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Implementation of integer factorization algorithm with pisano period
The problem of factorization of large integers into the prime factors has always been of mathematical interest for centuries. In this paper, starting with a historical overview of integer factorization algorithms, the study is extended to some recent developments in the prime factorization with Pisano period. To reduce the computational complexity of Fibonacci number modulo operation, the fast Fibonacci modulo algorithm has been used. To find the Pisano periods of large integers, a stochastic algorithm is adopted. The Pisano period factorization method has been proved slightly better than the recently developed algorithms such as quadratic sieve method and the elliptic curve method. This paper ideates new insights in the area of integer factorization problems. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Implementation of Morphological Gradient Algorithm for Edge Detection
This paper shows the implementation of a morphological gradient in MATLAB and colab platforms to analyze the time consumed on different sizes of grayscale images and structuring elements. A morphological gradient is an edge detecting technique that can be derived from the difference of two morphological operations called dilation and erosion. In order to apply the morphological operations to an image, padding is carried out which involves inserting 0 for dilation operation and 225 for erosion. Padding for the number of rows or columns is based on the size of the structuring element. Further, dilation and erosion are implemented on the image to obtain morphological gradient. Since central processing unit (CPU) implementation follows sequential computing, with the increase in the image size, the time consumption also increases significantly. To analyze the time consumption and to verify the performance across various platforms, the morphological gradient algorithm is implemented in MATLAB and colab. The results demonstrate that colab implementation is ten times faster when constant structuring element with varying image size is used and five times faster when constant image size with varying structuring element size is used than the MATLAB implementation. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Implementation of Movie Recommendation System Using Hybrid Filtering Methods and Sentiment Analysis of Movie Reviews
In present era of digitization of entertainment, immense volume of movies are produced, which results in the necessity of sophisticated recommendation systems. In the streaming platform these systems empower users to discover new and relevant movies, benefiting both viewers and the entertainment industry. This research paper offers a comprehensive method for incorporating movie review sentiment analysis into a hybrid recommendation system. The study focuses on 4890 movies using a broad dataset containing the detailed descriptions of the movies along with the reviews. To employ the demographic filtering, the popularity score of the movies were calculated, then to apply the collaborative filtering, the textual movie descriptions were vectorized using the countvectorizer method. To predict the sentiment of the movie reviews, the high accuracy model "ControX/Sen1"was used. This hybrid recommendation system ranked the movies based on the user's preferences by employing cosine similarity, the sorted list was further filtered with the positive sentiment reviews. By including sentiment analysis, this research advances sophisticated movie recommendation systems by providing a comprehensive method for addressing user preferences and emotional resonance in film selections. 2024 IEEE. -
Implementation of multicloud strategies for healthcare organisations to avoid cloud sprawl
Healthcare organisations are being overwhelmed by data, devices and apps, and disjointed multiple cloud services. Well-heeled multicloud can provide a unified cloud model that provides greater control and scalability at reduced costs. Healthcare multicloud is turning into an appealing path for associations to manage the blast of advanced healthcare information in digital health, internet of things, associated gadgets and healthcare applications. As more human services associations grasp distributed computing, they are progressively going to a blend of open, private, and hybrid cloud administrations and foundation. In fact most of the healthcare service organisations plan to utilise different cloud vendors over the upcoming years. While having multiple cloud vendors can give organisations more flexibility and redundancy, managing multiple clouds can be a challenge. It can lead to cloud sprawl, unauthorised cloud use known as shadow IT, disjointed cloud solutions, inefficiencies, and waste. Copyright 2022 Inderscience Enterprises Ltd. -
Implementation of OpenId connect and O Auth 2.0 to create SSO for educational institutes
Increase in the number of users is directly proportional to the need of verifying them. This means that any user using any website or application has to be authenticated first; this leads to the creation of multiple credentials of one user. Now if these different websites or applications are connected or belong to one single organization like a college or school, a lot of redundancy of data is there. Alo ng with this, each user has to remember a wide range of credentials for different applications/websites. So in this paper, we addre ss the issue of redundancy and user related problems by introducing SSO using OpenId Connect in educational institutes. We aim to mark the di fference between the traditional system and proposed login by testing it on a group of users. 2018 Authors. -
Implementation of Recent Advancements in Cyber Security Practices and Laws in India
In the past few decades, a large number of scholars and experts have found that wireless connectivity technologies and systems are susceptible to many kinds of cyber attacks. Both governmental organizations and private firms are harmed by these attacks. Cybersecurity law is a complex and fascinating area of law in the age of information technology. This essay aims to outline numerous cyber hazards as well as ways to safeguard against them. In both local and international economic contexts, it is critical to establish robust regulatory and legal structures that address the growing concerns about fraud on the internet, security of information, and intellectual property protection. Additionally, it covers cybercrime's different manifestations and security in a global perspective. Due to recent technical breakthroughs and a growth in access to the internet, cyber security is now utilized to safeguard not just a person's workstation but also their own mobile devices, including tablets and mobile phones, that have grown into crucial tools for data transmission. The community of security researchers, which includes members from government, academia, and industry, must collaborate in order to comprehend the new risks facing the computer industry. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Implementation of speech recognizer and synthesizer for the physically challenged
Speech Recognition and Speech Synthesis are two complementary technologies that are used in systems to which the human voice serves as input or output. People with physical, motor disabilities prefer systems that can be driven by their voice than using the strenuous, usual and standard input-output devices such as keyboard, mouse and monitor. Solutions under the umbrella of Assistive Technology are designed to support people with disabilities to overcome the difficulties in handling their diurnal chores. Present-day commercial speech processing systems have received wider customer acceptance, yet not suitable for people with speech disabilities. It is observed that present-day speech recognizers fail to recognize voices with distortions, misrepresentations and deformations. The unintelligibility of the input voice limits the use of off-the-shelf speech processing products by the speech-impaired user community. In such scenarios, the speech processing systems require alterations to become suitable for the specialised user group. Techniques of adaptation are popular in the field of speaker recognition, which can be applied in the domain of Augmentative and Alternative Communication (AAC). The main aim of this research is to model a speaker adaptive system for the speech-disabled users with articulation disorders and neurologically-based disorders due to illnesses like cerebral palsy. The problem context for this research work is two-fold: accepting the incomprehensible speech input and transforming the same into a more understandable speech. The first portion is to adapt a speech recognizer and verify the recognition accuracy; the second portion is to substitute the recognized words with a better- comprehensible voice. Due to the medical requirements of the research subjects, collecting and using live speech data of individuals is an onerous task with complex infrastructure. Also, the collection and storage of patients data are restricted by ethical procedures. Hence, the data created by various Universities, following the standard procedures in a noise-free environment are used for this research work. Experiments are conducted on the voice data sets in order to improve the recognition accuracy for speakers uttering individual words. The Speech Recognizer is implemented using Hidden Markov Models and Speech Synthesizer is implemented using a pattern-searching algorithm on a database with text input and voice output (concatenative synthesis). The adaptation techniques, viz., Maximum Likelihood Linear Regression (MLLR) and Maximum A Posteriori (MAP) are applied in a pipeline with adjusted language model and pronunciation dictionary. This has reduced the Word Error Rates (WER) of recognizing the incoherent speech. In the process of adaptation, the parameters of the acoustic model of a generic speech recognizer are altered using the feature vectors generated from the training data set applying maximum likelihood linear regression. Parameters of this updated model are then used as informative priors to MAP adaptation. Speech Synthesizer, i.e., the Text-to-Speech system then translates the recognized text into a more-intelligible voice which is clearer to the listeners. The simulation with test data sets measured the effectiveness of the combined algorithm proposed here; it produced improvements in recognition accuracy from 43% (for a speaker with 93% speech intelligibility) to 90% (for a speaker with 2% speech intelligibility). An analysis of the improvement in recognition accuracy and speed of recognition for each speaker reveals that the proposed methodology is more effective for severely dysarthric speakers than those with less speech impairments, making the proposed model socially significant. -
Implementation of Supervised Pre-Training Methods for Univariate Time Series Forecasting
There has been a recent deep learning revolution in Computer Vision and Natural Language Processing. One of the biggest reasons for this has been the availability of large-scale datasets to pre-train on. One can argue that the Time Series domain has been left out of the aforementioned revolution. The lack of large scale pretrained models could be one of the reasons for this.While there have been prior experiments using pre-trained models for time series forecasting, the scale of the dataset has been relatively small. One of the few time series problems with large scale data available for pre-training is the financial domain. Therefore, this paper takes advantage of this and pretrains a ID CNN using a dataset of 728 US Stock Daily Closing Price Data in total, 2,533,901 rows. Then, we fine-tune and evaluate a dataset of the NIFTY 200 stocks' Closing Prices, in total 166,379 rows. Our results show a 32% improvement in RMSE and a 36% improvement in convergence speed when compared to a baseline non pre trained model. 2023 IEEE. -
Implementation of survivability aware protocols in WSN for IoT applications using Contiki-OS and hardware testbed evaluation
The Internet of Things is a network of devices capable of operating and communicating individually and working for a specific goal collectively. Technologically, many networking and computing mechanisms have to work together with a common objective for the IoT applications to function, and many sensing and actuating devices have to get connected to the Internet backbone. The networks of resource-constrained sensor devices constitute an integral part of IoT application networks. Network survivability is a critical aspect to consider in the case of a network of low-power, resource-constrained devices. Algorithms at different layers of the protocol stack have to work collectively to enhance the survivability of the application network. In this article, the survivability-aware protocols for wireless sensor networks for IoT applications are implemented in real network scenarios. The routing strategy, Survivable Path Routing protocol, and the channel allocation technique, Survivability Aware Channel Allocation, are implemented in Contiki-OS, the open-source operating system for IoT. Furthermore, the implementation scenarios are tested with the FIT IoT Lab hardware testbed. Simulated results are compared with the results obtained from the testbed evaluation. 2023 Elsevier B.V. -
Implementation of Time-Series Analysis: Prediction of Stock Prices using Machine Learning and Deep learning models: A Hybrid Approach
Experts in the finance system have long found it difficult to estimate stock values. Despite the Efficient - market hypothesis Principle claim that it is difficult to anticipate share prices with any degree of precision, research has demonstrated that share price movements could be anticipated with the proper levels of precision provided the correct parameters are chosen and the proper predictive models are created. individuals who are adaptable. The share market is unpredictable in essence, making its forecasting a difficult undertaking. Stock prices are affected by more than economic reasons. In this project, Arima, LSTM and Prophet models are used to predict the future way of behaving share price, the datasets has been obtained from NSE, share price prediction algorithms have been created and tested. According to the empirical findings, the LSTM model would be used to anticipate share prices rather well over a substantial amount of time with exactness. 2022 IEEE. -
Implementation of tokenization in natural language processing using NLTK module of python
With the advancement of technologies, now it is possible to analyze the large amount of unstructured text circulated online with various tools and methods for understanding the changes as well to infer meaningful insights from the text data. In this work, the aim is to understand how Python can be used for text analytics by the help of various libraries available in it. The natural language processing (NLP) is being used to analyze and synthesize natural language and speech in Python. 2023 Scrivener Publishing LLC. -
Implementation of vendor-managed inventory in hospitals: an empirical investigation
This research aims to determine critical success components for implementing the vendor-managed inventory (VMI) and test their influence on the inventory in Indian hospitals. The independent and dependent components of the research are identified from the extensive literature review. The independent variables are top management commitment, supply chain strategy, business process integration, continuous improvement, resource sharing, and information technologies adoption. The dependent variable identified is the adoption of vendor-managed inventory. The study results suggest that the proposed latent variables significantly impact the VMI and significantly contribute to VMIs implementation and sustainability. The study highlights the importance of VMI in Indian hospitals, and therefore, it will help the management focus on the VMI for enhanced operational efficiency. Previous studies have not empirically tested the impact of the suggested practices for VMI in Indian hospitals. The analysis would help evaluate VMI adoption in Indian hospitals. Copyright 2024 Inderscience Enterprises Ltd. -
Implementation Strategies for Green Computing
In this chapter, we look at how renewable energy sources can be integrated into the planning, design, and construction of long-term sustainability in green buildings. When it comes to establishing a framework for environmentally friendly building, there are two primary schools of thought. One is related to the use of conventional architecture and low-energy construction material. The fundamental focus of green building design is on using renewable energy solutions for the purpose of managing energy protection. When referring to a green building, either sustainable construction or green construction may be used instead. To guarantee a structure will last for its intended purpose and the environment will not be harmed in the process, sustainable construction practices should be included from the start. Additionally, the economics of renewable energy are presented in this chapter with eco-friendly construction practices that make use of renewable energy sources. 2024 selection and editorial matter, Vandana Sharma, Balamurugan Balusamy, Munish Sabharwal, and Mariya Ouaissa. -
Implementing a programmable drop voltage controller vlsi
This study offers a new synchronized practice area door array (FPGAs), to minimize electricity usage. Concurrent bit-serial architecture is shown in the figure to minimize energy consumption and timing synchronization of switching structures. Researchers offer a fine-grained energy control system with each Look-up database to minimize the Static energy by the channel length, which is now equivalent to the dynamical one (LUT). A 90 nm Processor is the planned field-programmable VLSI. Its electricity consumption is 42 percent lower than that of sequential design. 2021, SciTechnol, All Rights Reserved. -
Implementing artificial intelligence agent within connect 4 using unity3d and machine learning concepts
Nowadays, we come across games that have unbelievably realistic graphics that it usually becomes hard to distinguish between reality and the virtual world when we are exposed to a virtual reality gaming console. Implementing the concepts of Artificial Intelligence (AI) and Machine-Learning (ML) makes the game self-sustainable and way too intelligent on its own, by making use of self-learning methodologies which can give the user a better gaming experience. The use of AI and ML in games can give a better dimension to the gaming experience in general as the virtual world can behave unpredictably, thus improving the overall stigma of the game. In this paper, we have implemented Connect-4, a multiplayer game, using ML concepts in Unity3D. The machine learning toolkit ML-Agents, which depends on Reinforcement Learning (RL) technique, is provided using Unity3D. This toolkit is used for training the game agent which can distinguish its good moves and mistakes while training, so that the agent will not go for same mistakes over and over during actual game with human player. With this paper, authors have increased intelligence of game agent of Connect 4 using Reinforcement Learning, Unity3D and ML-Agents toolkit. BEIESP. -
Implementing Ensemble Machine Learning Techniques for Fraud Detection in Blockchain Ecosystem
A new era of digital innovation, notably in the area of financial transactions, has been conducted in by the rise pertaining to block-chain technology. Although the decentralized nature of blockchain technology renders it prone to fraud, it has been praised for its capacity to offer a safe and transparent platform for financial transactions. The integrity of the entire blockchain network may be compromised by fraudulent transactions, which may also damage user and stakeholder trust. This study aims to assess machine learning's efficacy in detecting fraudulent transactions within blockchain networks and identifying the most effective model. To achieve its objectives, this study used a combination of data collection, data preprocessing, and machine learning techniques. The data used in this study was dataset of blockchain transactions and pre-processed using techniques such as feature engineering and normalization. Then trained and evaluated using several machine learning models, including Logistic Regression (LR), Naive Bayes (NB), SVM, XGboost, LightGBM, Random Forest(RF), and Stacking, in order to determine their effectiveness in detecting fraudulent transactions. XGBoost demonstrated the highest accuracy of 0.944 in the stacking model, establishing it as the top-performing model, closely followed by Light GBM. The study's discoveries offer significant practical implications for advancing fraud detection methods in blockchain networks. By pinpointing the most efficient machine learning model and crucial predictive fraud features, this research provides vital insights for refining precise detection algorithms, enhancing blockchain network security, and broadening their reliability across various applications. 2023 IEEE. -
Implementing Innovative Weed Detection Techniques for Environmental Sustainability
Agriculture, supporting over half of India's population, grapples with the challenge of weed control. Current methods applied in plantation crops lack efficiency and pose environmental and health risks. This paper advocates a paradigm shift, emphasizing the critical need for effective weed detection using cluttered unmanned aerial vehicle (UAV) images. The research methodology integrates image processing, Mask R-Convolutional Neural Networks (R-CNN), and Internet of Things (IoT). A dataset of 200 UAV images was subjected to a thorough preprocessing. In the initial phase, weeds and crops were identified with precision employing an UAV-tailored Mask R-CNN with instance segmentation. This was found to surpass traditional methods in terms of communication between the model and the agricultural environment. For timely decision-making, real-time data were collected using IoT. Average Precision (AP) values reveal high accuracy, notably 89.1% for weeds, 88.9% for crops, and an overall precision of 89.4%. The Mask R-CNN network segments and classifies images, marking weed zones communicated to farmers via Raspberry Pi with a GSM module, enabling real-time alerts and informed decision-making for efficient weed control. This holistic approach, providing object classifications, detailed bounding boxes, and masks, addresses weed control challenges, highlighting the transformative potential of advanced technologies in agriculture. 2024, Institute for Environmental Nanotechnology. All rights reserved. -
Implementing privacy and data confidentiality within the framework of the Internet of Things
Throughout the current and future worldwide Web network infrastructure, the notion of the Internet of Things (IoT) foresees the pervasive interconnection and cooperation of intelligent things. As such, the IoT is simply the next logical step in the expansion of the Web into the real world, ushering in a plethora of unique services that will enhance peoples lives, give rise to entirely new economic sectors and smarten up the physical infrastructure upon which we rely, including buildings, cities and transportation networks. As smart devices permit widespread information collection or tracking, the IoT will not be able to reach its full potential if the vision for the IoT is not implemented appropriately. These helpful characteristics are countered by concerns over confidentiality, which have, to date, hindered the viability of IoT aspirations. In the face of widespread surveillance, the management of private information and the development of tools to limit or evade pervasive monitoring and analysis are two examples of the new difficulties brought about by such dangers. This paper considers the privacy concerns raised by the Internet of Things in depth. Henry Stewart Publications 2398-1679 (2023). -
Implementing Quality Healthcare Strategies for Improving Service Delivery at Private Hospitals in India
Healthcare is becoming the largest growing sector of India because of its huge coverage, providing services and investment by public and private players. In India growth of private hospitals have totally changed the scenario of health care delivery. This study explores the effectiveness of the strategies to provide quality health care and thereby improving the service delivery in Private Hospitals. In total 122 responses were collected after administering the questionnaires. The findings of this study reveals that quality health care strategies has positive impact on service delivery. Quality health care strategies showed a different kind of associations with three measures of quality namely structure, process and outcome measures. The implications from the study provides the need of multifaceted approach for implementing quality improvement strategies and adoption of the model for the same. This study recommends a blend of quality improvement programs with increased ICT (Information and Communication Technology) applications for enhancing the turnaround time. Further study can be conducted on other healthcare quality dimensions and strategic interventions that can enhance the quality of health care and clinical outcomes in Private Hospitals in India. 2017 Indian Institute of Health Management Research. -
Implementing quality healthcare strategies for improving service delivery at private hospitals in India /
Journal of Health Management, ISSN No. 0972-0634.