19c96fd517d854497e8-383d 20220131061617273 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Recent Technology and Engineering (IJRTE) IJRTE 22773878 10.35940/ijrte.2277-3878 https://www.ijrte.org/ 01 30 2022 10 5 School of Housing, Building, and Planning, Universiti Sains Malaysia, 11800 Penang, Malaysia. Dr. Ts. Noor Faisal Olanrewaju Sharafadeen Babatunde Postgraduate Department of Building Technology, School of Environmental Studies, The Federal Polytechnic, Ado Ekiti, Nigeria. This writes up analyse earlier investigation on the major benefits and problems associated with the utilization of palm kernel shell as limited substitute of marble chippings as a local material in the production of kernelrazzo floor finish tiles. Although papers on kernelrazzo floor finish are few and one hundred and one (101) current related papers on palm kernel shell concrete were used to produce lightweight concretes and former literatures was assessed on the durability and strength of kernelrazzo floor finish. Rarely work has been done on incorporating palm kernel shell as incomplete substitute of marble in the production of kernelrazzo floor finish tiles. Palm kernel shell is one of the waste agricultural products obtained from production palm oil which is originating from the palm oil industry which common in Africa and Asia. Properties and characteristics of palm kernel shell (PKS) and marble chippings are summarized as specific gravity to include shape, thickness and texture include bulk density, water absorption and moisture content. Mechanical properties tests on palm kernel shell also include abrasion property and impact resistivity. Lightweight properties of concrete produced with palm kernel shell (PKS) have a limited substitute of coarse aggregate which include chemical and physical properties of granite dust were appraised. Mechanical properties of kernelrazzo floor finish tiles that were reviewed include compressive strength; curing and minimum requirement for Terrazzo floor finish and final for the area covered on this review is the consequence of acids, bases, and salts on the durability and strength of kernelrazzo floor finish. 01 30 2022 134 145 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E6729.0110522 https://www.ijrte.org/portfolio-item/e67290110522/ Department of Electronics and Telecommunication Engineering, Prof. Dr. Vishwanath Karad MIT World Peace University, Pune (MH), India. Shruti Gurudas Patil Dr. Mrunal Ninad Annadate Department of Electronics and Telecommunication Engineering, Prof. Dr. Vishwanath Karad MIT World Peace University, Pune (MH), India. With the rapid growth of technology and data, the healthcare domain has emerged as one of the most important research areas in the modern period. Machine Learning is a novel method for disease prediction and diagnosis. This study demonstrates how machine learning can be used to forecast disease based on symptoms. Techniques of Machine learning such as Bayes, Random Forest, and SVM are used to forecast the disease on the supplied dataset. The research determines which algorithm is the best based on its accuracy. The accuracy of an algorithm is determined by its performance on a particular dataset. One of the most significant disorders is heart disease. We discovered machine learning models to predict heart problems in order to lower the incidence of death caused by heart disease. In this paper, we used a dataset from 1988 that included four databases: Cleveland, Hungary, Switzerland, and Long Beach V., and applied an algorithms to it to obtain the results. Previous studies had lower accuracy, therefore we focused on this research to enhance accuracy rate, precision, and recall which are very crucial parameters in medical field, in order to forecast heart problems and rescue patients. In this paper, we worked on different algorithms such as SVM, Random Forest, Naïve Bayes, Neural Network and Decision Tree. The model was implemented using the Python programming language. Analysis result indicates that SVM and Decision Tree algorithms have achieved highest accuracy which is 98.05%. 01 30 2022 117 122 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E6768.0110522 https://www.ijrte.org/portfolio-item/e67680110522/ Assistant Professor, Department of Electrical & Electronics Engineering, SJCE, JSS Science and Technology University, Mysuru, India. Mrs. Rekha M N*, M N Dr. U B Mahadevaswamy Professor, Department of Electronics and Communication Engineering, Sri Jayachamarajendra College of Engineering, Mysuru, India. A smart grid network allows the existence of distributed power generation units. These units generate power through renewable or non-renewable means and supply it through the distribution networks. A major problem with these distributed power generation units is that they introduce harmonic components and affect power flow, creating high impedance faults (HIF) in the distribution network. HIF detection is difficult because the associated current has a low amplitude, rendering overcurrent safety devices ineffective. Wireless communication is one of the solutions for fault detection and feeder reconfiguration. This proposed work has an effective sensor network employed to determine and localize the HIF faults in the distribution network supporting distribution generation units. Fast Independent Component features are clustered in each area, and a SVM classifier is constructed to recognize faults. The learnt knowledge represented in SVM is converted to decision rules and disseminated into the sensor network nodes for effective distributed detection and localization of faults. Due to distributed detection, faults can be localized in less time. This improves the accuracy of fault detection as well as improves the network performance. 01 30 2022 106 112 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E6775.0110522 https://www.ijrte.org/portfolio-item/e67750110522/ Faculty of Chemical Engineering, Universiti Technologi Malaysia, Johor, Malaysia. Noor Fathiah Haziqah Othman Norzita Ngadi Faculty of Chemical Engineering, Universiti Technologi Malaysia, Johor, Malaysia. Abu Hassan Nordin Faculty of Chemical Engineering, Universiti Technologi Malaysia, Johor, Malaysia. Nur Aien Fatini Abd Latif Faculty of Chemical Engineering, Universiti Technologi Malaysia, Johor, Malaysia. Amir Asyraf Nasarudin Faculty of Chemical Engineering, Universiti Technologi Malaysia, Johor, Malaysia. The application of domestic waste as an economical and eco-friendly adsorbent has emerged among the most promising options for pharmaceutical remediation due to its high performance. Nonetheless, one of the most significant challenges of modified adsorbents is the difficulty in their recovery process, which includes separating adsorbents from cleaned water. This study synthesized and investigated a magnetic adsorbent derived from chicken eggshell (CE) modified with polyethyleneimine (PEI) for aspirin removal to address this issue. The chosen variables for the adsorbent synthesized were the ratio of CE:PEI (1:1, 1:2, 2:1, 2:0.5), the ratio of CE-PEI: magnet powder (2:1:2, 2:1:1, 2:1:0.5, 2:1:0.25). Adsorption studies were carried out to remove 0.1 g/l of aspirin. The results indicated that the optimal synthesis conditions for the magnetic chicken eggshell modified with polyethyleneimine (MCEP) are 2:1 for CE: PEI ratio, 2:1:1 ratio for CE-PEI to magnet particles and 120 minutes of crosslinking time. 01 30 2022 113 116 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E6781.0110522 https://www.ijrte.org/portfolio-item/e67810110522/ Department of Electronics and Telecommunication, Jabalpur Engineering College, Jabalpur, (M.P), India. Noopur Soni Dr. Agya Mishra Department of Electronics and Telecommunication, Jabalpur Engineering College, Jabalpur (M.P), India. Radio frequency identification technology is one of the fastest-growing technologies in the realms of navigation, medical, robotics, communication system, logistics, security, safety, etc. Surveillance is one of the important fields where high accuracy and fast response are needed. In this research work, RFID sensors are used to track moving objects with an intelligent supervision model. The sophisticated surveillance model employs neural networks followed by an adaptive filtering technique based on an Unscented Kalman filter. A neural network is also one of the most efficient and powerful technology in the field of learning and data processing capability. A neural network has the capability of processing a mammoth amount of data because of this feature its efficiency and accuracy are quite high. This model localizes N number of objects/targets through an intelligent surveillance model, picks a random object from this pool of localized objects to track, categorizes their movement through a controlled checkpoint, and calculates the distance traveled by the moving object /target. Experimental results show that the proposed model can locate multiple-objects with the help of multiple input RFID antennas and tags and track them concerning to the RFID antennas with high accuracy and stability in the complex indoor environment and this intuitive model can be effectively implemented at the airport, railway station, shopping mall, retail management, as well as any other surveillance purpose. For this research work number of authors work, is reviewed and based on literature review this model is designed. 01 30 2022 117 124 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E6782.0110522 https://www.ijrte.org/portfolio-item/e67820110522/ Assistant Professor, Department of Electronics & Communication Engineering, Vidyavardhaka College of Engineering, Mysuru, India. Mrs. Geetha M N Dr. U B Mahadevaswamy Professor, Department of Electronics and Communication Engineering, Sri Jayachamarajendra college of Engineering, Mysuru, India. The Transmission of signals from one end to other end without intrusions and efficient manner is most hilarious task. Accordingly, OFDM method is utilized for transmission which has better resistance against the multipath fading and yields better efficiency among other wireless communication processes. Even though bounded with more advantages PAPR problem is undesirable for OFDM which in turn readily reduces the data rate. Hence, PAPR Problem reduction improves the quality of service. Thus the reduction of PAPR is done by utilizing adaptive clipping and windowing methods with interdependent discretization approach by utilizing CAIM algorithm, which arrange signal in equal discrete level and makes clipping with reduced BER and Planck-tapper window further yields renovated signal with amplitude varied linear signal which in turn mitigates the PAPR and also allows the signal to flow on exact discrete interval which readily improves the efficiency to a great extent. This proposed methodology is implemented in MATLAB software. 01 30 2022 125 132 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.E6783.0110522 https://www.ijrte.org/portfolio-item/e67830110522/