-74813b3e17f460286df245a 20220708065850048 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/ 07 30 2022 11 2 Department of Electronics and Telecommunication Engineering, JD College of Engineering & Management an Autonomous College Affiliated to DBATU, Lonere (Maharashtra), India. Princy Vaidya Dr. Sanjay Haridas Dean (Academics), JD College of Engineering & Management an Autonomous College Affiliated to DBATU, Lonere, India. Avinash Ikhar Department of Electronics and Telecommunication Engineering, JD College of Engineering & Management an Autonomous College Affiliated to DBATU, Lonere, India. Pedestrians, ridden or herded animals, vehicles, streetcars, and buses are all instances of road users who travel alone or in groups on public roadways. Both informal and official standards are included in the phrase "road regulations." standards and legislation that have emerged over time to help keep traffic flowing smoothly and efficiently. The informal rules and legislation that have developed over time to facilitate the orderly and timely flow of traffic are known as rules of the road. In structured traffic, terms like priorities, lanes, right-of-way, and traffic management have specific definitions. Heavy motor vehicles (cars, trucks), other vehicles (mopeds, bicycles), and pedestrians are the three types of traffic. Some countries have complicated and detailed traffic laws, while others rely on common sense and driver cooperation. In terms of travel, the organization t gives a better mix of safety and efficiency. Road work, garbage, and street collisions can all obstruct traffic flow and transform it into a chaotic mess. On heavily packed freeways, a minor disruption will persist, a phenomenon known as traffic waves. A complete breakdown of organization can result in gridlock and traffic congestion. In simulations of organized traffic, stochastic processes, queuing theory, and mathematical physics equations are widely used. 07 30 2022 113 116 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.B7158.0711222 https://www.ijrte.org/portfolio-item/b71580711222/ HOD, Department of Electronics and Telecommunication, Mumbai university, Mumbai (Maharashtra), India. Umesh Mhapankar Mr. Milind Shah Department of Electronics and Telecommunication, Mumbai University, Mumbai (Maharashtra), India. People with autism speech disorders, paralysis, or muteness cannot communicate via speech. These individuals can think but cannot express and create overt speech. As a result, the system must obtain and interpret the electric and magnetic signal developed at the Scalp during imagined or intended speech. These magnetic signals are termed MEG (Magnetoencephalography), and electrical signals are named EEG (Electroencephalography). This technology must be wearable, non-invasive, and easy to use daily. To make the system wearable, the location of the electrode is essential. Since the EEG has good temporal resolution but poor spatial resolution, mapping the area on the Scalp of imagined speech is difficult. Similarly, the MEG has an excellent spatial resolution. But the MEG signal is weak, only up to 10-9 T to detect. Therefore, the delicate magnetic field in the brain due to imagined speech can be seen only by an OPM (Optically Pumped Magnetometer) or SQUID sensor. This paper explores a slightly different type of sensor based on an optically pumped magnetometer with a low cost as the cost of SQUID and OPM is large. A self-made magnetic sensor is used to map the location on the Scalp. The MEG and EEG measurement has been done in terms of PSD (Power Spectral Density). An analysis calculates the deviation compared with a different located point on the Scalp. The area on the Scalp of imagined speech was selected with the help of a literature review. The EEG measurement has done to confirm the location. 07 30 2022 117 121 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.B7144.0711222 https://www.ijrte.org/portfolio-item/b71440711222/ M.Tech Student, Department of Mechanical Engineering, MIT ADT University, Pune (Maharashtra), India. Aalhad Satav Nishigandha Patel Assistant Professor, Department of Mechanical Engineering, MIT ADT University, Pune (Maharashtra), India. The issue of vehicle accidents due to human error and behavior (rash driving, drink and drive, etc.) is on rising resulting into fatality and economical loss. In order to overcome these issues, the autonomous vehicle is being explored as a probable solution. Driver will be replaced by AI based autonomous system; The main objective is to convert the manual operated EV into autonomous vehicle. The project work is divided into three main parts i.e., Control System for Vehicle Control, AI (Artificial Intelligence) and UI (User Interface). The main objective of User Interface is to give information regarding vehicles health i.e. battery remaining, charging time, distance to be covered, etc. AI will be use as decision making system or we can say brain of the autonomous vehicle. System Control or Vehicle Control System is to control the vehicle’s steering, acceleration and braking system with the help of PID controller while the vehicle is being operated in autonomous mode. The main purpose is to make an autonomous EV for the passengers to go to their selected destination with a single click by selecting destination on display (UI). So the vehicle control is responsible for the vehicle’s steering i.e., at what desired angle the vehicle need to turn, during acceleration the factors that are considered are; at what speed the vehicle needs to move in an straight path or during a turn and the braking system is operated with the help of LIDAR where if it detects an object at what distance it needs to apply the brakes and when to release the brakes. All the factors and conditions required in developing the autonomous vehicle will be considered, as use cases to improve the accuracy of autonomous vehicle, resulting into achieving desired objective of safe travel. 07 30 2022 122 127 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.B7151.0711222 https://www.ijrte.org/portfolio-item/b71510711222/ Assistant Professor, Department of Computer Science, Vishwakarma Institution of Information Technology, Pune (Maharashtra), India. Mrs. Disha Sushant Wankhede Rohan Rajendra Sadawarte Students, Department of Computer Science, Vishwakarma Institution of Information Technology, Pune (Maharashtra), India Mahek Ibrahim Mulla Students, Department of Computer Science, Vishwakarma Institution of Information Technology, Pune (Maharashtra), India Shreya Rahul Jadhav Students, Department of Computer Science, Vishwakarma Institution of Information Technology, Pune (Maharashtra), India Predicting the rise or fall of an epidemic or pandemic is an essential part of establishing control over it. Post-World War 1, when there was an outbreak of the “Black Plague” there weren’t any means to analyze and predict. Although today we are equipped with tools like Machine Learning and Artificial Intelligence which have certainly enabled us to prevent unnecessary loss of life. It helps prepare the health officials to build the infrastructure and interpret the intensity of preparedness regulation of resources. The aim of this survey is to analyze and shed some light on the various algorithms and methods such as - regression models, neural networks, ARIMA, etc. Before building any model, gathering and processing the data is also essential. Hence our paper also focuses on which social media platforms proved beneficial in comparison to all we found and then made fit to be incorporated into the models. While researching for this paper, we observed that every disease has a different transmission type that leads to an outbreak and is a key factor in constructing a model. The literature evaluation in this work is centered on various prediction algorithms and their strategies for extracting online data from social media sites like Facebook and Twitter, all of which have drawn a lot of interest in early disease diagnosis for public health. 07 30 2022 128 137 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.B7160.0711222 https://www.ijrte.org/portfolio-item/b71600711222/ Department of Computer Science Engineering, S.R.M. Institute of Science and Technology, Chennai (Tamil Nadu), India. Amnaya Pradhan Neha Sharma Department of Computer Application, Panjab University, (Chandigarh), India. Throughout the globe, 1.6 million people annually fall prey to dia- betes. And an alarming total of 422 million people throughout the world have been diagnosed with diabetes, most of the contribution to this number being from low and middleincome countries. Diabetic retinopathy is the number one cause of blindness in the world. It generally affects people from ages 25 to 65. It occurs when the blood vessels present in the retina get damaged by hyper - glycemia or prevents blood from passing through the eyes. It is crucial to treat diabetic retinopathy early. If left untreated, it eventually leads to blindness. The proposed methodology is to use Convolutional Neural Networks with ResNet in order to diagnose diabetic retinopathy. Fundal cameras are used to obtain retinal images. The aim is to detect and prevent this disease, where it is challenging to perform medical tests. As per the research study, the images will be prepro- cessed, segmented, enhanced, and then the extraction of features such as micro aneurysms and hemorrhages will occur. Based on this, the disease will be clas- sified into mild, moderate, severe, or proliferative. In the future, this model may also be used to detect other conditions, such as glaucoma and macular degener- ation. 07 30 2022 138 143 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.B7175.0711222 https://www.ijrte.org/portfolio-item/b71750711222/ Software Engineering Intern, Dassault Systemes Solutions Lab Private Ltd., Mumbai (Maharashtra), India. Sneha Magar Prof. Mangesh Dhavalikar Principal Investigator, MIT Art, Design & Technology University, Loni Kalbhor (Maharashtra), India. Stamping and inspection are tasks that have been done manually since the beginning of time. Doing these tasks manually is time consuming, there is a possibility of error in it and it gets hectic after a while so it cannot be performed continuously. Automation has been simplifying and reducing the possibility of error over the recent years. In this project we will be designing and developing a prototype for stamping and inspection of metal cans using pneumatics and programmable logic controller (PLC). The stamping is done using the mechanism of quick retrieval by pressurized air. We aim to inspect cans of different sizes and make the system compatible to a wider range of industries. Different sizes of cans will require different pressure while stamping which will be programmed and stamping will be done accordingly with the required pressure. We can further develop the system to automate the whole production including pouring of material in cans, lidding, packaging, stamping and sorting. This work will propose a 3D design on the electropneumatic system and all the components used will also be discussed. This article is unique due to the concept of using one conveyor belt for two different sizes of cans. With the help of this design we can design different systems which can be compatible for multiple products in different industries. This might turn out to be a great solution for small scale industries that want to automate their plants but have not been able to due to high initial cost. 07 30 2022 144 148 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.B7178.0711222 https://www.ijrte.org/portfolio-item/b71780711222/ Department of Electrical & Electronics Engineering, Shri Shankaracharya Technical Campus, Bhilai (Chhattisgarh), India. Manish Kashyap Dr. Achala Jain Department of Electrical & Electronics Engineering, Shri Shankaracharya Technical Campus, Bhilai (Chhattisgarh), India. Vinita Swarnakar Department of Electrical & Electronics Engineering, Shri Shankaracharya Technical Campus, Bhilai (Chhattisgarh), India, In this paper, the ELD problem is resolved via ABC (Artificial BEE Colony) technique. The major goal of this study is to use the IDO method to present very efficient & reliable approach for solving ED problem in Power system. The suggested approach is used to solve a variety of non-convex ED issues, including banned operating zones with ramp rate constraints. This problem is described as an optimization of the objective function and minimization of the overall operating cost while gratifying all allied constraints, accompanied by the lowest down & up time limitations, startup cost, and spinning reserve. A six generators scheduling problem is discussed, along with its formulation, representation, and simulation result. 07 30 2022 149 152 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.B7180.0711222 https://www.ijrte.org/portfolio-item/b71800711222/ Research Scholar, Department of Computer Science and Engineering, Sethu Institute of Technology, Pulloor, Kariapatti (Tamil Nadu), India. S. Asha Dr. M. Parvathy Professor and Head, Department of Computer Science and Engineering, Sethu Institute of Technology, Pulloor, Kariapatti (Tamil Nadu), India. Since the last few decades, image denoising is one of the most widely concentrated areas of research in the domain of image processing. A wide variety of denoising algorithms have been explored to date, but the problem of noise prevention in Magnetic Resonance Images is still a great barrier to the diagnosis and treatment of certain diseases. This paper mainly focuses on the study and analysis of different Denoising algorithms, the type of noise handled, and their efficiency. Preprocessing of medical images is considered one of the important steps that can enhance the accuracy in the prediction of various diseases. The presence of noise and other artifacts are believed to degrade the prediction accuracy which is the important metric that directs physicians to prolong further in providing clinical guidance to the patients. Most of the algorithms perform denoising in the complex domain. Deep learning-based Denoising algorithms are found to produce more promising results than traditional ones. However, the number of training samples and the training time are some limitations worth mentioning. Magnetic Resonance Images are sources of input for medical diagnosis of a variety of diseases. On removal of noise, these images can go a long way in the early diagnosis of numerous fatal diseases and can save lives. The predominant objective of this summary is to direct the researchers to choose prompt denoising techniques appropriate for their applications despite the available limitations in the same. This review is comprehended with the main aim of suggesting effective image denoising approaches that can go a long way in enhancing the quality of biomedical images. 07 30 2022 153 158 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.B7181.0711222 https://www.ijrte.org/portfolio-item/b71810711222/ Ph.D. (Pursing), Sriram Chandra Bhanja Deo University, Baripada (Odisha), India. Sidhartha Sankar Dora Dr. P.K. Swain Assistant Professor, Department of Computer Application, North Orissa University, India. The applications of wireless sensor networks are increased day by day for different applications. Heterogeneous wireless sensor networks offer limitless possibilities because to their expandable capabilities, such as diverse computing power and sensing range, but they also represent significant issues due to the scarcity of energy, which is typically non-renewable. The node deployment, coverage area, connectivity, power depletion and network life time are major issues in wireless sensor networks. We have deployed heterogeneous sensor nodes on the basis of energy to design a heterogeneous network model and applied intelligent node deployment techniques by using metaheuristic algorithms such as genetic algorithm (GA) and particle swarm optimization (PSO) algorithm to minimize the power depletion of sensor nodes for enhancing network life time using multi-hop transmission in this paper. The benefits of heterogeneity have been revealed by our experimental findings. 07 30 2022 159 164 CC BY-NC-ND 4.0 10.35940/BEIESP.CrossMarkPolicy www.ijrte.org true 10.35940/ijrte.B7182.0711222 https://www.ijrte.org/portfolio-item/b71820711222/