bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automatic Solar Panel Cleaner Robot Using IoT
Authors: Kumar R , Ramapraba P S , Kishore Somala Rama , Jayanthi S 
Published: July 2021

Abstract
The solar panel operates by allowing light to penetrate solar cells. The more a panel is impacted, the more power it produces. The dust and bird drops are more likely to collect due to the solar windows’ upward angles. The mud that is not safe even with air. The same light effect is decreased on the panel, and sensor production is high. The producers and installers of solar panels claim the planned energy figures focused on optimum renewable solar panel efficiency. The radiation’s construction on a solar panel will impact the panel’s capacity to fulfill the predicted number. It is therefore essential and appropriate to clean the solar panel in order to secure and obtain further electricity. Thus, we build and clean the solar panel, and increase panel capacity, for the automated system.
Vehicular Formed Network Coding Conscious Connectivity for Sensor Networks
Authors: Sivakumar P , Lenin J , Asha Shiny X S , Vanisree K 
Published: July 2021

Abstract
With wireless communication, resource efficiency is an important issue. Routing protocols has considered to be a successful technology in recent years, reducing transmission numbers, saving energy and improving network efficiency, especially appropriate for Wireless Networks that drive network coding expertise. However, current network coding knowing routing issues with failed network coding due to faulty conditions. Established network coding, on the other hand, typically takes into account the ideal traffic planning schedule and overlooked resource and load checkpoints that are critical for wireless communication. Therefore, current network coding in the information of routings on wireless sensor networks cannot be implemented explicitly. With regard to the problems mentioned above, this paper proposes the TSCAR for wireless sensor systems for routing in the context of a traffic system. A standard network coding criterion is set out in TSCAR to overcome the fake sequencing problem. In order to produce further coding behaviour as coding openings occur, a method for influencing the transport of various flows is also suggested. Furthermore, the TSCAR delay is analysed by the network calculus. In order to quantify the tracks found, a codification, load, also energy-aware metrics are also planned. Extensive simulation findings show that the TSCAR improves the amount of coding possibilities in addition the percentage of coded packages, boosts system efficiency, then extends wireless network sensor network life.
Design of Low-Cost Object Identification Module for Culinary Applications
Authors: Pranav Vijay Chakilam B , Revanth  , Muppirala Vyshnavi , Anilet Bala A , Maik Vivek 
Published: July 2021

Abstract
Using up one’s fridge’s contents often requires touch creativity, which is typically hard to come by in a very busy world. This aim is to style a model that may scan the things in one’s fridge and recommend recipes that supported what one has got, even taking one’s preferences and dietary restrictions under consideration. Computers are commencing to process globe objects without the requirement for codes or human intervention in their language. This project aims to create a module that uses object recognition to detect vegetables and fruits and display recipes that include those foods. This concept uses a camera that is installed in a refrigerator to show it into a sensible one. The system identifies objects that are placed inside using complex object recognition algorithms using the camera. If a vegetable or fruit is placed inside, the system will identify it and displays its name on the screen. It creates an indication of the chances of object recognition. The platform enables users to put any number of various vegetables into the refrigerator. The system then checks a list of recipes that contain the ingredients inside the refrigerator and filters those that feature the vegetables available. Together with this, we also aim to watch the refrigerator’s contents and find the freshness and age using various sensors and provide inventory and warnings when they are on the verge of completion.
Multiple Network and Double System Orthogonal Low-Ranking Training Melanoma Image Grouping
Authors: Sureshkumar R , Yuvaraj S A , Kuppusamy P G , Dilliraj E 
Published: July 2021

AbstractA significant method to identify and track early cancer of the breast in clinical practise is histopathological image analysis. The diagnosis of breast cancer is still facing issues with an open in healthcare sector, however, with a limited quality. We are creating a classification system based on histological picture pictures, integrating deep learning with mechanical methodologies of learning, in sequence to enhance the prediction of early recognized breast cancer and to minimize the work pressure of physicians. In particular, through pre-trained Deep Convection Neural Networks, we build a multi-network extraction model, create an efficient method of reducing features and train an ecosystem supporting vector machine (E-SVM). Next, we use scale transformation and colour improvement approaches to prepare histological pictures. Second, four pre-trained DCNNs extract the multi-network functionality. Thirdly, the Dual Network Orthogonal Low-Rank Learning (DOLL) role selection approach is further introduced to increase efficiency and to reduce unnecessary efficiencies. An E-SVM is at last instructed by melded usefulness and casting a voting procedure for characterization, what isolates the pictures into four gatherings considerate, in situ carcinomas, obtrusive carcinomas, and ordinary. The suggested procedure is tested by us for the public ICIAR 2018 Challenges Data Set on histology photographs of breast cancer. Our approach can offer very promising productivity and underperform province approaches through analytical outcomes.
Implementation of Quantum-Dot Cellular Automata Based Efficient N-Bit BCD Adders Using Verilog
Authors: Qureshi Vaseem Ahmed , Lavanya L , Goel Anuj Kumar 
Published: July 2021

Abstract
Quantum Dot Handheld Automatics is one of the best technologies in recent days for introducing a high speed and ultra-low powered digital circuit. Few binary and decimal arithmetic circuits were designed using an efficacious QCA-based algorithm. However, there is a substantial enhancement required yet if there is inbuilt availability of logic gates inside the QCA. Therefore, this article introduced innovative binary coded decimal (BCD) adders using QCA methodology. Simulation analysis discloses that proposed BCD adders obtained higher computational speed with lower delays and lesser area than conventional CMOS approaches.
A Comprehensive Review on Machine Learning Based Optimization Algorithms for Antenna Design
Authors: Seshu Kumar N , Yalavarthi Usha Devi 
Published: July 2021

Abstract
Machine learning has become a great attention to find optimize solutions in different areas and is anticipated to play a vital role in our upcoming technologies. This paper presents a comprehensive review on basic optimization algorithms for micro-strip patch antenna design using machine learning. Classification of machine learning based algorithms: deterministic, stochastic and surrogate model assistant is discussed. Further machine learning models training for optimizing output and for prediction of antenna parameters is presented in this paper. This paper is useful to the readers who work on a particular antenna using the Machine Learning Techniques.
A Numerical Analysis of Blood Flow in Clogged Artery
Authors: Sahu Nilesh , Sharama Ayush , Kumar Pankaj 
Published: July 2021

Abstract
The artery wall gets stressed due to the variation in pressure of the blood flow passing through it. When this pressure subsides, these arterial walls dilate in reaction to increasing blood pressure and contract. The additional simulation focused on determining when there is a blockage inside an artery, the velocity and wall shear stress. The current simulation focused on determining the velocity, pressure and wall shear stress when there is a different stages of blockage present inside an artery. This paper has extended the simulation with the blockage developed at the symmetric or asymmetric condition inside the arteries. The analysis of blockage expressed with flow contour, pressure, wall shear stress and velocity. This work explains completed simulations with 10% to 80% blockage at different Reynolds number as 50,100,500,1000, and 4000. More particularly, focused on understanding the functioning and morbidity related to vital organs of the human anatomy with maximum shear stress. The result suggested that how these parameters vary according to the above stated cases and the stage of critical situation. The parameters set in the study at hand aim to resolve the complexity of the procedure by eons and can be utilized in placing a design of set apparatus which will greatly help in increasing the mortality among ailing humans.
Band gap engineering on CdS quantum dots through dual doping of Zn2+ and of Ni2+ ions
Authors: Sakthivel P. , Devadoss I. , Krishnamoorthy A. 
Published: July 2021

Abstract
The CdS Quantum dots (QDs) have been synthesized without using any capping agent via a co-precipitation chemical route. The structural and optical investigations of the Ni2+-doped Cd0.9Zn0.1S QDs have been made. XRD patterns revealed that the synthesized QDs exhibit cubic structure and the particle size was measured ∼ 2 nm. EDX spectra showed that the existence of cadmium, zinc, sulphide, and nickel in the synthesized samples. FTIR spectra verified the vibrational peaks related to available molecules of elements in the samples. The absorbance rate of Ni2+-doped Cd0.9Zn0.1S QDs doped was more than Cd0.9Zn0.1S QDs in the visible region. The introduction of Ni2+ ions into CdZnS QDs improved the optical transmittance in the visible spectrum and the band gap was blue-shifted for Ni = 6 at.% composition. The quantum confinement effect played an effective role in the band gap tailoring of CdS QDs via particle size tuning mechanism.
Analysis of Numerical Method on Inverse Kinematics of Robotic Arm Welding with Artificial Intelligence
Authors: Yaseen Syed , Prakash Jaya 
Published: July 2021

Abstract
The key component of versatile production processes is robots. They are found in diverse systems where it is possible to bypass and optimize human function. I simulated a remote-control trickster in MATLAB with 6 degrees of freedom in this research. A robot is used in numerous applications, such as decoration, carpentry and equipment testing. In hardware verification labs, robotic arms are used to hold passive and power rail probes that connect from instruments like scopes and power supplies to PCB boards to protect the PCB layout from rip off due to sudden movement of the probes. Robot kinematics uses the geometry (position and orientation) of rigid bodies (links) and joints to control the movement of the robot. In this project, to control its movement, I have illustrated either forward or reverse enzyme kinetics of a device. Through Kinematics uses the positions of a joint to measure the end-effector position of the robot. The angles of the joints with the end-effector position are determined by inverse kinematics as the reference. There are several methods to calculate the forward and inverse kinematics such as analytical methods, numerical hit and trial, and iterative methods.
Comprehensive Analysis of Machine Learning Algorithms Used in Robo-Advisory Services
Authors: Potdar Akhilesh , Pande Mandaar 
Published: July 2021

Abstract
Adoption of Robo advisory is rapidly increasing among retail investors. Approximately 10% of all assets under management will be managed by them by 2025. Researchers continuously work on finding machine learning and deep learning algorithms which will accurately predict the stock price movement with less error. Various Robo advisor platform providers are working on different approaches like NLP (Natural language algorithm) based sentiment analysis, hybrid time series algorithms to limit human bias, which existed in the current financial advisory services. This research compared the performance of various forecasting algorithms, which can be used in the Robo advisory framework. RMSE (Root mean square value) value is taken into consideration in this research to compare the performance of algorithms as it is a good and simple loss function. It has been observed that newer algorithms like those proposed by Facebook are able to predict stock prices pretty well, but ARIMA outperformed all the algorithms which are considered in this research.
Synthesis of Images from SMRI through Extreme Integrand Unstable Learning
Authors: Tamilselvi M , Dappuri Bhasker , Tapas Bapu B R , Baburaj E 
Published: July 2021

Abstract
In conventional deep learning medical imagery research, appropriate healthcare images are often invaluable. Still, it can limit acquisitions of such texture features because of many concerns, such as high cost, patient problems, etc. However, due to recent developments in profound education techniques that can greatly ease the challenge mentioned above, synthesizing medical photographs has already synthesized different modalities such as MRI images, PEI images, heart infrared detector, retinal images, etc. Unfortunately, a synthesis picture of the Arterial Spin Marking, now an important fMRI predictor in diagnosing dementia disorders, has not yet been thoroughly studied. For the first time in this research, ASL images from magnetic resonance structural images have been prepared successfully. Theoretically, ASL objects’ production from functional magnetic resonance imaging will be indicated by a new, highly unstable, discrimination-based paradigm fitted with new resNet post carried out a broad variety of tests. Useful statistical evaluation of this newly released model to synthesize ASL pictures close to the actual ones acquired during the actual scanning of ASL photographs from the current model shows excellent performance while undergoing extreme regional and voxel-based partial volume correction checks which are necessary for ASL pictures.
An Integrated and Sustainable Model for E-Waste Management for Pune City Households
Authors: Bhat Viraja , Patil Yogesh 
Published: July 2021

Abstract
Worldwide use of electronic devices has led to a serious socio-economic-technical challenge for managing them at their end-of-life stage. Managing EOL electronic devices or e-waste using traditional methods has been potentially hazardous to the environment and health. United nation’ sustainable development goals also have linked e-waste to three of its SDGs and support the sustainable consumption and quantification of e-waste. Policy interventions, consumer awareness, and active environmentalist roles have forced developed nations to engage in scientific ways of e-waste treatment. The high cost of treatment and increasing volume has led to a scenario where developing nations, including India, have been more attractive and easy targets for dumping e-waste. Out of 1.7 million tons of e-waste generated in India, 8% of e-waste reaches the landfill as useless material, 1.5% of which are formal recycling, and the informal sector handles more than 90%. Ineffective policy implementation and lack of stakeholder participation pose a challenge in improving the scenario of e-waste management. Although, there is many stakeholders have been brought into the sphere of policy, household shave not been considered major generators, with about 16% of the total e-waste. Growing urbanization increased dependency on electronic product usage due to the pandemic; we need to channel the household e-waste to the formal treatment units with a systematic approach. Pune is one of the proposed smart cities and has been in the news due to the solid waste treatment challenges due to the treatment sites and infrastructure. With municipal solid waste, e-waste collection and treatment at the regular treatment facilities, not equipped to handle e-waste leads to untreated e-waste parts in whole or in traces. Lack of awareness and non-segregation at source, inadequate collection channels has impacted the environment, requiring a focused and workable systematic framework for household generated e-waste disposal. Access to disposal at the doorstep develops an integral behavior that can be altered by providing proper doorstep MSW disposal infrastructure as an extension of the existing municipal solid waste segregation model. This paper proposes a theoretical, sustainable, and integrated model built with the current MSW model foundation aimed at household e-waste management, providing collection and inventory services for Pune residents.
MeDrone- A Smart Drone to Distribute Drugs to Avoid Human Intervention and Social Distancing to Defeat COVID-19 Pandemic for Indian Hospital
Authors: Maheswari R , Ganesan R , Venusamy Kanagaraj 
Published: July 2021

Abstract
Many healthcare centres generally have a centralized unit-dose drug distribution system (CUDD). The in-patient’s drugs are warehoused in a centralized location such as hospital pharmacy. The required or prescribed drugs available in the pharmacy will be distributed to the patient by either the nurse or attendant. The attendant often collects the medicine from the pharmacy. This process is quite time-consuming and in this COVID‘19 pandemic should maintain social distancing by the attendant in the pharmacy. In a field where time is of great importance and can save lives and also, a human intervention which in turn need to maintain social distancing, we can use MeDrones that will dispense medicines to the designated patient(s) location which will avoid human intervention and social distancing to defeat COVID-19 pandemic which is the need of the hour. The drone will be designed to deliver payload (tablets/saline) within/across the hospital premises. Apart from delivering medicines to the respective patients, the drone will be equipped to decide the optimal path to reach patients and prioritize attending to critical patients based on the hospital’s central database. The drones can be customized to return the unused medicines from the patient’s location to the pharmacy. Lightweight secured drugs container kit with authentication enabled will be designed for carrying the medicines to distribute to the respective destination inside the healthcare centre. The drone regulations result from increasing demand in UAV that must be devised to permit adaptation extensively as a logistics solution, to escalate efficacy in the healthcare domain.
An Efficient Algorithm for Increasing Modularity in IoT Based Automation Systems
Authors: Kumar Sumit , Singh Abhaya Pal , Ghosh Aritra 
Published: July 2021

Abstract
IoT-based platforms allow for an integrated approach to digital systems. With the increase in complexity of devices and processes in industry and commerce, automation presents itself as a technological panacea in the form of IoT (Internet of Devices) based applications. This paper presents an adaptive, efficient, and affordable system architecture through which modularity in IoT-based automation can be realized. The proposed technique involves an IoT-based platform to coordinate, control also calibrate microcontrollers connected to various sensors and devices. Using a web page or application as an interface, the system can be configured to operate diversified workloads via distributive control and remote monitoring. The flexible nature of the system allows for a modular approach to parameter design and system integration while allowing dynamic task allocation. Data available through feedback can be used to further streamline and orient tasks in real-time. This allows a single platform to multitask using an adaptive algorithm.
Towards Development of Performance Metrics for Benchmarking SLAM Algorithms
Authors: Bhargava Mudit , Mehta Rushad , Adhikari Chandan Das , Sivanathan K 
Published: July 2021

Abstract
The true autonomy of mobile robots cannot be achieved without Simultaneous Localization and Mapping (SLAM). With this capability, mobile robot could concurrently build a map of the environment and locate itself with respect to the map. Although there are several variants of SLAM algorithms contributed by researchers so far, only a very few works were aimed at comparing their performances with appropriate metrics and providing detailed directions and insights to the user on selection criteria and indicative use cases. In this work, we presented a comparative study of three popular SLAM algorithms and provide some significant quantitative performance measures of the same by using our novel | R | and | S | performance metrics as well as conventional metrics. The comparative study was carried out in ROS (Robot Operating System) using Turtlebot3 robot model on three SLAM packages viz G-mapping, Karto SLAM, and Frontier Exploration SLAM. Furthermore, the results show that the proposed metrics are very efficient and compact in comparing and quantifying the performance of SLAM algorithms.
COVID-19 Death Risk Assessment in Iran using Artificial Neural Network
Authors: Adebayo Ifeoluwapo R , Supriyanto Eko , Taheri Sahar 
Published: July 2021

Abstract
Since the pandemic spread of COVID-19, it has posed a unique public health concern worldwide due to its increased death rate all around the world. The pandemic disease is caused by the SARS-CoV-2, which is the main cause of Middle East Respiratory Syndrome (MERS) and severe acute respiratory syndrome (SARS). Risk assessment is a vital action toward disease risk reduction as it increases the understanding of the risk factors associated with the disease and allows existing data to decide on adequate preventive and mitigation measures. Machine learning techniques have gained strength since 2000, as it has crucial role in data analysis and is really helpful to develop standard mortality models. This study aims to find the best model for data analysis using the Artificial Neural Network (ANN) and other risk factors, which contribute to the high mortality and morbidity associated with COVID-19 in Iran, to predict the risk of death for the people with different situation. A systematic review and meta-analysis were examined by using patient risk factor data from studies done by researchers to estimate COVID-19 death risk. Risk factors for the disease were extracted from an existing study. Using ANN, the best risk prediction for the disease is calculated. Assessment of a different number of hidden neurons with a different training function using the Bayesian Regularization algorithm, the best training function for the ANN model with 5 hidden neurons is found to have the most satisfying results. The coefficient of determination (R) and Root Mean Square Error (RMSE) was 9.99999e-1 and 4.54201e-19 respectively.
Awareness of Reuse Reduce Recycle & Dispose of E-Waste in Chennai
Authors: Ganesan R , Ramesh B , Teja Charan 
Published: July 2021

Abstract
In this survey, I came to know that the development of area or city leads to the growth of E-waste, but the percentage of the public who are aware of E-waste is not growing up to the mark. The technical solutions, segregation process, preventive methods, skilled labors working on E-waste, modern technology adopted for disposal are very less in growing cities when compare to the metropolitan cities. I came to know about the urgency of creating awareness to every person using electronic gadgets about E-waste and its ill-effects on the human race. Segregation of metals and components while disposing of the E-waste also causes ill effects for the workers involved in that, as facilities like gloves, caps, masks are not available in poor countries. Dumping of E-waste into seas or in barren islands leads to water pollution, affecting aquatic and marine life. As most of Chennai is a coastal area, many small and medium industries and household E-wastes are releasing into the seas easily. The survey conducted in Chennai city reveals the immediate reaction to the proper disposal of E-waste.