred over the lasttwo decades (from 1992 to 2011). A robust hybrid-classification routineis implemented to monitor the changes in forest cover and ANOVAalong with Tukey’s HSD (Honestly Significant Difference) test is used totest the significance of temperature variation associated with a shift inland cover classes. The results showed a significant growth insettlements, agricultural area and barren soil whereas water body, lowervegetation, scrub and pine forest are diminishing. In both decades, thetemperature alteration associated with a change in land cover classesare statistically significant (confirmed by ANOVA and Tukey’s HSD tests)for most of the land use/land cover classes. Based on these findings, thisstudy concludes that forests are dwindling at MHNP and the degradingcondition of the forest is below par and necessitates the promotion ofconservation practices to minimize ecological disturbances. | Noora Khalid Saleem Ullah Sheikh Saeed Ahmad Asad Ali Farrukh Chishtie | 2019 | International Journal of Digital Earth2019，12，10： | 1 |
| 3 | Adaptive task scheduling in IoT using reinforcement learning显示文摘Purpose-The intelligence in the Internet of Things(IoT)can be embedded by analyzing the huge volumes of data generated by it in an ultralow latency environment.The computational latency incurred by the cloud-only solution can be significantly brought down by the fog computing layer,which offers a computing infrastructure to minimize the latency in service delivery and execution.For this purpose,a task scheduling policy based on reinforcement learning(RL)is developed that can achieve the optimal resource utilization as well as minimum time to execute tasks and significantly reduce the communication costs during distributed execution.Design/methodology/approach-To realize this,the authors proposed a two-level neural network(NN)-based task scheduling system,where the first-level NN(feed-forward neural network/convolutional neural network[FFNN/CNN])determines whether the data stream could be analyzed(executed)in the resourceconstrained environment(edge/fog)or be directly forwarded to the cloud.The second-level NN(RL module)schedules all the tasks sent by level 1 NN to fog layer,among the available fog devices.This real-time task assignment policy is used to minimize the total computational latency(makespan)as well as communication costs.Findings-Experimental results indicated that the RL technique works better than the computationally infeasible greedy approach for task scheduling and the combination of RL and task clustering algorithm reduces the communication costs significantly.Originality/value-The proposed algorithm fundamentally solves the problem of task scheduling in realtime fog-based IoT with best resource utilization,minimum makespan and minimum communication cost between the tasks. | Mohammad Khalid Pandit Roohie Naaz Mir Mohammad Ahsan Chishti | 2020 | International Journal of Intelligent Computing and Cybernetics2020，13，3： | 0 |
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