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Improvement to Recommendation system using Hybrid techniques
Currently, recommendation systems are a common tool for providing individualized recommendations and item information to users. For personalization in the recommendation system, there are a variety of strategies that can be used. To improve system performance and offset the shortcomings of individual recommendation strategies, a hybrid recommender system integrates two or even more recommendation techniques. The demand to summarize all of the knowledge on actual methods and algorithms utilized in hybrid recommended systems necessitates the need for a systematic review in the domain. These materials will be employed to aid in the development of an auto-switching hybrid recommender system. In the content-based filtering technique, the algorithm is based on the contents of items and the collaborative filtering technique algorithm combines the relationship between user and item. Both of the approaches of recommendation system are suffers from some limitations, this is a big issue to predict better recommendations to the user. Hybrid systems are introduced to overcome the main limitations of both techniques. These systems are made with a combination of content-based and collaborative filtering techniques and have advantages of both techniques. With the use of hybrid systems, the quality of recommendations is improved. Hybrid recommendation systems use previous data of a user to find his/her interest and then they target the set of an adjacent user which is similar with that user and according to adjacent user recommend things to the user. Hybrid systems offer the items that share the common things that a user rated highly (Content-based filtering) and make suggestions by comparing the interest of a similar user (Collaborative filtering). 2022 IEEE. -
Improving Consumer Engagement with AI Chatbots: Exploring Perceived Humanness, Social Presence, and Interactivity Factors
In many consumer industries, AI robots are becoming more and more popular because they let businesses communicate with their customers in a cheap and quick way. However, how well these measures work rests on how real and present people think they are in social situations. The main things that affect how customers deal with AI chatbots are looked into in this research. These are interaction, social presence, and perceived humanity.A wide range of users will be asked to fill out quantitative polls that will be used to judge how humanlike AI chatbots are, how well they can interact with others, and how much they interact with people. Additionally, performing qualitative interviews will give you a fuller picture of what customers want and how they interact with AI chatbots. Companies can make their chatbot exchanges with customers better by figuring out what makes the bots act like humans: friendly, interested, and sociable. This will allow them to make chatbots that are very specific to their customers' needs and tastes. The goal of this researchprogramme is to make customers happier, more loyal to brands, and have better experiences by creating AI chatbots that can have conversations with people like real people. 2024 IEEE. -
Improving Groundwater Forecasting Accuracy with a Hybrid ARIMA-XGBoost Approach.
In addressing the critical challenge of accurate groundwater level prediction, this study explores the comparative performance of various machine learning models. We implement a novel hybrid model combining ARIMA and Extreme Gradient Boosting (XGB) for the prediction of groundwater levels, and compare it against traditional models including ARIMA, XGBoost, LightGBM, Random Forest, and Decision Trees. Traditional approaches often rely on single models; however, our research seeks to delve into the intricacies of hybrid model architectures. Combining the strengths of ARIMA and XGB, we aim to build a highly accurate and efficient groundwater level prediction system. Comprehensive evaluations were conducted using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), The future scope of machine learning in water resource management includes integrating such models with real-time monitoring systems and expanding their applications to diverse environmental conditions and regions. 2024 IEEE. -
Improving Image Clarity with Artificial Intelligence-Powered Super-Resolution Methods
Super-resolution has advanced significantly in the last 20years, particularly with the application of deep learning methods. One of the most important image processing methods for boosting an image's resolution in computer vision is image super-resolution besides providing an extensive overview of the most recent developments in artificial intelligence and deep learning for single-image super-resolution. This study delves into the subject of image enhancement by investigating sophisticated AI-based super-resolution techniques. High-quality photographs have become more and more in demand in a variety of industries recently, including medical imaging, satellite imaging, entertainment, and surveillance. Pixilation reduction and detail preservation are two areas where traditional image enhancing techniques fall short. Artificial intelligence has demonstrated amazing promise in addressing these issues, especially with regard to Deep Learning models. The applications, benefits, and difficulties of modern super-resolution techniques are thoroughly examined in this work. We also suggest new approaches and push the limits of image enhancement by experimenting with state-of-the-art artificial intelligence algorithms. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Improving Indoor occupancy estimation using a hybrid CNN-LSTM approach
Indoor Air Quality (IAQ) monitoring has been a significant research domain in energy conservation. Many energy resources are required to maintain the IAQ using airconditioning or other ventilation systems. Currently, the research works highly optimize an on-demand driven energy usage depending on the occupant present inside the building. In the last decade, numerous research works have evolved for such an optimization by installing sensors and predicting occupants using machine learning techniques. This research fails to deploy non-intrusive sensors and appropriate machine learning algorithms to predict the occupancy count. Advancement in neural network techniques termed deep learning has made significant performance in recognition and cognitive tasks. Thus, this paper proposes a hybrid deep learning model that stacks the convolutional neural network (CNN) and long short term memory (LSTM) to improve the prediction rate of the occupancy count. Experimentation has been carried out in real-time multivariate sensor data for the occupancy estimation and evaluated the performance in terms of accuracy, RMSE, MAPE, and coefficients of determinants. 2022 IEEE. -
Improving Renewable Energy Operations in Smart Grids through Machine Learning
This paper reviews the work in the areas of machine learning's role in bolstering renewable energy within smart grids. As the global shift towards eco-friendly energy sources such as wind and solar gains momentum, the challenge lies in managing these unpredictable energy sources efficiently. Innovative learning techniques are emerging as potential solutions to these challenges, optimising the use and benefits of renewable energies. Furthermore, the landscape of energy distribution is evolving, with a growing emphasis on automated decision-making software. Central to this evolution is machine learning, with its applications spanning a range of sectors. These include enhancing energy efficiency, seamlessly integrating green energy sources, making sense of vast data sets within smart grids, forecasting energy consumption patterns, and fortifying the security of power systems. Through a comprehensive review of these areas, this paper highlights the potential of machine learning in paving the way for a greener, more efficient energy future. The Authors, published by EDP Sciences, 2024. -
Improving Speaker Gender Detection by Combining Pitch and SDC
Gender detection is helpful in various applications, such as speaker and emotion recognition, which helps with online learning, telecom caller identification, etc. This process is also used in speech analysis and initiating human-machine interaction. Gender detection is a complex process but an essential part of the digital world dealing with voice. The proposed approach is to detect gender from a speech by combining acoustic features like shifted delta cepstral (SDC) and pitch. The first step is preprocessing the speech sample to retrieve valid speech data. The second step is to calculate the pitch and SDC for each frame. The multifeature fusion method combines the speech features, and the XGBoost model is applied to detect gender. This approach results in accuracy rates of 99.44 and 99.37% with the help of RAVDESS and TIMIT datasets compared to the pre-defined methods. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Improvised Model for Estimation of Cable Bending Stiffness Under Various Slip Regimes
It is well known that the bending response of a stranded cable varies between two extremes, known as a monolithic stickslip state and a completely frictionless loose wire state. While the monolithic state offers the maximum stiffness for the cable, the latter loose wire assembly results in minimum stiffness. The estimation of the actual behavior of the cable under any loading scenario demands a proper modeling that accounts for the interaction of the constituent wires in the intermittent slip stages. During loading, the wires are not only subjected to forces along their axes but are considerably acted upon with radial forces that cause clenching effect. Major research works have focused on the frictional resistance of these radial forces from the Coulomb hypothesis, which contributes to the macro slip phenomenon. As the effect of these radial clenching forces are also significant in causing high contact stresses between wires at the adjacent layers, the need for considering the micro slip at these locations is also vital in the evaluation of the net cable stiffness. In this paper, a novel model is proposed that considers the slip caused by the Coulomb friction hypothesis and the micro slip caused by the Hertzian contact friction for the evaluation of bending stiffness. The variation of the bending stiffness has been evaluated for a single-layered cable as a function of bending curvature at various locations by studying their slip regimes. The predicted results are compared with the published results to establish the refined combined slip hypothesis suggested in this paper. The suggested slip model in this paper has also been accounted with the improvised kinematic relations that consider the wire stretch effect, a parameter that has been neglected in this cable research till date. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Indian Road Lanes Detection Based on Regression and clustering using Video Processing Techniques
Detecting the road lanes from moving vehicle is a difficult and challenging task because of road lane markings with poor quality, occlusion created by traffic and poor road constructions. If the driver is not maintaining the road lanes properly, the proposed system detects the road lanes and gives the alarm to the driver so that driver can take the corrective actions there by we can avoid the accidents. The paper mainly focusses on detection of road lanes from sequence of image taken from the video from moving vehicle. The Methodology mainly consisting of lane segments merging and fitting using clustering and weighted regression techniques to fit the curve in the place of group of lane segments and curve fitting separately. 2021, Springer Nature Singapore Pte Ltd. -
INDIVIDUAL AND GROUP VARIATIONS IN WAYFINDING AMONG USERS IN AN EDUCATIONAL BUILDING
The effective performance of users in an Educational Building is determined by the available resources and also the environment in which they dwell. Wayfinding is a daily occurrence for every user of an academic institution and this is facilitated through the distinct articulation of different spaces and recognizable circulation systems. The user behavior in a known/unknown building varies as an individual and with a group of individuals. This variation can be observed in an enclosed space and public setting. For an individual, the psychological state could influence navigating within the building whereas, for a group of individuals, the group dynamics could influence each other to navigate. The paper uses mixed methods to understand and assess the individual and group variations in wayfinding. The study was undertaken in a recently constructed School of Architecture at CHRIST University, Bengaluru. The understanding was accomplished with elaborate literature studies and the assessment was through the field observation techniques and surveys carried out with identified users like frequent individuals, new individuals, frequent groups, and new groups.The study tells that for both individuals and groups, the parameters like architectural elements, sensorial qualities, wayfinding behavior, gender, and psychological state influence them in wayfinding. It was also noted that most of the student users prefer shortcuts rather than the formal entance and lobby to navigate the classrooms. Accomplishing easy, comfortable, and efficient wayfinding within an educational building requires effective layout planning. These findings aim to contribute to the detailed understanding of effective layout planning in an educational building and its impact on user behavior for architects and decision-makers. ZEMCH Network. -
Induction of radio frequency transmission in indian railway for smooth running of traffic during fog
Our railway system drives whole sole based on its electrical signaling but due to poor visibility it becomes impossible to run the traffic smoothly We are suggesting to use radio wave communication technology for running of train when conventional signaling cant be followed due to poor visibility. During winter season, due to heavy fog especially in North India and East India it becomes almost impossible to drive the train on time. Our idea can remove this problem permanently. A dedicated radio frequency band will be used by railway service and a specific frequency will be assigned to all tracks running to a specific direction. All trains will be equipped with a transmitter and a receiver. Train drivers will get notification of received radio frequency within a certain circumference (5 km). So if it receives the same frequency which it is transmitting then the driver will understand another train is there on the same track so signaling room and the driver will also be aware of the fact. Then the control room or the driver can take action considering speed and distance between this two accordingly. If another train will be running on the next track then also it will receive signal but in that case it will run at as usual speed. 2017 Taylor & Francis Group, London. -
Industry Internet of Things (IIoT) Adoption Pressures in SME OEMs
Small and Medium Original Equipment Manufacturers (SME OEMs) face challenges in IIoT adoption due to a lack of technical expertise, additional costs, and preferences of the end-users and significant institutional pressures. This research investigates the influence of Environmental Attitude on the Adoption Intention of Industry Internet of Things (IIoT) technologies among Small and Medium Enterprises and Original Equipment Manufacturers (SME-OEMs). This research analyses the effects of End-user Pull and Institutional Pressure in this relationship. A survey of 263 SME OEMs from 11 industrial sectors across 67 cities was conducted using purposive sampling. Structural Equation Modeling (SEM) was used to analyze data, assessing direct and indirect effects. Results show a significant positive relationship between Environmental Attitude and IIoT Adoption Intention. Mediation analysis reveals significant indirect effects through End-user Pull and Institutional Pressure, with complete mediation as the direct effect becomes insignificant. Findings highlight the crucial role of environmental attitude in shaping IIoT adoption intentions among SMEs. A positive environmental attitude drives SMEs to explore IIoT benefits. End-user Pull and Institutional Pressure are key mediators in this process. These insights are valuable for industry stakeholders, policymakers, and SMEs aiming to promote IIoT adoption. Fostering a positive environmental attitude and leveraging End-user Pull and Institutional Pressure can facilitate IIoT adoption. Policymakers can create initiatives to raise environmental awareness and encourage sustainable practices through IIoT. Industry players can form strategic partnerships to support SME OEMs in IIoT adoption. 2024 IEEE. -
Influence of atmospheric plasma spray process parameters on crystal and micro structures of pyrochlore phase in rare earth zirconate thermal barrier coatings
Yttria-stabilized zirconia (YSZ) thermal barrier coatings is most widely used in gas turbine engines applications and its primary role is to protect the underlying base metal from degradation at its high temperature (>1000 C) service environment. While YSZ serves well in this role, materials with higher thermal stability and lower thermal conductivities are required to be developed for attaining higher operating temperatures and thereby higher energy conversion efficiencies. A number of rare-earth zirconates which form the cubic fluorite-derived pyrochlore structures (A2B2O7) where A: La, Gd, Sm, Ce and B: Zr are being developed, some compositions are more attractive due to their good amalgamation of thermal and mechanical properties. However, when these materials are plasma spray coated on metal substrates, the favorable properties are not immediately realized due to various contributing factors such as poor adhesion/cohesion, microstructure (porosity, defects) or even incomplete stabilization or destabilization of the desired phase (crystal structure) after passing through the plasma. In this paper, plasma sprayable powders of zirconate pyrochlores (or with disordered fluorite structures) synthesized from using La and Ce as the trivalent ''A cation, were plasma sprayed onto Inconel 718 substrates, by using different plasma spray parameters. The considerable influence of these spray parameters on the structural phases (analyzed via XRD) and microstructures (studied via SEM on polished cross section metallographs) are presented in detail. 2019 Elsevier Ltd. All rights reserved. -
Influence of cryogenic treatment of cutting tool inserts on tool wear and surface roughness during milling of Inconel 718
Inconel 718 is a superalloy which is a hard to difficult machining material. It is widely used in industries such as aerospace, defence, energy production, biomechanical and marine. It is used at elevated temperatures and areas where thermal and fatigue stress is high. Due to its superior quality and hard surface, machining of this material becomes a challenge. Cutting tools have failed enormously in milling this material. However, tungsten carbide and ceramics have found some effective features in creating a better machinability. In this paper, microstructure of the inserts have been studied during machining to determine low surface roughness on the material. Cryogenic treatment of the inserts has been carried out to improve tool life and compared with the untreated inserts. 2020 Author(s). -
Influence of heat treatment on the tensile and hardness characteristics of friction stir weld joints of dissimilar aluminium alloys
Friction stir welding (FSW) is a solid-state low energy input welding technique. Most capable of joining very high strength alloys, which are finding wide range of applications in automobile and aerospace components. The current research focuses on the influence of post weld heat treatment on mechanical properties of friction stir weld joints of AA 7075 and AA 5052 dissimilar aluminum alloys. The trial experiments have been carried out using design of experiments (L16 Orthogonal Array) and the optimized process parameters have been selected based on the maximum hardness and the corresponding ultimate tensile strength (UTS). Further, the friction stir welding is accomplished with optimized process parameters (L9 Experimental trial) viz., the feed rate of 100?mm/min, tool rotational speed of 1200?rpm, tool offset of (-) 0.5?mm and using a cylindrical taper pin tool profile. The post heat treatment has been carried out on the friction stir weld joints obtained using the optimized parameters and the mechanical properties of the L9 Heat Treated (L9 - HT) and L9 - Non Heat Treated (L9 - NHT) specimens have been compared. The results shows that the post heat treated weld joints have higher micro hardness and tensile strength compared to the non-heat-treated weld joints. This is majorly attributed to recrystallization and elimination of voids due to the change in the microstructure of the weld joint. 2022 Author(s). -
Influence of manufacturing process on distribution of MWCNT in aluminium alloy matrix and its effect on microhardness
Nano composites are finding increased focus and their influence on improving the matrix properties are very attractive. But the success is fully dependent on the uniform distribution and dispersion of nano reinforcements in the matrix. Manufacturing process was found to have greater role in distribution of the reinforcements. The liquid processing and solid processing like SPS and hot coining found to have different effect on the matrix due to the nature of reinforcements. Current study focussed on the microstructure study using Back scattered images and the microhardness with and without reinforcements. MWCNT was occupying the particle boundary. Hot coining was found to distribute MWCNT on the particle surface as well as on the particle boundary. Clustering was absent and resulted in improved hardness in comparison with casting as well as spark plasma sintering. 2018 Trans Tech Publications, Switzerland. -
Influence of nano ?-Al2O3 as sintering aid on the microstructure of spray dried and sintered ?-Al2O3 ceramics
Alpha Alumina (?-Al2O3) has traditionally been sintered to near theoretical density by employing variations in raw material properties, particle sizes, grinding methods, compaction pressures, sintering aids or minor quantities of additives and sintering temperatures. All these parameters directly influence the grain growth morphology and microstructure of the sintered alumina ceramic characteristics. Growth of large grained microstructure facilitated by fine grinding of raw material and coalescence of the grains enhanced by dopant additions are well researched. The maximum sintered density and strength of the fired body could be attained through large grained microstructure which include near spheroidal grains. Most of the final sintering is accomplished via additions of suitable aids which also may be promoted by liquid-phase sintering which is considered highly advantageous compared to solid-state sintering for products in many defense applications. In this paper the influence of nano ?-Al2O3 (<100 nm particle size) as sintering aid to obtain the desired microstructure in sintered micron sized (1 to 5 m) ?-Al2O3 is being reported. 1.0 and 1.5 wt% nano ?- Al2O3 powder were spray dried with 99.0 and 98.5% ?-Al2O3 powder respectively, with polyvinyl alcohol binder, compacted into 10 mm dia and 5 mm thick pellets and sintered at 1450 C with 3 h soak time. In addition to the two different sintering aid additive percentages, other variables included are spray dried powders removed from (i) chamber and (ii) cyclone. The sintered ceramics were characterized for bulk density and fracture surface microstructure via SEM analysis. Nano alumina as sintering aid exhibited significant influence that included generation of microstructure with porosity, precipitation or liquid phase sintering. The study was limited to establishing the definitive role played by nano alumina to influence the sintering of micron alumina. 2022 -
Information extraction and text mining of Ancient Vattezhuthu characters in historical documents using image zoning
The aim of this paper is to develop a system that involves character recognition of Brahmi, Grantha and Vattezuthu characters from palm manuscripts of historical Tamil ancient documents, analyzed the text and machine translated the present Tamil digital text format. Though many researchers have implemented various algorithms and techniques for character recognition in different languages, ancient characters conversion still poses a big challenge. Because image recognition technology has reached near-perfection when it comes to scanning English and other language text. But optical character recognition (OCR) software capable of digitizing printed Tamil text with high levels of accuracy is still elusive. Only a few people are familiar with the ancient characters and make attempts to convert them into written documents manually. The proposed system overcomes such a situation by converting all the ancient historical documents from inscriptions and palm manuscripts into Tamil digital text format. It converts the digital text format using Tamil unicode. Our algorithm comprises different stages: i) image preprocessing, ii) feature extraction, iii) character recognition and iv) digital text conversion. The first phase conversion accuracy of the Brahmi script rate of our algorithm is 91.57% using the neural network and image zoning method. The second phase of the Vattezhuthu character set is to be implemented. Conversion accuracy of Vattezhuthu is 89.75%. 2016 IEEE. -
Information Extraction Using Data Mining Techniques For Big Data Processing in Digital Marketing Platforms
In the dynamic landscape of digital marketing, harnessing the potential of big data has become paramount for informed decision-making. This study explores the integration of data mining techniques within big data processing frameworks to extract valuable information in digital marketing platforms. With the exponential growth of data generated through online interactions, social media, and e-commerce, traditional methods fall short of uncovering meaningful insights. This research focuses on leveraging advanced data mining algorithms to sift through vast datasets, identifying patterns, trends, and user behaviours. The proposed approach aims to enhance marketing strategies by extracting actionable intelligence from diverse data sources. Techniques such as association rule mining, clustering, and sentiment analysis will be employed to unveil hidden correlations, segment target audiences, and gauge consumer sentiment. The scalability of big data frameworks ensures efficient processing of massive datasets, allowing marketers to make real-time, data-driven decisions. Additionally, the study explores the challenges and opportunities associated with implementing data mining in big data environments for digital marketing. This research contributes to the evolving field of digital marketing by providing a comprehensive framework for extracting, processing, and utilizing information from big data. The findings promise to empower marketers with a deeper understanding of consumer behaviour, enabling the development of more personalized and effective marketing strategies in the ever-evolving digital ecosystem. 2023 IEEE. -
Innovation Characteristics, Personality traits and their impact on Fintech Adoption-P2P Lending
This paper investigates moderating influence of innovation attributes on the perceptions of Peer-to-Peer or P2P lending users and the influence of innovativeness traits on instrumental beliefs regarding the adoption of P2P lending. Two technology adoption theories were combined to develop the conceptual map denoting antecedent factors. Using 464 responses, structural equation modeling analysis was used to test the hypotheses. Performance expectancy, effort expectancy, social influence, and perceived compatibility were salient antecedents of P2P lending adoption. Perceived compatibility moderates the relationship between performance expectancy, facilitating conditions, and buying intentions. Innovativeness trait predicts performance expectancy and effort expectancy of P2P lending users. 2024 IEEE.