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This paper focused on improving student performance prediction, based on their personal and academic performance characteristics. Due to the incredible growth in recent technology like social media, it may deter the students from their actual track, and this is one of the reasons for the students to perform poor in academic activities and it even leads to course drop outs. Predicting students' performance will alert the learner to know about their performance and it gives as a chance to improve their performance in future. The dataset used for the research purposes includes data about students' performance from the academic and other class room activities in the college during the course time. , Educational data mining algorithms is used to predict the student performance which is a module in automated intelligent education systems

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Currently, the aluminum alloys are utilized more in level of all industries for different applications; furthermore, industries need high-strength alloys for making innovative components. For those reasons, many researchers hope to prepare hybrid aluminum metal matrix composites at various composition levels. In this experimental work, we intended to prepare the hybrid metal matrix composites such as aluminum alloy 7079 with reinforcement of ZrO2 + Si3N4 through stir-casting process. Major findings of this work, as to optimize the stir-casting process, can be to continually conduct wear test and evaluate the microhardness of the stir-casted specimens. Optimization of stir-casting process parameters is a preliminary work for this research by Taguchi tool. The chosen parameters are % of reinforcement (0%, 4%, 8%, and 12%), agitation speed (450 rpm, 500 rpm, 550 rpm, and 600 rpm), agitation time (15 min, 20 min. 25 min, and 30 min), and molten temperature (700°C, 750°C, 800°C, and 850°C). The prepared stir-casted materials are tested by wear analysis and microhardness analysis, through Pin-on-Disc wear tester and Vickers hardness tester, respectively. Wear parameters are optimized, the minimum wear rate is evaluated, and also the wear worn-out surfaces are examined through SEM analysis.

The Wireless Systems Are Employed With More Number Of Antennas For Fulfilling The Demand For High Data Rates. In Telecommunication, Lte-A (Long Term EvolutionAdvanced) Is A Well-Known Technology Intended For Wireless Broadband Communication Aimed At Data Terminals And Mobile Devices. Multiple Input Multiple Output (Mimo) Technology Is Used By Lte Which Is Also Known As Fourth Generation Mobile Networks To Attain Very High Data Rates In Downlink And Uplink Channels. Though The Mimo Systems In Massive Mimo Are Provided By Multiple Antennas, The Design Of The Appropriate Non-Erroneous Detection Algorithm Is A Major Challenge. Also, With The Increase In Quantity Of Antennas, The System's Spectral Efficiency Begins To Degrade. So As To Deal With This Issue, A Proper Algorithm Has To Be Utilized For Channel Estimation. The Bio Inspired Algorithms Have Shown Potential In Handling These Issues In Communication And Signal Processing. So, Grey Wolf Optimization (Gwo) Algorithm Is Used In This Approach To Estimate The Most Optimal Communication Channel. Also, The Spectral Efficiency And Data Capacity Are Enhanced Using The Presented Approach. The Proposed Approach’s Performance Is Compared With The Existing Approaches. The Simulation Result Exposes That The Presented Channel Estimation Approach Is Far Better Than Existing Channel Estimation Approaches In Performance Metrics Such As Bit Error Rate, Minimum Delay, Papr, Spectral Efficiency, Uplink Throughput, Downlink Throughput And Mean-Squared-Error