Malaysia Has Seen A Remarkable Boom In Industrialization In The Past Century In Almost All Sectors Of Economy; The Oil And Gas Sector Being No Exception. The Oil And Gas Sector Plays A Vital Role In The Malaysian Economy Due To Its Significant Contribution To Workforce And Government Activities, As Well As To Its Share Of Energy Provisions. There Have Been Numerous Earthquakes In The Region That Caused Tremors On Malaysian Soil. Due To Recent Big Earthquakes, There Was A Sudden Need For A Seismic Hazard Analysis (SHA) For Malaysia That Would Help Designers Choose Appropriate Design Considerations. There Has Been Numerous Seismic Hazard Studies So Far That Includes Malaysian Territories. However, There Is A Need To Assess How Reliable Those Studies Are. Two Main Potential Contributors To Error Have Been Identified: 1) Seismic Hazard Analysis Method And 2) Ground Motion Prediction Equation (GMPE). The Amount Of Variation In Predicting Erroneous GMPE Is Huge. Thus, This Paper Concentrates On Generating New GMPEs Due To Subduction Specified For Malaysia And Validated Against Developed GMPE.
Solar PV Energy Is An Integral Part Of Our Energy Use And A Vital Component Of Renewable Energy Networks. With The Rapid Advancement Of Technology, PV Module Prices Are Declining And PV Panels Become More Efficient. National Economies Are Making Ambitious Investments In Off-grid PV Systems And Grid-connected PV Networks. PV Electricity Is Volatile, Relies On Solar Irradiation And Other Meteorological Influences, Such As Temperature, Humidity, Precipitation, Wind Direction, And Cloud Coverage, Unlike Conventional Energy Production Systems. The Introduction Of Large-scale Grid-connected Solar PV Plants Has Posed Significant Problems For Power Grids, Such As Lack Of Device Flexibility, Efficiency, And Energy Balance. It Is Crucial To Forecast Solar Energy Production To Ensure A Reliable Energy Supply Across PV Grids. In This System, Artificial Neural Network (ANN) Based Levenberg-Marquardt (LM), Bayesian Regularization (BR) And Scaled Conjugate Gradient (SCG) Algorithms Are Deployed In Maximum Power Point Tracking (MPPT) Energy Harvesting In Solar Photovoltaic (PV) System To Forge A Comparative Performance Analysis Of The Three Different Algorithms.
Agriculture Is The Main Activity In Many Parts Of The Countries. Agriculture Acts As A Vital Part Of The Economic System Of Every Country. Agriculture Not Only Provides Food And Raw Material But Also Acts As A Source Of Livelihood For Farmers. Today, Farmers Are Facing Many Challenges In Agricultural Land. This Research Work Focuses On One Of The Main Challenges In Agricultural Land I.e., Disease Prediction. The Disease In Crop Plants Affects Agricultural Production, So A Model Is Proposed To Automate A Method For The Prediction Of Disease In The Plants And Intimating The Farmers To Take Appropriate Action Beforehand. In This Work, A Deep Learning Model Is Proposed That Accurately Classifies Any Leaf Images Is Having A Disease Or Not, In Addition To Providing A Type Of Disease.
Crop And Plant Diseases Entail Serious Implications For Food Security And Production Losses. Over The Years, The Lasting Global Trade And The Changing Climate Have Not Only Exacerbated The Existing Favorable Conditions For Plant And Crop Disease But Have Also Created New Conditions With Which Agriculture Must Now Contend. As The Food And Agriculture Organization Of The United Nations (FAO) Asserts, Plant Pests And Diseases Are Responsible For Losses From 20% To 40% Of Annual Global Food Production. This Means That Timely Disease Management Will Be Necessary In Order To Address The Increased Food Demand Caused By Population Growth. In This System, We Present An Analysis And Classification Of Research Studies Conducted Over The Past Decade That Forecast The Onset Of Disease At A Pre-symptomatic Stage (i.e., Symptoms Not Visible To The Naked Eye) Or At An Early Stage. We Examine The Specific Approaches And Methods Adopted, Pre-processing Techniques And Data Used, Performance Metrics, And Expected Results, Highlighting The Issues Encountered.
The Written Exam Is A Universal Tool For Evaluating Student Performance In The Field Of Education. The Written Exam Provides A Mechanism By Which Instructors And Organizations Ensure The Consistency Of The Assessment Process. Human Effort Required For The Assessment Is Very High And It Depends On Several Factors Such As Knowledge Of The Teacher, Application Level Understanding Of The Teacher, Criteria Of The Marking And Time Allotted. However, Traditional Evaluation Processes Consume Very Costly Efforts And Take Huge Time For The Completion Of The Complete Evaluation, Verification And Publishing Of The Result Process. This Research Introduces The Design And Implementation Of Handwritten Answer Evaluation (HAES) System For Student Exam Papers.
Human Body Has Its Basal Temperature That Can Be Exploited For Different Uses. Obviously, Thermal Properties Of Human Tissues Allow To Retrieves Specific Characteristics If Special Attention Is Paid. Therefore, Thermal Imaging Is A Suitable Tool For Many Applications. It Could Be Used In Medical Issues For Checking Temperature Variations Displayed By A Volume Under Investigation During Surgery Operations Related To Humans. It Plays A Double Role: Imaging And Temperature Measurements. This Paper Presents A Wide And Joint Experimental Research For Determining The Decreasing Temperature Encompassed In A Volume During A Urological Intervention In Order To Established, In A Real Time, Which Tissues And Adherences Must Be Taken In Consideration For Continuing And Optimizing The Surgery Process.