-22b9b34417bc6092a744271 20220216022653960 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 01 10 2021 10 3 Department of Information Technology, Taif University, Taif, Saudi Arabia. Hamza Turabieh Ahmad S. Alghamdi Computer Engineering Department, Taif University, Taif, Saudi Arabia. Wi-Fi technology is now everywhere either inside or outside buildings. Using Wi-fi technology introduces an indoor localization service(s) (ILS). Determining indoor user location is a hard and complex problem. Several applications highlight the importance of indoor user localization such as disaster management, health care zones, Internet of Things applications (IoT), and public settlement planning. The measurements of Wi-Fi signal strength (i.e., Received Signal Strength Indicator (RSSI)) can be used to determine indoor user location. In this paper, we proposed a hybrid model between a wrapper feature selection algorithm and machine learning classifiers to determine indoor user location. We employed the Minimum Redundancy Maximum Relevance (mRMR) algorithm as a feature selection to select the most active access point (AP) based on RSSI values. Six different machine learning classifiers were used in this work (i.e., Decision Tree (DT), Support Vector Machine (SVM), k-nearest neighbors (kNN), Linear Discriminant Analysis (LDA), Ensemble-Bagged Tree (EBaT), and Ensemble Boosted Tree (EBoT)). We examined all classifiers on a public dataset obtained from UCI repository. The obtained results show that EBoT outperforms all other classifiers based on accuracy value. 01 10 2021 49 53 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C8375.0110321 https://www.ijitee.org/portfolio-item/C83750110321/ Department of Science and Technology, Doctoral school Sustainable Development and Society, Thiès, Senegal. Adam Gaye Mamadou Babacar Ndiaye University Institute of Technology, Thiès, Senegal. Oumar Diallo University Institute of Technology, Thiès, Senegal. Harouna Mamadou Bal University Institute of Technology, Thiès, Senegal. Salif Gaye University Institute of Technology, Thiès, Senegal. In order to address energy efficiency issues in the building sector, we conducted this study which focuses on the optimization of the mechanical characteristics of Typha concrete for its use in load-bearing structures of buildings. The fact that buildings are very energy-intensive makes it essential to develop new forms of construction based on bioclimatic architecture and the valorization of certain materials considered as waste in construction. To achieve these objectives, wehave targeted the use of Typha Australis thanks to its great availability and high the rmalinsulation capacity. Thus, starting from the composition of a control concrete determined bythe Dreux Gorisse formulation method with a characteristic compressive strength of 20 MPaat 28 days, Typha S1 series concretes are formulated with the substitution of sand up to 40, 50,and 60% of Typha. In order to increase the mechanical strength of Typha S1 series concretes, the cement class and G/S ratio are increased for the second S2 series. At the end of this research, the results obtained show that some of these concretes withdifferent proportions of Typha have good mechanical performance, which depends on theirstructural use. 01 10 2021 54 58 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C8377.0110321 https://www.ijitee.org/portfolio-item/C83770110321/ Department of Electrical Engineering, AP Goyal Shimla University, (Himachal Pradesh), India. Showkat Rasool Dar Gopal Naryal Department of Electrical Engineering, AP Goyal Shimla University, (Himachal Pradesh), India. Dr Anand Mohan Department of Electrical Engineering, AP Goyal Shimla University, (Himachal Pradesh), India. This paper presents energy requirements of J&K state of India with the present policies and future planning for the best utilization of the available resources. There will be acceleration in the future developmental graph in the country like India resulting in bulk of the energy demand. The procedures and methods applied currently adopted in renewable field aims for exploring possible energy production while considering the environmental impact. The hilly region such as J&K and Ladakh region has tremendous amount of renewable energy potential. The presentation of this paper aims the cost analysis of hybrid power system in the region. For optimization and sensitivity analysis, HOMER pro version 3.23.8 is used. The renewable resources for selected area are analyzed in Global Solar atlas (GSA) and Global wind atlas (GWA) and the result is compared with the meteorological tower data set. The modeling of the hybrid system is purposed on a 10% of load variability and as a result of the study, the levelized cost of energy (LCOE) from the hybrid system is obtained 0.0466 USD/kWh which will be 30 to 50 percent cheaper than the price of electricity from conventional system. 01 10 2021 80 86 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C8381.0110321 https://www.ijitee.org/portfolio-item/C83810110321/ Department. of Computer science and Engineering Delhi Technical Campus GGSIPU, Delhi, India. Megha J Panicker Vikas Upadhayay Department. of Computer science and Engineering Delhi Technical Campus GGSIPU, Delhi, India. Gunjan Sethi Department. of Computer science and Engineering Delhi Technical Campus GGSIPU, Delhi, India. Vrinda Mathur Department. of Computer science and Engineering Delhi Technical Campus GGSIPU, Delhi, India. In the modern era, image captioning has become one of the most widely required tools. Moreover, there are inbuilt applications that generate and provide a caption for a certain image, all these things are done with the help of deep neural network models. The process of generating a description of an image is called image captioning. It requires recognizing the important objects, their attributes, and the relationships among the objects in an image. It generates syntactically and semantically correct sentences. In this paper, we present a deep learning model to describe images and generate captions using computer vision and machine translation. This paper aims to detect different objects found in an image, recognize the relationships between those objects and generate captions. The dataset used is Flickr8k and the programming language used was Python3, and an ML technique called Transfer Learning will be implemented with the help of the Xception model, to demonstrate the proposed experiment. This paper will also elaborate on the functions and structure of the various Neural networks involved. Generating image captions is an important aspect of Computer Vision and Natural language processing. Image caption generators can find applications in Image segmentation as used by Facebook and Google Photos, and even more so, its use can be extended to video frames. They will easily automate the job of a person who has to interpret images. Not to mention it has immense scope in helping visually impaired people. 01 10 2021 87 92 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C8383.0110321 https://www.ijitee.org/portfolio-item/C83830110321/ Hassan First University, Faculty of Sciences and Techniques, Settat, Morocco. Youness Tribis Abdelali El Bouchti Hassan First University, National School of Business and Management, Settat, Morocco. Houssine Bouayad Hassan First University, Faculty of Sciences and Techniques, Settat, Morocco. Initially developed for Bitcoin cryptocurrency, Blockchain Technology (BCT) is a decentralized transaction managing technology that provides security, anonymity and data integrity in transactions without the need of any trusted third party. The interest in BCT has been grown, as research topic for many fields, such as finance, health, government, agriculture and many particular Supply Chain (SC) industries. However, there is a lack of systematic literature reviews (SLR) on existing research concerning how BCT is well relevant in SC. This paper conducts a systematic review in order to examine all pertinent research on SC based on BCT. The main inspiration for this work was to synthesize existing evidence, classify research tendencies available in the literature, and identify open themes and gaps for development in this discipline. However, 45 primary papers have been extracted from scientific databases. This systematic review provides direction for future research regarding the applications of BCT for SC 01 10 2021 125 136 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C8384.0110321 https://www.ijitee.org/portfolio-item/C83840110321/ Research Scholar, Department of Mechanical Engineering, University College of Engineering, JNTUK, Kakinada. R Peddi Raju A.Gopala Krishna Department of Mechanical Engineering, University College of Engineering, JNTUK, Kakinada. Al2O3 is a commonly used cutting tool material for machining cast iron and hard steels. However its low fracture toughness has been its potential drawback to use it for wider applications. In order to improve the fracture toughness, graphene nanoplatelets has been used as the reinforcement. The Al2O3 -TiCN composite have been made by powder metallurgy. The present work compares the performance characteristics of Al2O3 -TiCN with and without graphene nanoplatelets (GNP) in CNC machining. The machining performance of the prepared cutting tools is tested in terms of temperature generated, flank wear, cutting force and surface finish. It is observed that the prepared composite tool with GNP has much improved machining performance over the Al2O3 cutting tool that has no GNP reinforcement. The work has the unique novelty of using GNP as the reinforcement in Al2O3 cutting tool material. 01 10 2021 93 96 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C8390.0110321 https://www.ijitee.org/portfolio-item/c83900110321/ Department of Civil Engineering, PhD Scholar, G.D. Goenka University, Gurugram, Haryana. India. Prateek Malik Dr. Sudipta K Mishra Department of Civil Engineering, Associate Professor, G.D. Goenka University, Gurugram, Haryana, India. Soft soil having a low bearing capacity is found in many parts of the world and construction on soft soil is a challenge. To overcome the situation, the soil needs to be stabilized with some external material like geosynthetic, fly ash and rice husk. Geosynthetics materials are tremendous materials used to solve many civil engineering problems. Fly ash is a by product produced by burning of coal and is available in various thermal power plant as a waste material. Disposal of fly ash is also a problem but in one area where it can be used is soil stabilization. A comprehensive review of published literature on the use of geosynthetic and fly ash to stabilize and enhance the strength of soil was carried out. The effect of using geosynthetic material and fly ash was investigated on the properties of soil like Optimum Moisture Content, Maximum Dry Density, California bearing ratio, unconfined compressive strength and compaction behavior of the soft soil. 01 10 2021 97 104 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C8393.0110321 https://www.ijitee.org/portfolio-item/C83930110321/ Department of Computer Science, Dravidian University, Kuppam, India. Dr. D. Mabuni Dr. S. Aquter Babu Department of Computer Science, Dravidian University, Kuppam, India. In machine learning data usage is the most important criterion than the logic of the program. With very big and moderate sized datasets it is possible to obtain robust and high classification accuracies but not with small and very small sized datasets. In particular only large training datasets are potential datasets for producing robust decision tree classification results. The classification results obtained by using only one training and one testing dataset pair are not reliable. Cross validation technique uses many random folds of the same dataset for training and validation. In order to obtain reliable and statistically correct classification results there is a need to apply the same algorithm on different pairs of training and validation datasets. To overcome the problem of the usage of only a single training dataset and a single testing dataset the existing k-fold cross validation technique uses cross validation plan for obtaining increased decision tree classification accuracy results. In this paper a new cross validation technique called prime fold is proposed and it is experimentally tested thoroughly and then verified correctly using many bench mark UCI machine learning datasets. It is observed that the prime fold based decision tree classification accuracy results obtained after experimentation are far better than the existing techniques of finding decision tree classification accuracies. 01 10 2021 105 110 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C8403.0110321 https://www.ijitee.org/portfolio-item/C84030110321/ is a Research Doctor at LASTIMI Laboratory, Superior School of Technologies of Sale, Mohammadia School of Engineering, Mohamed V University City of Rabat, Morocco. Aziz Srai Fatima Guerouate is a Research Professor at LASTIMI Laboratory, Superior School of Technologies of Sale, Mohammadia School of Engineering, Mohamed V University City of Rabat, Morocco. Hilal Drissi Lahsini is a Research Professor at LASTIMI Laboratory, Superior School of Technologies of Sale, Mohammadia School of Engineering, Mohamed V University City of Rabat, Morocco. The e-learning study reflects a trend in the integration of information and communication technologies in universities. This trend evokes a new form of teaching and learning and a new form of relationship between students and teachers. In fact, information and communication technologies, such as e-learning, call into question the ways of thinking and the ways of acting of individuals in the representation of learning. This paradigm shift requires introspection and the renewal of skills. In the face of these changes, higher education institutes must develop and make essential the courses that allow students to adapt to the new demands of the labor market. on the other hand, information and communication technologies and computer networks, These objects from daily life, are part of the immediate environment that is both professional, educational and personal of each one. With the massive arrival of personal and accessible digital tools (computers, nomadic equipment such as mobile phones and digital tablets, etc…), multiple online spaces are emerging on the Internet (discussion forums, e-learning platforms, blogs, messaging, chats, social networks like Facebook, online information sharing sites, etc…). E-learning offers features that differentiate it from others media objects such as books or television. e-learning offers quick, even instant, access to a multitude of information sources. They make it possible to store them and facilitate the possibilities of networking between individuals and groups of individuals whatever the time and place. Access to the Internet information network is “universal”. You only need to connect to a computer on the network to access almost this entire network. Access is also “simultaneous” because each Internet user exists on the network in the form of information by “his digital presence”, by the data that he moves or deposits and the interactions caused. We can also say that access is independent of time and distance since it is a space permanently open to human activity. Developing an e-learning application for each technology requires a lot of human resources and technical knowledge. To solve this problem we propose a development of an e-learning application according to a model-driven architecture approach. This paper is a development of our work in paper [Srai,2020]. 01 10 2021 111 115 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C8408.0110321 https://www.ijitee.org/portfolio-item/C84080110321/ Faculty of Multimedia, The Posts and Telecommunications Institute of Technologies, Hanoi, Vietnam. Thanh-Tam NGUYEN Son-Thai LE Department of Multimedia Communications, The School of Information and Communication Technology, Thai Nguyen University, Thai Nguyen, Vietnam. Van-Thuy LE the School of Foreign Languages Thai Nguyen University, Thai Nguyen, Vietnam. One of the widely used prominent biometric techniques for identity authentication is Face Recognition. It plays an essential role in many areas, such as daily life, public security, finance, the military, and the smart school. The facial recognition task is identifying or verifying the identity of a person base on their face. The first step is face detection, which detects and locates human faces in images and videos. The face match process then finds an identity of the detected face. In recent years there have been many face recognition systems improving the performance based on deep learning models. Deep learning learns representations of the face based on multiple processing layers with multiple levels of feature extraction. This approach has made sufficient improvement in face recognition since 2014, launched by the breakthroughs of Deep Face and Deep ID. However, finding a way to choose the best hyperparameters remains an open question. In this paper, we introduce a method for adaptive hyperparameters selection to improve recognition accuracy. The proposed method achieves improvements on three datasets. 01 10 2021 116 119 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C8409.0110321 https://www.ijitee.org/portfolio-item/C84090110321/ Structural Engineering Department, Arab Academy for Science Technology and Maritime Transport – Cairo Campus. Shided M.S Abdel Rashid I Professor, Faculty of Engineering, Ain Shams University, Egypt. EL Monayeri O.D Associate Professor, Faculty of Engineering, Arab Academy for Science Technology and Maritime Transport – Cairo Campus. EL Sayad M . A, Ph.D., Faculty of Engineering, Arab Academy for Science Technology and Maritime Transport – Cairo Campus. The Analytic Network Process (ANP) approach was applied to evaluate the overall project performance. The interdependencies between the performance factors in sanitation infrastructure projects have been modeled, and their cumulative influences were simulated and quantified. This model focuses on the planning and construction phase of projects under unit price contracts and is intended for use by owners, supervision consulting firms (Owner’s representative) and/or contractors as well. The proposed model assist both owners, consultants and contractors in controlling projects during the different phases and can also be used as a basis for simulation of various managerial scenarios/interventions to explore the best solution to correct the negative effect of poor performance. The proposed model is generic and may be applicable to almost any project in the construction industry, since all projects, regardless of size or type, follow a broadly similar pattern of development. This model focuses on the two phases; planning phase and construction phase of a project and is intended for implementation by both owners and contractors. Although the proposed evaluation model reflects to a great extent the contractors’ preferences, most contractors perceive performance according to project objectives and contractual aspects. The model also reflects to some extent the owner’s and or its consultant’s interventions within the different project cycle. Therefore, the objective of this research is not to standardize the priorities of performance factors, but rather to establish a systematic framework for consistent and quantitative evaluation process for performance of planning and construction of sanitation infrastructure projects in Egypt that take into consideration the interdependencies between multi dimensional performance factors. 01 10 2021 120 124 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C8414.0110321 https://www.ijitee.org/portfolio-item/C84140110321/