Recent Trends in Parallel Computing (RTPC) ISSN: 2393-8749 Click here for complete Editorial Board Scientific Journal Impact Factor (SJIF): 6.102 Recent Trends in Parallel Computing (RTPC) is a journal focused towards the rapid publication of fundamental research papers on all areas of Parallel Computing. It's a triannual journal, started in 2014. Journal DOI no.: 10.37591/ RTPC Focus and Scope Cover Tree, Diamond Network, Mesh, Linear Array, Star, Hypercube, Chordal ring, Cube-connected-cycles. ILLIAC IV, Torus, PM2I, Butterfly, Mesh-of-tree Pyramid, Generalized Hypercube Twisted Cube, Folded Hypercube Cross-connected Cube Parallel Architectures: Shared Memory, Scalable Multi-Processors, Interconnection networks. Task and Data parallelism, Programming for performance Multi-Core programming: Properties of Multi-Core architectures, Pthreads, OpenMP. GPU, Accelerators. Multi-core architectures. Parallel programs on multi-core machines. Readership: Graduate, Postgraduate, Research Scholar, Faculties, Institutions, and in IT Companies. Indexing: The Journal is index in DRJI, Google Scholar Submission of Paper: All contributions to the journal are rigorously refereed and are selected on the basis of quality and originality of the work. The journal publishes the most significant new research papers or any other original contribution in the form of reviews and reports on new concepts in all areas pertaining to its scope and research being done in the world, thus ensuring its scientific priority and significance. Manuscripts are invited from academicians, students, research scholars and faculties for publication consideration. Papers are accepted for editorial consideration through email info@stmjournals.com or nikita@stmjournals.com Abbreviation: RTPC Frequency: Three issues per year Peer Reviewed Policy Editorial Board Instructions to Authors Publisher: STM Journals A division of: Consortium eLearning Network Private Ltd Address: A-118, 1st Floor, Sector-63, Noida, Uttar Pradesh-201301, India Phone no.: 0120-4746-214/ Email: nikita@stmjournals.com
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Research on Cyber Offenses under Information Technology Act, 2000
Indians have switched to online technology and services mainly after the demonetization that took place in November 2016. Also, the Digital India campaign launched by the Government of India encourages people to use online services by making them easily available. Such technological advancements come with cybercrimes and so, Cyber Security is very essential especially in 2021. There have been multiple incidents where the world has encountered cyber-attacks which almost lead to cyber warfare. To avoid such incidents from happening again, the Indian Parliament has formed the IT Act, 2000. It covers all the crimes and punishments related to those crimes and describes all the things which come under Cyber Crime. The objective of this study is to cybercrimes and provisions of existing cyber laws in India and to tackle the cybercrimes using IT Act, 2000.
With developing expenses of power and worry for the natural effect of petroleum products, execution of eco-accommodating fuel sources like sun-based force are rising. The fundamental strategy for saddling sun-oriented force is with exhibits comprised of photovoltaic (PV) boards. Aggregation of residue and garbage on even one board in a cluster decreases their effectiveness in energy age extensively and underscores the need to keep the boards' surface as spotless as could really be expected. In this investigation, a sun-based board cleaning robot (SPCR) has been planned. The planned double engine and crawler robot moves evenly, and the cleaning brush runs on the upward pivot. In addition, the length of the solar panel array can be detected by position switches to keep the SPCR in desired working area.
Semantic Segmentation Using Fully Convolutional Network for An Autonomous Vehicle
Numerous individuals inside the profound learning and PC vision networks comprehend what picture arrangement is. We would like to inform about our model that, what single object or scene is present within the image. Semantic segmentation is the most informative segmentation, where we wish to classify each and every pixel within the image. Deep networks have proved to be encoded with highlevel feature like semantic means which can deliver superior performance in salient object detection. Our key insight is to create “Semantic Segmentation using Fully Convolutional Network for an Autonomous Vehicle” where semantic segmentation is one of the key problems within the field of computer vision. Watching the larger picture, semantic segmentation is among one of the high-level tasks that paves the way towards complete scene understanding. The significance of scene understanding as a center PC vision issue is featured by the established truth that an expanding number of uses feed from deriving information from symbolism. A number of those applications include self-driving vehicles, human-computer interaction, geo-sensing etc. Self-driving vehicles require a profound comprehension of their environmental factors. To support this, fully convolutional networks classifies the road, pedestrians, cars, and sidewalks at pixel-level accuracy. During this project, we develop a neural network and optimize it to perform semantic segmentation using fully convolutional network models.
Recent Advancements in Parallel Computing
Abstract From the development and achievement of the first computer constructed from transistors in 1955, the quest for faster computer systems and computations have delivered approximately diverse technological advancement in the way computer systems are constructed and the strategies concerned in programming those computers. These trends have seen an intensive change in speed, size and power intake of those computer systems. In all, there was a wonderful development in computational speedup, length and electricity ate up through those computer systems. This paper talks about those mechanical advancements/enhancements in computer hardware and construction prompting parallel computers, beginning from the essential successive computer to the parallel computer frameworks accessible today with a point of convergence on the significant thing inconveniences that prompted the move in the methodologies those PCs had been fabricated, the limitations and future turns of events It additionally discusses the programming models adopted for those parallel computers and the way their limitations and gains make contributions to a shift in ideology and higher speedup of computations.
Predicting Employees Performance using Data Mining Techniques
Human resources management (HRM) has become one of the essential interests of managers and 4decision-makers in almost all types of businesses to adopt plans for correctly discovering highly qualified employees. In this research, data mining techniques were utilized to build a classification model for predicting the performance of employees using a real dataset. For this, we are using different methods such as support vector machine, Logistic Regression and Neural Network. This System rates the performance of employees and gives a predictive analysis of data mining techniques. Our main objective is how the training/test split ratio affects the estimation of the generalization performance and to find the best algorithm for predicting employee performance.
Static Load Balancing Scheduling for Multiprocessing System
Multiprocessing is the coordinated processing of programs by more than one computer processor. Multiprocessing simply means putting multiple processors in one system in order to speed up computation. This paper talks about the static load balancing scheduling algorithm for the multiprocessing system. In this different tasks are statically assigned to different processors for efficient utilization of each processor in the system. In this a new term “Gap” has been added which is the basis of the performance of the system. Our Simulation result shows the better and efficient utilization of computer processors in a static manner. Keywords: Multiprocessing system, scheduling algorithm, load balancing.
Parallelizing Apriori Algorithm using Open MP
An Association rule mining used for finding frequent item sets thus generating rules from the frequent item sets. Finding frequent itemsets is more expensive in terms of CPU power and computing resources utilization. Apriori Algorithm is used for high dimensionality on massively large data sets. Parallelism reduces the time required for serial processing. Parallel computing can be applied for mining of association rules. Parallel apriori algorithm focus on parallelizing the process of frequent item set discovery. The computation of frequent item sets mainly consists of creating the candidates and counting them. The parallel frequent itemsets mining algorithms addresses the issue of distributing the candidates among processors such that their counting and creation is effectively parallelized. In this paper we have Implemented Apriori Algorithm in serial and parallel on a quad core processor and compared their performance with respect to time. Keywords: Multi-core, parallelism, Apriori, data mining, open MP
Ant Colony Optimization in Cloud for Load Balancing
There is an increase in the demand of cloud these days, as more and more people are looking for virtual storage of data rather than investing in physical storage. Cloud has many advantages like it focuses on maximizing the effectiveness of the shared resources. Cloud resources are shared by multiple users and these resources are dynamically allocated as per the demand. One of the major issues faced by cloud computing these days are that, demands for resources are continuously increasing as well as are irregularly distributed. To overcome this, it is very essential that there is a proper load distribution. In our paper, we propose Ant Colony Optimization algorithm for implementing load balancing amongst nodes. This is a modified algorithm over the existing ones as in this method, the ants continuously update a single global result set, whereas in the previous ones, each ant had their own result set. Here we use the concept of foraging and trailing pheromones. This helps to create an efficient result set which helps in load balancing. Keywords: Ant colony optimization; load balancing; cloud computing; foraging and trailing pheromones.
Air Computing: A Parallel Computing Module for Offloading Computational Workload on Neighboring Android Devices
Developing complex applications which requires the increasing computational requirements is sophisticated and remains a daunting task. For application developers, countering the real-time computing in mobile devices has always remained a demanding job to ask for more computational power and energy usage than that offered by devices of present generation. In this paper, the author presents an out of the box air computing technology which aids the developers to run real-time applications in parallel with the shared hardware of the neighboring smart phones over a particular range to run complex algorithms while ensuring independencies of limitation set by hand-held devices. Keywords: Parallel computing, android development, air computing
Parallelizing Mutations for Genetic Algorithm
In the present work, the author has implemented the mutations crossovers of genetic algorithm (GA) over a parallelism environment. This attempt will enable us collect data to build a framework for shared parallelism for the solution of equations in the same verifiable computational time. The data indicate towards the better implementation of a larger computational problem in the parallelized for the better solution in the verifiable execution time. Keywords: Genetic algorithm, parallelism, time complexity
Remote Monitoring and Controlling of Digital Signage
This paper is the design implementation and evaluation of a digital signage system based on a PC to display web contents (or other dynamic information) on digital displays located at one or more locations. The system will display information based upon a “playlist” that can be dynamically updated. It avoids the single point of failure of a display system, since each display has an attached processor and local storage containing both the information to be displayed and the local playlist of what is to be displayed. Additionally, the design allows content to be customized to specific local viewers. The granularity of the schedule (i.e., playlist) is much shorter than in existing digital signage systems – leading to a more visually dynamic experience for viewers. On the basis of evaluation, we strongly believe that this approach to digital signage will displace existing signage systems. Keyword: Signage, multimedia systems, Raspbian, software architecture, front-end, back-end
Edge Split Mapping Parallel Algorithm for Tetrahedral Mesh Refinement on Distributed Memory Systems
In this paper we have described a parallel mesh refining algorithm based on edge split mapping parallel algorithm for on a distributed memory system; that aids the user in auto-managing the assignment of processor workload and subdivision of the 3d-mesh. We discussed the design and performance of the algorithm which speeds up the processing of the instructions over a multi-core processor. Keywords: Parallel algorithm, Distributed memory, Mesh Refinement
Coarse Grain to Mixed Grain Parallel Computing
Parallel computing interests are growing again as a result of multicore processors. History of parallel computing dates back to early 1970’s when supercomputers supported high performance computing demand for scientific applications. Today, highly dense Very Large Scale Integration (VLSI) chips have opened up new opportunities and challenges for parallel computing. In this article, we provide a short synopsis of an evolutionary path of parallel computing from both hardware granularity and programming point of view. The article also discusses the challenges of parallel computing to emerging big data analytics area. Keywords: Parallel Computing, Coarse Grain, Fine Grain, Mixed-grain, Multiprocessor, Multicore, Instruction Level Parallelism, SIMD, MIMD, Data Flow, VLSI, Field Programmable Gate Arrays (FPGA), Multi-FPGA, Graphic Processor Units (GPU), Big Data Analytics
Dynamic Data Flow Based Spatial Sorting Method for GPUs: Software Based Autonomous Parallelization
The direct communication between distant parts of a computer system for a mesh or grid based scalable parallel system isn't cost effective. Thus, we have presented and evaluated the performances of spatial sorting method for multi-core GPUs (Graphic Processing Unit) computing. It is a parallel sorting algorithm for autonomous threading of workload which is considerably faster than Thrust Merge sorting, which is known to be the best comparison based sorting algorithm for GPUs. The study shows that the proposed algorithm is more efficient than randomized sample sort, when the memory operations for parallelization of stack units are taken into consideration. Additionally, the proposed algorithm provides an advantage over traditional sorting methods in way that it facilitate dynamic data flow and therefore, its operating time is devoid of the input data dependent fluctuations. Our technique proves its potential extensions of adaptability to more complex parallel algorithms. Keywords: Autonomous parallelization, threading, sorting, GPUs, parallel algorithm, dynamic data flow
Abstract Distributed computing is turning into an inexorably well-known undertaking model in which figuring assets are made accessible on-request to the client varying. The one of a kind incentive of distributed computing makes new chances to adjust IT and business objectives. Distributed computing utilize the web advances for conveyance of IT-Enabled abilities ‘as a support of’ any required clients for example through distributed computing we can get to whatever we need from anyplace to any PC without stressing over anything like about their stockpiling, cost, the executives, etc. In this paper I give an extensive report on the inspiration components of receiving distributed computing, survey the few cloud organization and administration models. It likewise investigates certain advantages of distributed computing over customary IT administration condition including adaptability, adaptability, decreased capital and higher asset use are considered as appropriation purposes behind distributed computing condition. I likewise incorporate security, protection, and web reliance and accessibility as shirking issues. The later remembers vertical versatility as specialized test for cloud condition.
Robot: A Smart Technique to Manage Waste
Abstract Nowadays, the executives of waste from its assortment to unloading and interruption has gotten one of the best testing and exhausting task for metropolitan companies, all around the world. To make this tedious occupation simple, another idea of Smart Dustbin has been thought about for Smart structures, clinics, schools and rail line stations. The Smart trash specialist thought is a progression of conventional garbage man by suspending it to become shrewd teaching sensors and some type of rationales.
A Study Waste Management using Waste Sorting Machine
Abstract New world addresses load of difficulties that incorporates squander the executive framework. Overseeing waste adequately and reusing proficiently, country should need to invest amounts of energy and can push one stride ahead. In this work, machine is created which can figure out the losses in different structures to make the board simpler. It tends to be conceivable to figure out metal, paper, plastics and glass by building up an electromechanical framework utilizing microcontroller and operational enhancer. Arranging metal and glass customary sensors are utilized and for arranging paper and plastics a sensor utilizing LASER and LDR is created.
Abstract Artificial intelligence (AI) is a part of software engineering which includes building brilliant machines equipped for performing assignments that ordinarily require human knowledge. Computer-based intelligence is an interdisciplinary science with numerous methodologies, yet headways in AI and profound learning are making a change in perspective in for all intents and purposes each area of the tech business. Computer based intelligence has different applications, for example, mechanical technology, progressed counts which require precisions, and so forth All the more as of late the headways in AI have opened a pathway which will permit AI based applications with the end goal of training which will be investigated in this examination paper.
3D Object Modeling Using Augmented Reality
The primary objective of this paper is to offer readability of lecturer’s principles to the scholars in an actual like surroundings via 3-D visual aids that may be used to make clear or decorate Information of an idea simply. As college students said, “tell us and we overlook. Display us and we Consider” This paper makes a specialty of regarding the kids in know-how all the ideas Without difficulty and find out each concept Interactive via superimposing the present second item into 3-D surroundings which is referred to as Augmented fact. The common learner could be able to Examine better by way of gazing and listening something than by honestly analyzing something. We can Be using this specific belongings of the human thoughts to accelerate gaining knowledge of. If visible aids are Transformed from 2nd to 3D surroundings, the pupil will have a stay environment to Recognize the concepts. Visible aids tools are to be had to instructors to add reality, clarity, and Range to the drill which is necessary for college students at the earlier levels of language mastering. Keywords: Augmented reality, VR gadgets, surroundings,Technology, 3D Cite this Article: Chetna Egral, Mrunali Shinde, Apurva Pol, Mahi Khemchandani.3D Object Modeling Using Augmented Reality.Recent Trends in Parallel Computing. 2020; 7(2): 14–18p.
Speech to text model is automatic speech recognition model which converts the spoken words into the corresponding text of that word.The objective of this system is to take input from the user in audio format and convert it into the words, which would be designed for the application of ATVM machine. The model for speech to text is created by using the neural networks to train the dataset of various recordings related to train ticket and then it was tested.Therefore, this model provides a better way to book a train ticket in a easy way by means of speech to text. Keywords: speech, text, NLP, technology, KNN Cite this Article: Gauri Dhammpal Suryawanshi, Dhanshree Sanjay Thakare, Jyoti Krishnat Yadav, Vrushali Pankaj Thakur. Speech to Text Using NLP. Recent Trends in Parallel Computing. 2020; 7(2): 4–8p.
Every organization has their own human resources in order to perform the internal and external human resource activities. Among the total human resource activities, managing the human capital i.e. employees is the significant task to any HR. The main function of HR is to recruit, manage and store the employee data which includes their personal information including their job roles, job streams, projects allotted, salaries and many more which allows them to face huge workload. In order to support the HR’s there are some electronic based HR systems called HRMS- human resource management system. Organizations should maintain HRMS software with huge number of client server applications, service providers as well as control tools. But this application is cost effective one that allows them to manage their employee’s data in a simple manner. This human resource management system will allow the HR’s to manage the employee payrolls, the timings of employee works, employee information systems, employees recruitment, employees training, employee retention and performance appraisal, employee service and employee attendance. This project belongs to a category of web application that can be accessed through PC or mobile phone with internet connection. Keywords: HRMS, HR, HRMS, MVC Framework, technology Cite this Article: Rasik Umanath Thalkar, Shubham Rajesh Jagushte, Vaishali Jadhav.Human Resource Management:Techskill. Recent Trends in Parallel Computing. 2020; 7(2): 19–23p.
Automated Detection of Polycystic Ovarian Syndrome using Neural Network Algorithm
The ovarian ultrasound imaging could be a good tool in infertility treatment. Monitoring the follicles is extremely important in human reproduction. Periodic measurements of the scale and shape of follicles over several days are the primary means of evaluation by doctors. Today monitoring the follicles is finished by non-automatic means with human interaction. This work could also be very demanding and inaccurate and, in most of the cases, means only an additional burden for doctors. The detection of PCOS is often done using blood tests and ultrasound images of the ovaries that detect multiple follicles. This provides the accurate results of if women are suffering PCOS or not. The proposed methodology goes to be accustomed detect multiple follicles that indicate the sign of PCOS. Using image-preprocessing techniques we'll be enhancing the quality of the image to make it ready for the classification phase. Neural network algorithms that are best for such processed have gotten accustomed classify the syndrome. The experimental results demonstrate the efficiency of the tactic. Keywords: Multiple Follicles, ultrasound images, neural network algorithm, technology, PCOS (Polycystic Ovarian Syndrome). Cite this Article: Deeksha Manish Singh, Kardam Hitendra Acharya, Omkar Eknath Mestry, Arti Gore. Automated Detection of Polycystic Ovarian Syndrome using Neural Network Algorithm. Recent Trends in Parallel Computing. 2020; 7(2): 9–13p.
One common way to obtain information in the modern society is to read books. As books are not portable enough to carry and read, there has been demand for electronic books. Devices for digitization of books, called book scanners, have been invented. However, most book scanners target industrial consumers such as companies, libraries, and schools, leaving behind personal consumers. Book-Scanning is the process of converting physical books into digital media such as images, electronic text, or electronic books (e-books). These can be easily distributed, reproduced, and read-on screen. The digitization of book requires a scanner. After the page turned the camera will capture the image. User interaction for scanning and page turning is not required. Keywords: page pick-up head, page turner arm, V-shape cradle, perforated means porous or it has series of small holes on it, sturdy means strongly and solidly built, actuated means start to operate. Cite this Article: Neha Chandrakant Gawde, Dhanashree Ashok Salvi, Kiran Bodake, Pragati Pejlekar. Scanner for Books. Recent Trends in Parallel Computing. 2020; 7(2): 1–3p.
Secure Providing Key-Deduplication by Identity-Based Broadcast Encryption (IBBE)
Duplication, that can save costs for storage by allowing us to keep just one copy unrecognized information, is particularly noteworthy because of the dramatic rises in the cloud content. These are usually stored until re-appropriated to ensure information authentication. Conventional encryption undoubtedly leads to various distinctive ciphertexts generated by separate mystery keys from the corresponding plaintext, which block the deductibility of information. Concurrent coding allows deductions, because the corresponding grievances are usually scratched into ciphertexts. One common problem is how many keys can be managed efficiently and effectively. A couple of deduplication proposals to handle the central administration problem is proposed. Either the key management systems are to be addressed or the proprietors of information need to comply. We plan a new Key-Deduplication convention on the customer side of this article. Keywords: deduplication, convergent encryption, HDF, IBBE, keygen Cite this Article: A. Chandrasekar, Anusha Yamjala, Sai Teja Bommineni, Nithin Rao Doddikindi. Secure Providing Key-Deduplication by Identity-Based Broadcast Encryption (IBBE). Recent Trends in Parallel Computing. 2020; 7(1): 18–23p.
Diabetic Level Prediction in Humans: Using Supervised Machine Learning Methods
Diabetes could be an unremitting malady or a metabolic sick wellbeing cluster where an individual endures from a pervasive sum of insulin-producing blood glucose within the body or since the cells of the body do not react to affront. Steady diabetes hyperglycemia is the damage to long term organs, particularly lungs, kidneys, nerves, the heart and the veins are connected to weakness and misfortune. The objective of this investigation is to utilize vital highlights, to make an expectation calculation based on machine learning and to discover the ideal classifier for the closest comes about compared to clinical comes about. The recommended approach is planning to utilize prescient examination to distinguish characteristics of diabetes mellitus that are afflicted in early location. The comes about appear the choice tree calculation and the most noteworthy specificities of the Irregular Woodland are 98.20% and 98.00% separately, for the consider of diabetic information. The most noteworthy precision of Naïve Bayesian tests is 82.30 focuses. The collection of appropriate highlights from information sets is assist extended within the inquire about to extend classification accuracy. Keywords: Diabetes, T2DM, supervised learning methods, WHO, diabetes mellitus Cite this Article: M Monika, Yeshwanth Moudgalaya, Akhil Reddy Akkati. Diabetic Level Prediction in Humans: Using Supervised Machine Learning Methods. Recent Trends in Parallel Computing. 2020; 7 (1): 1–6p.