19c96fd517d854497e8-2594 20220215053832107 beie:beie director@blueeyesintelligence.org WEB-FORM International Journal of Innovative Technology and Exploring Engineering IJITEE 22783075 10.35940/ijitee https://www.ijitee.org/ 04 30 2021 10 6 Pursuing B.E. (CSE), Indore Institute of Science & Technology Indore, India. Nayan Uchhana Ravi Ranjan Pursuing B.E. (CSE), Indore Institute of Science & Technology Indore, India. Shashank Sharma Software Developer, Bachelors of Engineering in Information Technology, IET DAVV, Indore, India. Deepak Agrawal Assistant Professor, Department of Computer science, IIST, Indore, India. Anurag Punde Assistant Professor, Department of Computer science, AITR, Indore, India. Every year fraud cost generated in the economy is more than $4 trillion internationally. This is unsurprising, as the return on investment for fraud can be massive. Cybercrime specialists estimate that an investment of 1 million dollars into fraud or attack can net up to $100 million. Financial institutions such as commercial and investment banking operations are increasingly being targeted. And we know that the only way to fight fraud effectively is through the use of advanced technology. The answer lies in relying on advanced analytics and enterprise-wide data storage capabilities that support the use of artificial intelligence (AI) and machine learning (ML) approaches to stay one step ahead of criminals. AI is best suited to defend against today’s fast-changing and complex bank fraud, where new threats are under development every day. Approaches relying on fragmented and siloed data, rules-based approaches or traditional point-solutions are no longer acceptable. These approaches are not only ineffective, but they are extremely costly to banks and financial services firms because they force legal and compliance teams to spend a lot of time trying to gain access to the data they need. By relying on advanced analytics and AI and ML capabilities, fraud and compliance units can spend their time working on more-complex fraud issues. Manual investigation can be reduced through the use of complex algorithms powered by ML, often in conjunction with rules, a combination that offers significant advantages over purely based -rules fraud detection. In this paper, we have included different machine learning algorithms used to detect credit card frauds and also provide a comparative study between different algorithms. 04 30 2021 101 108 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.C8400.0410621 https://www.ijitee.org/portfolio-item/C84000110321/ Assistant Professor, Department of Information Tevhnology, Manipal University Jaipur, India. Neha Sharma Saurabh Sharma Associate Professor, Amity School of Hospitality, Amity University Rajasthan, Jaipur, India. Vatsal Sharma Student, Department of Information Technology, Manipal University Jaipur, Jaipur, India. Abhinav Utkarsh Student, Department of Information Technology, Manipal University Jaipur, Jaipur, India. Gaurang Bharadwaj Student, Department of Information Technology, Manipal University Jaipur, Jaipur, India. It is a period of online exchanges, managing daily installments through e-wallet for shopping, paying to merchant, also to fulfill all the liquid cash requirement through wallet money with no physical notes or currency involved for transaction, wallet exchange help to web advancements. The cash move and gross repayment is conceivable in only hardly any seconds. The web innovations have been utilizing and still new idea dependent on past encounters are currently coming into exploration and confab. A lot of research papers for wallet creation and inner cycle are accessible. Uncommonly, wallet measure through various methodologies like BC (Bank Channel) Wallet, RBI Wallet, Domestic Pay Service Wallet are principle thought in this exploration work. The exchange cycle faces numerous issues, these issues influence the backer, bank, client regarding misfortune in business. Whereas the client is additionally unsatisfied and unable to do finish wallet procedures. This paper presents a recreation examination of the versatility on simultaneous exchange handling over a combination of portable and fixed exchanges, because of moderate organization availability and down worker might be a similar exchange prepared twofold because of which RBI rules infringement happen and backer, client, bank, specialist all is grieved and misfortune the real business. This paper execution normally utilizes conflict location dependent on the Hidden label bases exchange accommodation through which copying any exchange is preposterous, it ensures that every exchange has prepared just a solitary path under RBI rules or wallet terms. 04 30 2021 1 4 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.E8677.0410621 https://www.ijitee.org/portfolio-item/E86770310521/ Department of Biomedical Engineering, Parul Institute of Technology (PIT), Parul University, Vadodara, Gujarat, India. Herbert Azairwe Mudingoto Eddie Department of Biomedical Engineering, Parul Institute of Technology (PIT), Parul University, Vadodara, Gujarat, India. Rashid Humayoon Department of Biomedical Engineering, Parul Institute of Technology (PIT), Parul University, Vadodara, Gujarat, India. Dimpal Khambhati Department of Biomedical Engineering, Parul Institute of Technology (PIT), Parul University, Vadodara, Gujarat, India. Electrolyte bottle monitoring has become difficult due to tight schedules most especially during these pandemic times that have left health workers with little or no time. Improper monitoring of electrolyte bottles can result into serious life-threatening risks. We intend to design a reusable electrolyte level sensor using Ultrasonic sensor and Arduino Nano. The project will have a transmitter circuit that will use Rf communication to send the data to receiver circuit for processing. The transmitter circuit has an ultrasonic sensor that will be used to measure the distance of electrolyte level in bottle. The data is then sent wirelessly to the receiver circuit where percentage of the electrolyte level is shown on LCD module. If it shows 100% electrolyte consumed, then an alarm is sent off alerting the health worker in charge. It is also our aim to develop an electrolyte level detector that is reusable, efficient and most importantly affordable for health care industries. Building such a monitoring system, will reduce patient hazards and also improve health care service delivery in terms of accuracy. Health care workers will not need to constantly or manually assess the level of electrolyte left in the bottle most especially during night shifts. Air particles or bubbles can enter the patient’s blood stream if electrolyte bottle gets empty and still attached to patient. If these particles enter, it can result into immediate death. With further developments, it can be upgraded to send message to a doctor/nurse’s phone, along with the patient’s room number and other specifications. 04 30 2021 5 9 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.E8688.0410621 https://www.ijitee.org/portfolio-item/E86880310521/ ECE Department, PSG College of Technology, Coimbatore, India. Dr. L. Thulasimani A. Antinita Shilpha Daly ECE Department, PSG College of Technology, Coimbatore, India. The main goal of vehicular communication is to provide a more safe and efficient vehicular operation. The challenge in a Vehicle-to-Everything (V2X) network is to provide reliable connectivity for the Vehicle-to-Vehicle (V2V) links and high data rate connectivity for the Vehicle-to-Infrastructure (V2I) links at the same time. This requirement leads to spectrum sharing in vehicular communication. As the vehicular systems increases, the transmit power levels increases in the environment which in turn causes harmful effects on the atmosphere. The objective of this paper is to analyze the graph-based spectrum sharing algorithms that are available for vehicular communication and to develop a power optimization algorithm based on Hidden Markov Model (HMM) and to incorporate it into these algorithms in such a way to achieve better sum capacity for the V2I links along with a guaranteed reliability for the V2V links. 04 30 2021 10 13 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.F8703.0410621 https://www.ijitee.org/portfolio-item/F87030410621/ Asst. professor, Department Computer Science and Engineering, CBIT, Hyderabad, (Telangana), India. A. Mohan P. Vamshikrishna PG Student, Department Computer Science and Engineering, CBIT, Hyderabad, (Telangana), India. People use the support of distributed computing however can’t completely believe the cloud suppliers to have protection and confidential information. To guarantee secrecy, data owners relocate encoded information rather than plain texts. To divide the encoded documents with different clients, Ciphertext-Policy Attribute-based Encryption (CP-ABE) can be utilized. But this cannot become secure against some other assaults. Many other schemes did not gave guarantee that the cloud provider has the power to check whether a downloader can unscramble or not. Consequently, these files are accessible to everybody who is approachable to the cloud storage. An intentionally harmful assailant can download a great many records to start Economic Denial of Sustainability (EDoS) attacks, it will to a great extent expend the cloud asset. The owner will bear all the expenses for the cloud storage but the cloud provider doesn’t provide the whole information about the access or usage. There is no transparency for the owner. We have to solve these concerns. In order to this we are going to propose a solution for securing the encrypted data from EDoS attacks and providing the owner whole usage information about the cloud storage. We are implementing by using the arbitrary access policy of CP-ABE. 04 30 2021 14 17 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.F8715.0410621 https://www.ijitee.org/portfolio-item/F87150410621/ Department of ECE, Chaitanya Barathi institute of Technology, Osmania University, Hyderabad (Telangana), India. M. Ramana Reddy This work Demonstrates a wideband LNA for 5G receiver front end modules with high linearity, Low noise reused topology has an inter stage wideband inductor based two common source cascade stages. The configuration provides the bias current; better Noise figure increases the forward gain. By providing RC Series network at gate terminal of second stage the return losses are reduced and stability will be increased. After pre and post simulation all parameters are better than the existing LNAS. After post simulation results, the Noise figure is achieved less than 1dB and forward gain as flat 16dB for wide band width of 1.5 – 5.5 GHz. At the 1dB compression point the output is 20dbm achieved and OIP3 IS +40dbm is achieved. The chip size of an LNA along with pad is 0.64mm2. The design is GaAsp HEMT process at 50nm technology. 04 30 2021 18 21 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.F8718.0410621 https://www.ijitee.org/portfolio-item/F87180410621/ Chaitanya Bharathi Institute of Technology, Hyderabad, India. M Thirupathaiah Renewable Energy Sources (RES) such as Solar Photovoltaic (PV) became more popular over the last decade due to increasing environmental awareness and tax exemption policies on the solar PV systems. Integration of solar PV using various smart load management techniques will boost the efficiency of the overall system by reducing the massive cost of electricity bills. There is a need to find efficient and expert ways to enjoy these RES exclusively. Besides providing the connection between different loads, this system has the ability to collect information and execute control commands for the households by providing continuous observations and information about both load and supply profile, convincing the end user to take preventive measures by switching the auxiliary load to save power. This paper presents implementation of a low cost Solar based DC grid using Arduino. In the proposed system, the node which acts as a microcontroller reads the power consumption by the loads in each unit through current sensor. When the excess amount of power is consumed at particular unit, the controller makes the relay cut off the supply to the loads, which will be continuously displayed through LCD. This DC based power system helps to eliminate the requirement of converters systems, reducing converter cost, power system complexity, improve efficiency and reliability. 04 30 2021 109 113 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.F8725.0410621 https://www.ijitee.org/portfolio-item/F87250410621/ Assistant professor, Civil Department, A.M. Reddy Memorial College of Engineering and Technology, Guntur, India. Shaik Mahaboob Subhani Budda Beeraiah Assistant professor, Civil Department, Priyadarshini Institute of Technology and Management, Guntur, India. The improvements in (3D) three–dimensional underlying examination and processing assets have permitted the effective and safe plan of taller constructions. These constructions are the outcome of expanding metropolitan densification and financial suitability. The pattern towards continuously taller constructions has requested a move from the conventional strength based plan approach of structures to an emphasis on obliging the general movement of the design. Presently a day’s supported cement (RC) divider outline structures are generally suggested for metropolitan development in zones with high SE danger. Presence of shear dividers bestows an enormous solidness to the sidelong power opposing arrangement of the RC building. Appropriate specifying of shear dividers can likewise prompt bendable conduct of such constructions during solid quake shaking. One of the remarkable boundaries impacting the shear divider (SD) SE (SE) conduct outline structures is the SD region proportion. In this manner a scientific examination is performed to assess the impact of Shear Wall Area to floor zone proportion (SW/FZP %) on the SE conduct of multistoried RC structures with delicate story at ground floor. For this reason, 12 structure plans that have Five, Eight and Twelve stories with SW/FZP % going somewhere in the range of 0.70% and 1.31% in the two ways are created. Here, the conduct of these plans under quake stacking is evaluated via doing Response Spectrum Analysis and Linear Time History Analysis utilizing primary examination programming E-TABS. Reaction Spectrum Analysis is finished by SE code IS 1893:2002. Straight Time History Analysis is completed by considering the three ground movement records to be specific Bhuj, Chamba and Uttarkasi. The primary boundaries considered in this investigation are the connection SW/FZP % has with base shear and rooftop dislodging, story uprooting and story float. The logical outcomes demonstrated that building plans with SW/FZP % equivalent to 1% acted sufficiently under tremor loads. Furthermore when the SW/FZP % expanded past 1% it is seen that the improvement of the SE presentation isn’t as huge. 04 30 2021 22 27 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.F8726.0410621 https://www.ijitee.org/portfolio-item/F87260410621/ Department of CSE, PDA College of Engineering, Kalaburagi, India. Kumar Shilpa Shubangi D C Head of the Department & Professor, Department of Computer Science, Visvesvarya Technological University, Kalaburagi, India. Osteoporosis is a disease in which bones become fragile and more likely to break. Osteoporosis can progress painlessly until it causes a bone fracture or a bone break. Dual Energy X-ray Absorptiometry (DEXA) is more costly and not accessible easily so we are using Fuzzy Inference system to predict osteoporosis. In this fuzzy logic, we collect risk factors and rules for osteoporosis and build a interface which take inputs and predicts if a person has osteoporosis. In the following Literature survey, we will take risk factors, rules, and ways to implement them. Around the world, 33% of women and 20% men over the age of 50 will suffer a fracture caused by Osteoporosis. Osteoporosis is a disease in which Bones become shallow and are fractured. If predicted before, quality of life will increase and severe surgery may be avoided. 04 30 2021 28 31 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.F8729.0410621 https://www.ijitee.org/portfolio-item/F87290410621/ Facultad de Ciencias Administrativas, Universidad Autonoma de Baja California, Mexicali, Baja California, Mexico. Roberto Carlos Valdes Hernández Juan Gabriel López Hernández Instituto de Ingeniería, Universidad Autonoma de Baja California, Mexicali, Baja California, Mexico. Adelaida Figueroa Villanueva Facultad de Ciencias Administrativas, Universidad Autonoma de Baja California, Mexicali, Baja California, Mexico. José Luis Arcos Vega Instituto de Ingeniería, Universidad Autonoma de Baja California, Mexicali, Baja California, Mexico. This work shows the need to apply information and communication technologies (ICTs) with a Programming Logic Controller (PLC) to control humidity by remote monitoring in the refrigeration process of an agricultural industry located in the Mexicali Valley of northwestern Mexico, where large amounts of orange are grown in this region of the country. This industry there were automated electronic devices (AEDs) for humidity control in the refrigeration area to prevent the deterioration of this agricultural product, only that the AEDs installed inside the evaluated company, did not operate properly due to the presence of outdoor atmospheric pollution (hydrogen sulfide (H2S)) and oxide sulfur (SO2)), and extreme climates in summer and winter (variations of temperature and relative humidity (RH))in this area of the country, that had an negative effect in indoors environments, and damaged their electrical connections. The principal climatic variables are the temperature and relative humidity (RH). The humidity in the refrigerated room for the orange keep its nutritional and physiological conditions, had to be between 45% and 55%. As AED, did not function properly, the climate inside the agricultural industry was not controlled, causing low and high humidity levels. With indexes below 30% humidity in the refrigerated room, oranges dehydrated and began to wilt, in addition to losing weight that was considered as unfit products for marketing. And at levels greater than 60% humidity, in this agricultural product microorganisms formed and caused rot, considered as a defective product and in both cases causing economic losses. The PLC was used with a wireless system and installed in an isolated room that only specialized personnel could enter if necessary. This was done in this way, to avoid deterioration of their electrical connections and that they did not operate as the AEDs and had adequate humidity control, to constantly maintain adequate humidity in the refrigerated room and avoid the generation of defective products. The research was made out from 2018 to 2019. 04 30 2021 91 100 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.F8747.0410621 https://www.ijitee.org/portfolio-item/F87470410621/ Research Scholar, Department of CSE, Annamalai University, Chidambaram, (Tamil Nadu), Assistant Professor, MVJ College of Engineering, Bangalore, India. Kumar R Dr. S Pazhanirajan Assistant Professor, Department of CSE, Annamalai University, Chidambaram, (Tamil Nadu), India. Diabetes Mellitus (DM) is a disease that can lead to a multi-organ malfunctioning in patients due to non-regulated diabetes. Recent advancements in machine learning (ML) and artificial intelligence, the early detection and diagnosis of DM is more advantageous than the manual diagnosis through an automated process. It this review, DM’s recognition, diagnosis and self-management techniques from six facets, namely DM datasets, techniques involved in pre-processing, extraction of features; identification through ML; classification and diagnosis of DM; intelligent DM assistant based on artificial intelligence; are thoroughly analyzed and presented. The findings of the previous research and their inferences are interpreted. This analysis also offers a comprehensive overview of DM detection and self-administration technologies that can be of use to the research community working in the field of automated DM detection and self-management. 04 30 2021 32 41 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.F8748.0410621 https://www.ijitee.org/portfolio-item/F87480410621/ Department of Mechanical Engineering, KG Reddy College of Engineering and Technology, Moinabad, (Telangana), India. K. Udaya Sri B S N Murthy Department of Mechanical Engineering, Gandhi Institute of Technology and Management, GITAM University, Vishakapatnam, (Andhra Pradesh), India. N. Mohan Rao Department of Mechanical Engineering, Jawaharlal Nehru Technological University, Kakinada, (Andhra Pradesh), India. This study presents an experimental and analytical investigation on the effects of using methyl ester of linseed oil (MELO)-diesel blend of B10, B20, and B30 with hydrogen injection of 5%, 10%, and 15% in a VCR (Variable Compression Ratio) diesel engine, operated with the compression ratios (CRs) of 15, 16, 17, and 18 on DFM (duel fuel mode). This study also gives emphasis on the optimized emissions of CO, CO2, NO, and smoke, when the engine was operated with MELO-diesel blends, and hydrogen injections with the variation in engine load, crank angle (CA), using response surface methodology (RSM) with the help of MINITAB programming. During the analysis it was observed that the emissions of CO, CO2, O2, NO, and smoke were found to be a function of biodiesel blends, compression ratios, load, and percentage of hydrogen injection. The research results report that, the dual fuel mode of diesel MELO 20% blend with hydrogen injection of about 10% gave optimized results in terms of performance and exhaust emissions, while the optimized CR was 17. The engine was smoothly operated with B20-H10-CR17 over lower emissions compared to diesel, throughout the load spectrum. 04 30 2021 42 49 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.F8765.0410621 https://www.ijitee.org/portfolio-item/F87650410621/ Research Student, Symbiosis Institute of Technology Symbiosis International (Deemed University), Pune, India. Anusha Poorva Agrawal Assistant Professor, Symbiosis Institute of Technology Symbiosis International (Deemed University), Pune, India. Gagandeep Kaur Assistant Professor, Symbiosis Institute of Technology Symbiosis International (Deemed University), Pune, India. Artificial intelligence is a new technology in the field of computer science and information technology. Artificial intelligence techniques are very useful and also applied in many businesses. There are many research areas and opportunities in the field of Artificial intelligence. Artificial intelligence refers to the simulation of human intelligence in machines. Artificial intelligence allows app developers to achieve better mobile application experiences and also helps to improve personalized selections for users. This paper explores the different areas of the artificial intelligence-based system in tourism. This paper presents important future research directions. 04 30 2021 50 53 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.F8786.0410621 https://www.ijitee.org/portfolio-item/F87860410621/ Asst. Professor, CSE Dept, Chaitanya Bharathi Institute of Technology, Hyderabad, India. M Venkata Krishna Reddy Dr. S. Pradeep Associate Professor, CSE Dept, Siddhartha Institute of Engineering and Technology, Hyderabad, India. Profound learning’s goes to the achievement of spurs in a large number and understudies to find out about the energizing innovation. At this regular process of novices to venture the multifaceted nature of comprehension and applying profound learning. We present Convolution Neural Network (CNN) EXPLAINER, an intelligent representation instrument intended for non-specialists to learn and inspect (CNN)-Convolution Neural Network a fundamental profound learning model engineering. Our apparatus tends to key difficulties that fledglings face in finding out about Convolution Neural Network, it can be distinguish from pointing with educators and input with past understudies. Convolution Neural Network firmly incorporates representation outline that sums up the construction of CNN, and on-request, dynamic visual clarification sees that assist clients with understanding the hidden parts of CNNs. Constantly polished changes across levels of deliberation, our device empowers clients to examine the exchange between low-level numerical activities and undeniable level model designs. A subjective client study shows that Convolution Neural Network EXPLAINER helps clients all the more effectively comprehend the inward operations of CNNs, and is drawing in and agreeable to utilize. We additionally determine plan exercises from our examination. Created utilizing current web innovations, CNN EXPLAINER runs locally in clients’ internet browsers without the requirement of establishment or particular equipment, widening the general preparation with current profound learning strategies. 04 30 2021 54 60 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.F8804.0410621 https://www.ijitee.org/portfolio-item/F88040410621/ Management Information Systems Department College of Business, King Khalid University, Abha, Saudi Arabia. Osman A. Nasr Abd Almajeed Alajab Management Information Systems Department College of Business, King Khalid University, Abha, Saudi Arabia. Khalid Mohiuddin Management Information Systems Department College of Business, King Khalid University, Abha, Saudi Arabia. Faiz Bin Zafrah Business Administration Department College of Business, King Khalid University, Abha, Saudi Arabia. Ahmed A. Ahmed Management Information Systems Department College of Business, King Khalid University, Abha, Saudi Arabia. The project entitled “Car Spare Parts Shopping Application” enables customer to buy car spare parts using mobile application. This application advertises a collection of branded and high-quality car spare parts available for shopping. For shopping, the intended customers must have an authentication. The customers with authentication will avail the entire benefits offer on the application. Customers who do not have the authentication credentials, such customers can access the application and view the products details and services for the customers. Necessary, a user must have an account to become a customer, meanwhile the intended customer can shop the items and place in the available shopping cart. Importantly, the application facilitates the users or customers with billing details and tracks the customers’ orders with successful shipment acknowledgement. 04 30 2021 61 66 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.F8814.0410621 https://www.ijitee.org/portfolio-item/F88140410621/ SCSE, Galgotias University, Greater Noida, India. Akash Gupta Rahat Ali SCSE, Galgotias University, Greater Noida, India. Abhay Pratap Singh SCSE, Galgotias University, Greater Noida, India. P.Raja Kumar SCSE, Galgotias University, Greater Noida, India. Nowdays we are witnessing the technology transforming everything the way we used to do things and how the automobile industry is transforming itself with the use of technology IOT, Artificial intelligence, Machine learning. Companies shifting its products and its utilities in diferent way and they now want to acquire and introduce level-5 autonomous to future generation and big automobile companies are trying to achieve autonomous vechicles and we have researhed about the model that will help in assisting autonomous vechicles and trying to achieve that. We will develop this model with help of technologies like Artificial intelligence, Machine learning, Deep learning. Autonomous vehcicles will become a reality on our roads in the near future. However, the absence of a human driver requires technical solutions for a range of issues, and these are still being developed and optimised. It is a great contribution for the automotive industry which is going towards innovation and economic growth. If we talking about some past decade the momentum of new research and the world is now at the very advanced stage of technological revolution. “Autonomous-driving” vehicles. The term Self-driving cars, autonomous car, or the driverless cars have different name with common objective. The main focus is to keep the human being out of the vehicle control loop and to relieve them from the task of driving. Everyday automotive technology researchers solve challenges. In the future, without human assistance, robots will produce autonomous vehicles using IoT technology based on customer needs and prefer that these vehicles are more secure and comfortable in mobility systems such as the movement of people or goods. We will build a deep neural network model that can classify traffic signs present in the image into different categories. With this model, we are able to read and understand traffic signs which are a very important task for all autonomous vehicles .This model we have tested it and resulted in 95% accuracy. 04 30 2021 67 73 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.F8821.0410621 https://www.ijitee.org/portfolio-item/F88210410621/ Department of Computer Science and Engineering, Srinivas University College of Engineering and Technology, Mangalore, Karnataka, India. Sarojini Yarramsetti Anvar Shathik J Department of Computer Science and Engineering, Srinivas University College of Engineering and Technology, Mangalore, Karnataka, India. Renisha. P.S Department of Computer Science and Engineering, Srinivas University College of Engineering and Technology, Mangalore, Karnataka, India. In this digital world, experience sharing, knowledge exploration, taught posting and other related social exploitations are common to every individual as well as social media/network such as Face Book, Twitter, etc plays a vital role in such kinds of activities. In general, many social network based sentimental feature extraction details and logics are available as well as many researchers work on that domain for last few years. But all those research specification are narrowed in the sense of building a way for estimating the opinions and sentiments with respect to the tweets and posts the user raised on the social network or any other related web interfacing medium. Many social network schemes provides an ability to the users to push the voice tweets and voice messages, so that the voice messages may contain some harmful as well as normal and important contents. In this paper, a new methodology is designed called Intensive Deep Learning based Voice Estimation Principle (IDLVEP), in which it is used to identify the voice message content and extract the features based on the Natural Language Processing (NLP) logic. The association of such Deep Learning and Natural Language Processing provides an efficient approach to build the powerful data processing model to identify the sentimental features from the social networking medium. This hybrid logic provides support for both text based and voice based tweet sentimental feature estimations. The Natural Language Processing principles assists the proposed approach of IDLVEP to extracts the voice content from the input message and provides a raw text content, based on that the deep learning principles classify the messages with respect to the estimation of harmful or normal tweets. The tweets raised by the user are initially sub-divided into two categories such as voice tweets and text tweets. The voice tweets will be taken care by the NLP principles and the text enabled tweets will be handled by means of deep learning principles, in which the voice tweets are also extracted and taken care by the deep learning principle only. The social network has two different faces such as provides support to developments as well as the same it provides a way to access that for harmful things. So, that this approach of IDLVEP identifies the harmful contents from the user tweets and remove that in an intelligent manner by using the proposed approach classification strategies. This paper concentrates on identifying the sentimental features from the user tweets and provides the harm free social network environment to the society. 04 30 2021 74 79 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.F8825.0410621 https://www.ijitee.org/portfolio-item/F88250410621/ Department of Physics, H.P.T. Arts and R.Y.K. Science College, Nashik, (M.S.) India. Ujwala M. Pagar U.P. Shinde Department of Physics, L.V.H. Arts, Science and Commerce College, Nashik, (M.S.) India. Thick films of NiO deposited on glass substrate by screen printing technique. The nano powder of AR grade NiO was used for the preparation of thick films. The X-ray diffraction (XRD), Scanning Electron Microscopy and Electrical Characterization was carried out for unannealed and annealed films. The annealed films were at 250 0 C-400 0 C in a muffle furnace. Using characterisation techniques, the success of the synthesised nanoparticles was confirmed. The x-ray diffraction was used for structural characterization which confirms the polycrystalline nature of the films with cubic structure. From the SEM analysis the films show uniformity, roughness, large crystals and agglomeration of particles. The SEM-EDS analysed morphology and chemical compositions. The correlations between structural and morphological properties are reported. The D.C. resistance of the films was measured by half bridge method in air atmosphere at 30OC to 350OC. From the electrical parameters the NiO films shows semiconducting nature. The TCR, activation energy and sheet resistivity, specific surface area were calculated at different annealing temperatures. The electrical conductivity at room temperature was calculated as 4.56 × 𝟏𝟎 −𝟒 (𝜴 ∙ 𝒎) −𝟏 . 04 30 2021 80 85 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.F8826.0410621 https://www.ijitee.org/portfolio-item/F88260410621/ Department of Electrical and Electronics Engineering, Don Bosco Institute of Technology, Bengalore, India. Sinan M W M Sivakumar Department of Electrical and Electronics Engineering, Don Bosco Institute of Technology, Bengalore, India. Anguraja R Department of Electrical and Electronics Engineering, Don Bosco Institute of Technology, Bengalore, India. The purpose of this research is to find the loading limit of a power system before hitting voltage instability and to assess the margin to voltage instability of a system consisting of a wind farm. An index called Bus Apparent Power Difference Criterion (BSDC) is used to find maximum loadable point. The measure depends on the way that in the region of the voltage collapse no extra apparent power can be delivered to the affected bus. The analysis is performed combination of wind power injection at different wind speeds and line outages in the network. In the feasibility and siting studies of wind farms the steady state analysis with network contingencies give the utility or the developer a sense of network condition upon the injection of power in the network. However, the extent of voltage stability impacted due to load growth in the system is not assessed. The research paper makes way to assess the impact on voltage stability margin with obtaining the maximum loadable point of the system and assessing the best suited bus to integrate a wind farm into the system. 04 30 2021 114 117 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.F8828.0410621 https://www.ijitee.org/portfolio-item/F88280410621/ Assistant Professor of Civil Engineering Department, School of Engineering & Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur, (C.G), India. Rochak Pandey Dr. Shailendra Kumar Professor of Civil Engineering Department, School of Engineering & Technology, Guru Ghasidas Vishwavidyalaya, Bilaspur (C.G), India. This study investigates the progressions in mechanical properties of concrete by substitution of cement by Rice Husk Ash (RHA) in conjunction with superplasticizers. Rice husk is a by-product of the rice milling process, with inexact generation of 200 kg for every one ton of rice. Diffusion of Rice Husk Ash in the concrete matrix enhance the properties of the concrete. This investigation has been done to determine the strength parameters of concrete with various extents of cement supplemented by Rice Husk Ash. M20 grade concrete (Designed as per Indian standards) was tried with substitutions by weight of the cement amount by 2.5%, 5%, 7.5%, 10% and 15%. Various strength Test results reveal enhancement of strength at 5% substitution of cement by rice husk with compressive strength and flexural strength having an increment of 9.78% and 25.09% respectively as compared to the control mix. Pulse velocity test of the modified concrete at 5% replacement of cement by rice husk ash confirms it as a “good” dense Concrete matrix. 04 30 2021 86 90 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.F8829.0410621 https://www.ijitee.org/portfolio-item/F88290410621/ Laboratory of Materials Physics and Subatomics. Faculty of Sciences- Ibn Tofail University, Kenitra, Morocco. Acharoui Zakia Ettaki Badia Laboratory of Research in Computer Science, Data Sciences and Knowledge Engineering, School of Information Sciences Rabat, Morocco. Zerouaoui Jamal Laboratory of Materials Physics and Subatomics. Faculty of Sciences- Ibn Tofail University, Kenitra, Morocco. People spend more time on social media either for personal or social interest which generates an expanding amount of Data. This paper is written for researchers seeking to have an overview of the different technical methods used for political purposes principally Data Mining and Social Network Analysis. Hence, the first part introduces the impact of Social Media on politics for different aims such as communicating with voters, promoting participation, and predicting election results, then the two main methods to achieve political purposes were presented. Data mining approaches is likely to be used on political context to classify citizen’s opinion or predicting results thus by using methods such as term occurrence, mentions, Support Vector Machine, Machine Learning, and Artificial Neural Networks. The Social Network Analysis approaches are used to retrieve data about influencers, their role during a period, and the nature of the information shared. 04 30 2021 118 122 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.F8832.0410621 https://www.ijitee.org/portfolio-item/F88320410621/ Political Science Faculty, International Relation Dept. Istanbul University, Istanbul, Turkey. Fatemeh Dehghan Khangahi Farzad Kiani Engineering and Architecture Faculty, Computer Engineering Dept. Istanbul Arel University, Istanbul, Turkey. Voluntary or compulsory immigration of people to other regions or countries for different reasons can lead to social, cultural, and economic problems. In recent years, especially the rate of forced migration has increased and it is sometimes chosen as a last resort for social mobilization. Scientists and governments who have gathered data on migration for years have recently realized that Artificial Intelligence (AI) and Machine Learning (ML) methods are important in analyzing this data and developing utility models and systems. It has been gradually understood that these new technologies are very important in recent years, but studies have either only been done in the field of social sciences or only in the field of engineering. In this study, a comprehensive interdisciplinary study covering both dimensions is prepared. In this study, a machine learning-based model is presented by making a multidisciplinary study and exemplifying the Lake Urmia case study. The proposed method can be used in the decision-making process in the migration management. In our study, is proposed a model using three different algorithms (Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN)). According to the results, the SVM-based model outperforms others in accuracy and validations. The trained model of SVM has a success rate on mean accuracy as near to 86% with 4,00E-02 standard deviation rate. SVM ranked first and this method was followed by RF and KNN methods, respectively. In this context, this model can make forward-looking predictions and, like an expert system, can guide the relevant researchers and even state or form ideas according to the results obtained from it. 04 30 2021 123 127 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijitee.org true 10.35940/ijitee.F8833.0410621 https://www.ijitee.org/portfolio-item/F88330410621/