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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 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 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 hybrid machine learning approach for intrusion detection system
The Intrusion Detection System (IDS) enforces information security and is responsible to identify attacks and vulnerabilities inside a network. It does this by analyzing the packet stream throughout the network. In traditional IDS systems, the analysis is done by looking for signatures of known attacks or deviations of normal activity as described by the rules provided for the IDS system. Machine learning helps in deriving predictive knowledge and this makes it ideal to apply Machine learning in an IDS system to detect attacks. This paper focuses on creating a hybrid model that is best to implement in an IDS system. A hybrid model is implemented which combine multiple machine learning algorithms using Ensemble method. The experiments include evaluating machine learning algorithms such as Decision Tree, MLP (Multi-Layer Perceptron), Gradient Boosting etc. The algorithms with the best results are taken to construct Hybrid model. This Hybrid approach will improve the accuracy and efficiency for identifying the attacks by the IDS system. Depending on the type of attack, the IDS system can classify packets as DoS (Denial of Service), Probe, R2L (Root to Local), U2R (User to Root) or Normal. The experiments are carried using NSL-KDD Dataset. 2019, Institute of Advanced Scientific Research, Inc.. All rights reserved. -
Implementation of FOAF, AIISO and DOAP ontologies for creating an academic community network using semantic frameworks
Web 2.0 delivers the information which is then displayed in human readable content, omitting the crucial information which can be drawn from the data by the applications. Web 3.0 or semantic web is an extension to the current web, with an ambition to determine the drawbacks of the current web. The semantic web has already proven its influence in several communities around the globe, such as social media, music industry, healthcare domain, online blogs or articles, etc.; Among the several tools and technologies, ontologies or vocabularies are the foundation pillar for the semantic web. In this paper, the developed system aims at improving the collaboration and academic relations among staff which is directly related to our education community by providing a better networking platform which lets the agents discuss their achievements, titles, domain interests, and various other activities. Results have been analyzed to show how new facts, information can be implied from the presented knowledge of several agents and help generate a relationship graph by utilizing various semantic tools. The system discussed in this paper processes all the information in a format which can be understood by both humans and the machines, to interpret the underlying meaning about it and provide effective results. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Implementation of digital signature using hybrid cryptosystem
Security is a major concern when it comes to electronic data transfer. Digital signature uses hash function and asymmetric algorithms to uniquely identify the sender of the data and it also ensures integrity of the data transferred. Hybrid encryption uses both symmetric and asymmetric cryptography to enhance the security of the data. Digital Signature is used to identify the owner of the document but it does not hide the information while transferring the document. Anyone can read the message. To avoid this, data sent along with the signature should be secured. In this paper, Digital signature is combined with hybrid encryption to enhance the security level. Security of the data or the document sent is achieved by using hybrid encryption technique along with digital signature. 2018 Authors. -
Implementation of biological fuel cells in treating pharmaceutical effluents
Mankind suffers from a wide variety of infections, diseases, and lifestyle problems. To overcome, several industries worldwide aim to achieve their main objective as the synthesis of an enormous number of diverse drugs that neutralize problems. With the production of tones-to-tones pharmaceutical products these industries also generate extreme good amount of waste, pharmaceutical waste which is now concerned as it contains massive quantity of high organic load of toxic and non-toxic elements. However, the industrial sector adopts anaerobic wastewater treatment strategies to overcome this. As pharmaceutical waste owes highly varied and complexed recalcitrant elements in their complex drug molecules it is not ideal to treat only with anaerobic treatment. Hence, several biotreatments are becoming popularized because they employ MFCs, which are known for the generation of electricity directly from biodegradable organic compounds. The new bio electrochemical technology promises to be inexpensive in comparison to conventional ones. MFC holds the process of both oxidation and reduction permitting the degradation of a wide range of compounds to easily degradable and generates concurrent renewable energy. 2022 by Nova Science Publishers, Inc. -
Implementation of AI in manufacturing industries a case study
Artificial intelligence (AI) is getting progressively integrated into nearly every facet of our existence. Its applications are ubiquitous and ever-evolving, spanning fields such as autonomous vehicles, geology, medicine, and art. AI has, however, posed as many questions as it has answered. These include the definition and application of the technology (viz., assisted, augmented, or independent intellect), the question of whether computers are thinking machines similarly to humans, the wider implications of the impact of automation on society, and the unexpected moral and principled quandaries. This chapter provides an overview of artificial intelligence in manufacturing intended for executives in manufacturing and industrial companies who want to integrate AI into their business. Its main objective is to apply AI to the engineering, testing, and production stages of the manufacturing value chain. The goal is to discuss business applications that technology, data, and automated processes can support, and how the appropriate personnel, organizational structure, and culture can support them. This article discusses current advancements, poses problems, asks questions, and attempts to bring cutting-edge concepts and research closer to business. 2025 Mohamed Arezki Mellal. All rights reserved. -
Implementation of a 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. -
Implementation of a Heart Disease Risk Prediction Model Using Machine Learning
Cardiovascular disease prediction aids practitioners in making more accurate health decisions for their patients. Early detection can aid people in making lifestyle changes and, if necessary, ensuring effective medical care. Machine learning (ML) is a plausible option for reducing and understanding heart symptoms of disease. The chi-square statistical test is performed to select specific attributes from the Cleveland heart disease (HD) dataset. Support vector machine (SVM), Gaussian Naive Bayes, logistic regression, LightGBM, XGBoost, and random forest algorithm have been employed for developing heart disease risk prediction model and obtained the accuracy as 80.32%, 78.68%, 80.32%, 77.04%, 73.77%, and 88.5%, respectively. The data visualization has been generated to illustrate the relationship between the features. According to the findings of the experiments, the random forest algorithm achieves 88.5% accuracy during validation for 303 data instances with 13 selected features of the Cleveland HD dataset. 2022 K. Karthick et al. -
Implementation challenges of Total Quality Management (TQM) in dairy sector /
Smart Journal of Business Management Studies, Vol.15, Issue 1, pp.1-9, ISSN No: 2321-2012. -
Implementation and Investigation of an Optimal Full Adder Design for Low Power and Reduced Delay Conditions
Full adder is one of the important components in electronics, used for various fundamental processing algorithms such as addition and multiplication. The application of these full adders is included in but not limited to Very Large-Scale Integration (VLSI) and Digital Signal Processing (DSP). To provide scalability and reliability to the advanced algorithms for high-end applications, the designing system of full adder should be enhanced. So, in this paper, we intended to improve the efficiency of a full adder circuit to work under low power and delay conditions. The software we used in this project is MENTOR GRAPHICS using 180nm technology. The efficiency of the proposed transistor design is evaluated by analysing the power consumption, delay, PDP, capacitor load, delay w.r.t capacitance and PDP w.r.t capacitance. The parameters are compared between our proposed design and the literature schemes such as OLPFAD, DFEFA, DTLPCFA, and DPEHFA, respectively. It is evident that our proposed design outperforms the other. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Imperative of revisiting the operational risk control architecture in public sector banks cyjdf (PSBs) in India: A qualitative analysis
The banking sector in India has contributed to economic growth, parity and equity while equally keeping focus on profit and social objectives. The successive prudential and regulatory reforms introduced in the banking sector have made it more robust and stronger to withstand the bubbles and external shocks. Still, the Indian banking sector in general and public-sector banks (PSBs) in particular have been suffering from the bank frauds. This study endeavors to cover the increasing incidences of banking frauds in PSBs and probes the weaknesses and chinks in the operational risk architecture at the PSBs in India. This study selects Punjab National Bank as a true representative of PSBs and treats it as a critical case study to apply the learning and findings to the PSBs in India. This qualitative analysis of the study revealed that the chinks in the operational risk control mechanism and lax corporate governance are the main reasons behind the increasing incidences of frauds at PSBs. The findings of the study showed that a strong corporate governance and compliance framework, robust risk management architecture, investment in people, technology and systems will go a long way in achieving tighter control and supervision, streamlining processes and, most of all, adhering to a culture of checks and balances. 2019 LLC CPC Business Perspectives. All Rights Reserved. -
Impediments of product recovery in circular supply chains: Implications for sustainable development
Product recovery has fascinated the concentration of organizations and is prominent among industry practitioners and researchers due to improved environmental concerns, social awareness, and economic benefits. Circular supply chain (CSC) compounds the concept of product recovery in global supply chain management to present a sustainable perspective. Therefore, this study aims to determine impediments of product recovery and CSC toward sustainable production and consumption in the background of manufacturing organizations. This study determines potential impediments from literature and in consultation with experts. Further, a fuzzy VIKOR approach is practiced to prioritize the impediments of product recovery and CSC. Then, a sensitivity analysis is conducted to verify the robustness of the framework attained. The results from the study reflect that lack of collaboration from supply chain performers, lack of tax policies for facilitating CSC models and limited expertise, technology, information on CSC practices are the critical impediments to product recovery in CSCs. The findings of the study could assist industry managers and practitioners in developing procedures and strategies to attain sustainable development. 2022 The Authors. Sustainable Development published by ERP Environment and John Wiley & Sons Ltd. -
Impedance, Electrical and Dielectric behaviour of Tin Oxide Nanoparticle doped with Graphite, Graphene Oxide and Reduced Graphene Oxide
Nanostructured materials have attained incredible interest in recent days due to their distinctive chemical, physical, mechanical, magnetic and optoelectronic properties. In the present study, metal nano particle (SnO2) was doped with graphite, graphene oxide (GO) and reduced graphene oxide (rGO) with various composition (1:100), (1:1) and (100:1) by weight ratio. The citrate-nitrate gel combustion method was used to prepare nanocrystalline SnO2 while GO and rGO were synthesized through modified Hummer's method. The preparation of SnO2-rGO composites was done using a one-step hydrothermal process. The electrical and structural behaviour of the composites of graphite, GO and rGO mixed with SnO2 were elucidated by the impedance analyzer in the frequency range from 10Hz to 1MHz. It is observed that the composite of SnO2 with graphite and reduced graphene oxide have similar broad characteristics while SnO2 mixed with GO is exhibiting different properties which could be attributed to the presence of oxygen functionaries. 2021 The Authors. Published by ESG. All Rights Reserved. -
Impedance and electrochemical studies of rGO/Li-ion/PANI intercalated polymer electrolyte films for energy storage application
The present manuscript describes the synthesis of reduced graphene oxide (rGO) from coke by using modified Hummers method. The synthesized emeraldine poly aniline (PANI) polymer was used as a polymer host matrix. A series of polymer electrolyte films were prepared by varying concentration of rGO, PANI and Lithium carbonate. The synthesized PANI and rGO were soluble in common polar solvent. The structural, Nyquist and cyclic voltammetry studies of polymer electrolyte were investigated. The XRD and FTIR investigation confirms the formation of rGO and PANI in view of structural and chemical compositions respectively. The electrical property of polymer electrolyte was obtained by Nyquist plot which represents the perfect semicircular pattern. It confirms the charge transport mechanism with the decreased concentration of rGO in polymer electrolyte. The cyclic voltammetry performed at different scan rate on potential window ranged between-0.5 to 0.6 V represents the oxidation and reduction peaks. The overall results describe that the present electrolyte material can be a potential candidate for energy storage application.. 2019 Elsevier Ltd. -
Impacts of Pore Scale Gas Diffusion Layer Deformation on PEMFC Performance at Sub Zero Operation
Highlights Impact of assembly pressure on species and charge transport during cold start operation. Inhomogeneous GDL compression and intrusion is considered in the study. The intrusion effect leads to intense ice accumulation under the channels at 2 MPa. The importance of applying appropriate clamping pressure is highlighted in the study. 2023 The Electrochemical Society (ECS). Published on behalf of ECS by IOP Publishing Limited -
Impacts of imprisonment of women on the rights of their children: An Indian perspective /
International Journal of Advanced Research, Vol.3, Issue 10, pp.1297-1303, ISSN No: 2320-5407. -
Impacts of Cloud Computing in Digital Marketing
In modern day of digital marketing the cloud computing is proving extremely beneficial links for businesses. Moreover, it's characteristic to access the stored data from anywhere makes it more popular among the entrepreneurs. The present paper is an exploration of the cloud computing in respect of digital marketing. The paper defines and correlates the term cloud computing, digital marketing, as well as also elaborates about benefits that can be harvested by the integration of cloud computing in digital marketing strategy. 2021 IEEE.