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Recommendation System using Clustering and Comparing Clustering and Topic Modelling Techniques
In this paper, we have used a technique called clustering to recommend the products to the customer and also tried to compare clustering and Topic modelling to find out which technique is better for our purpose. From all the papers that have been reviewed, we observed that the greater part of the proposal approaches applied content-based filtering (55%). Collaborative-based filtering was applied by just 18% of the looked into approaches, and hybrid based by 16%. Other suggestion ideas included generalizing, thing driven proposals, and crossover suggestions. The content-based filtering approaches overwhelmingly utilized papers that the clients had made, marked, examined, or downloaded [1]. To begin with, it stays muddled which suggestion ideas and approaches are the most encouraging. For instance, analysts demonstrated different results on the presentation of content based and collaborative filtering. A portion of the time content-based filtering performed better contrasted with collaborative filtering sand a portion of the time it performed all the more regrettable. 2022 IEEE. -
Recommendations from teachers on schools' roles in identifying problems and building awareness among students
Students develop skills, gain knowledge, and achieve greater wellbeing by creating a positive school environment. Through the years, schools have realized the importance of mental health services for adolescents. Research on the role of schools in mental health awareness building and preventing mental health problems is meager, and focuses on students in the western context. This chapter focuses on the recommendations given by teachers on what role schools can play in identifying, preventing, and building awareness among adolescents. These recommendations are based on the themes obtained through semi-structured interviews with 24 teachers teaching 10th, 11th, and 12th graders in private high schools and colleges in Bangalore. Consequently, it aims to provide an overview of incorporating techniques and strategies to enhance mental health among school students in the Indian Scenario. 2024, IGI Global. All rights reserved. -
Recommender system for personalised travel itinerary
A recommender system is an approach to give an appropriate solution to a particular problem. This helps in recognising the pattern or behaviour of a user to suggest future possible likes of the user. Nowadays people like to travel during their spare time, it has become a rigid task to decide where to go. This paper represents a customised recommender system to help users in destining their itinerary. A model is designed to suggest the best places to visit in Rome. A questionnaire was prepared to get information about user's interest during their travel. The model generates the best five places to visit with respect to the choice picked by the user. The top five places for each category will be displayed to the user and the user was asked to pick a starting point for the itinerary. Then the model generates another set off a filtered list of places to enhance their travel experience. It includes displaying the top 5 restaurants to visit during their travel. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Recommender system for surplus stock clearance
Accumulation of the stock had been a major concern for retail shop owners. Surplus stock could be minimized if the system could continuously monitor the accumulated stock and recommend those which require clearance. Recommender Systems computes the data, shadowing the manual work and give efficient recommendations to overcome stock accumulation, creating space for new stock for sale to enhance the profit in business. An intelligent recommender system was built that could work with the data and help the shop owners to overcome the issue of surplus stock in a remarkable way. An item-item collaborative filtering technique with Pearson similarity metric was used to draw the similarity between the items and accordingly give recommendations. The results obtained on the dataset highlighted the top-N items using the Pearson similarity and the Cosine similarity. The items having the highest rank had the highest accumulation and required attention to be cleared. The comparison is drawn for the precision and recall obtained by the similarity metrics used. The evaluation of the existing work was done using precision and recall, where the precision obtained was remarkable, while the recall has the scope of increment but in turn, it would reduce the value of precision. Thus, there lies a scope of reducing the stock accumulation with the help of a recommender system and overcome losses to maximize profit. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Recommender Systems Using Semantic Web Technologies
Recommender Systems (RS) have risen in popularity over the years, and their ability to ease decision-making for the user in various domains has made them ubiquitous. However, the sparsity of data continues to be one of the biggest shortcomings of the suggestions offered. Recommendation algorithms typically model user preferences in the form of a profile, which is then used to match user preferences to items of their interest. Consequently, the quality of recommendations is directly related to the level of detail contained in these profiles. Through the review of related literature, it is evident that the genre of a movie is a major factor influencing user decisions about movies. However, the degree of membership of a movie to a genre is typically unavailable. Sometimes, certain genres memberships to a movie might not be assigned at all. Such genre membership information, if available, would provide a better description of items and consequently lead to quality recommendations. To capture complete information on content pertaining to different genre in movies, we have used two approaches ?? one that utilizes the available binary genre information and augments it by inferring the genre degree using the information available in folksonomies and another that does not rely on previous movie categorization but captures genres that manifest automatically when forming keyword clusters. Folksonomies or tags are user-defined metadata for items and embed abundant information about various facets of user likes and their opinions on the quality and the type of object tagged. The degree of genre presence in a movie is inferred by examining the various tags conferred on them by various users. Leveraging on tags to guide the genre degree determination exploits crowd sourcing to enrich item content description. Fuzzy logic naturally models human logic, allowing for the nuanced representation of features of objects and thus is utilized to derive such gradual representation as well as for modelling user profiles. Fuzzy user and object representations are leveraged for the design of both content-based as well as collaborative recommender systems. Experimental evaluations establish the effectiveness of the proposed approaches as compared to other baselines. We call this the Fuzzy User-Based Recommendation Approach (FUBRA). Keywords related to a movie indirectly contain information related to the various narrative styles. User profiles are also constructed based on user preferences for such keyword clusters. We call this the Keyword Clustering-Based Recommendation Approach (KCBRA).These profiles are then utilized to perform both Content-Based (CB) filtering as well as Collaborative Filtering (CF). This approach scores over the direct keyword-matching, genre-based user profiling method and the traditional CF methods under sparse data scenarios as established by various experiments. -
Reconceptualizing Empowerment And Autonomy: Ethnographic Narratives From A Self Help Group In South India
The paper revisits academics' conceptualizations of women empowerment as stopping short of autonomy. It departs from the general observation that women empowerment movements by and large have failed to translate the new agency of women outside the domains of socio economy; that women empowerment movements' capacity to re-engage with patriarchal structures and ideologies is seriously contained. Through an ethnography of Kudumbashree, an SHG in the South Indian state of Keralam, we question the neat distinctions between empowerment and autonomy that prevail in the academic common sense. The transition of agency from the economic to the political domain is a subtle enterprise and is mediated by a number of factors including the economic independence, decision making capability and political participation. Socio -economic - political implications of women empowerment could be the first step in challenging and overcoming the relations of oppression in any society. The stereotypical assumptions can be negotiated by solely apportioning responsibilities and re-engaging with the system through everyday practices. The nuances of empowered women's re-engagement with local gender/power regimes lead to changes at the conceptual level that cuts beyond the individual and group level material transformations. The Electrochemical Society -
Reconciling Privacy Rights With Digital Investigations: Legal-Ethical Challenges in Cyber Forensics
The advancement of cyber forensics-through Al-driven profiling, metadata analysis, and real-time surveillance has enhanced digital investigations but raised significant legal and ethical concerns. This chapter explores the growing tension between state surveillance and individual privacy, focusing on the Indian legal framework, includ-ing the Justice K.S. Putaswamy decision, and comparing it with global standards such as the GDPR, ECHR, and Budapest Convention. Key issues addressed include algorithmic bias, lack of consert, cross-border data sharing, and misuse of forensic tools against vulnerable groups. The chapter argues for a balanced, rights-based framework grounded in privacy-by-design, proportionality, and judicial oversight. It offers policy recommendations to ensure cyber forensic practices remain effective while upholding constitutional protections and international human rights norms. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Reconfigurable Intelligent Surface for mMIMO and NOMA Networks: Applications and Research Challenges
Reconfigurable Intelligent Surfaces are emerging as a transformative technology in wireless communication, particularly in the context of massive Multiple-Input Multiple-Output (mMIMO) systems and Non-Orthogonal Multiple Access (NOMA) networks. This chapter provides an in-depth exploration of how RIS can enhance the performance of mMIMO and NOMA networks, focusing on both practical applications and research challenges. RIS technology enables dynamic control over the wireless environment by adjusting signal reflections and enhancing signal propagation, which can significantly improve the efficiency and effectiveness of mMIMO and NOMA systems. For mMIMO, RIS can optimize spatial beamforming and mitigate interference, leading to enhanced capacity and coverage. In NOMA networks, RIS can This chapter offers a comprehensive overview of the potential of RIS to revolutionize mMIMO and NOMA networks, while also addressing the critical research challenges that must be overcome to fully realize its benefits. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Reconfigurable Intelligent Surface-Aided Physical Layer Security Techniques: Applications and Future Trends
Reconfigurable Intelligent Surfaces (RIS) are emerging as a groundbreaking technology in the realm of wireless communications, with significant implications for enhancing physical layer security. This chapter delves into the integration of RIS with advanced security techniques, exploring how this innovative technology can be harnessed to address the growing challenges of securing wireless networks. this chapter delves into the future trends and advancements in RIS technology, including next-generation RIS architectures and their potential integration with emerging technologies like 6G. It explores how RIS could pave the way for innovative security protocols and play a pivotal role in advancing secure wireless network infrastructures. RIS technology enables the dynamic and intelligent modification of radio environments through programmable surfaces, which can adjust and optimize signal paths to improve both communication efficiency and security. This chapter provides valuable insights into the current applications and future prospects of RIS in enhancing wireless network security. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Reconfigurable non-uniform band-generating filter bank for channelizer
Multi-band channelizer system must choose a specific channel from a broad bandwidth signal. A variety of distinct wireless standards and frequency bands are used in the channelizer. Reconfigurable and non-uniform multi-channels with narrow transition widths are necessary for channelizers. In this paper, a low complexity reconfigurable non-uniform band-generating filter bank (RNBFB) is proposed for multi-band channelizer. The RNBFB is used to generate a variety of non-uniform channels with a narrow transition width. Utilising frequency response masking (FRM) and the cosine modulation (CM) approach, many non-uniform channels are created. Comparing RNBFB to other state-of-the-art techniques, RNBFB generates multi-bands for channelizer with less multiplier complexities. For a better understanding of hardware complexity, the proposed RNBFB is implemented efficiently. A multiplier-free design such as Canonical Signed Digit (CSD), Multi-Objective Artificial Bee Colony (MOABC), and Shift Inclusive Differential Coefficients (SIDC) with a Common Sub-expression Elimination (CSE) are included in the suggested strategy to further optimise the RNBFB. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
Reconstruction of sparse-view tomography via preconditioned Radon sensing matrix
Computed Tomography (CT) is one of the significant research areas in the field of medical image analysis. As X-rays used in CT image reconstruction are harmful to the human body, it is necessary to reduce the X-ray dosage while also maintaining good quality of CT images. Since medical images have a natural sparsity, one can directly employ compressive sensing (CS) techniques to reconstruct the CT images. In CS, sensing matrices having low coherence (a measure providing correlation among columns) provide better image reconstruction. However, the sensing matrix constructed through the incomplete angular set of Radon projections typically possesses large coherence. In this paper, we attempt to reduce the coherence of the sensing matrix via a square and invertible preconditioner possessing a small condition number, which is obtained through a convex optimization technique. The stated properties of our preconditioner imply that it can be used effectively even in noisy cases. We demonstrate empirically that the preconditioned sensing matrix yields better signal recovery than the original sensing matrix. 2018, Korean Society for Computational and Applied Mathematics. -
Recruitment Analytics: Hiring in the Era of Artificial Intelligence
Introduction: Traditional recruitment system relied heavily on the applicants curriculum vitae (CV). This system, besides becoming redundant, has proved to be a futile exercise leading to the hiring of candidates that eventually turn out to be misfits. CVs were the only source of candidates data available for the recruiters a few years back. Face-to-face interviews was considered to be the ultimate solution for hiring suitable candidates. However, evidence suggests that interview scores and job performances do not complement each other. Advancement in artificial intelligence (AI) has introduced several techniques in the recruitment process. Purpose: This chapter underscores the drawbacks of the traditional recruitment process. Evidence suggests that the traditional recruitment process is prone to subjectivity and is time-consuming. Surprisingly, despite the disadvantages, the integration of AI into the recruitment process is still slow. This chapter highlights the need to harness AI and the advantage technology could bring to the recruitment process. Some of the techniques that are garnering attention and widely used by organisations, such as chatbots, gamification, virtual employment interviews, and resume screening are described to enable the readers to understand with less effort. Chatbots and gamification techniques are described through process flow charts. We also describe the various types of interviews that could be conducted through virtual platforms and the modality by which the resume screening technique operates. Today, we are at a juncture wherein it is pertinent to acknowledge the superiority of technology-driven processes over traditional ones. This chapter will help the readers to understand the modus operandi to implement chatbots, gamification, virtual interviews and online resume screening techniques besides their advantages. Scope: Although chatbots, resume screening, virtual interviews, and gamification are used in other areas, too, such as training and development, marketing, etc., in this chapter, we restrict solely to employee recruitment processes. Methodology: Scoping review is used to examine the existing literature from various databases such as Google Scholar, IEEE, Proquest, Emerald, Elsevier, and JSTOR databases are used for extracting relevant articles. Findings: Automation and analytics in recruitment and selection remove bias which is otherwise increasingly found in manual hiring processes. Also, previous studies have observed that candidates engage in impression management tactics in traditional face-to-face interviews. However, through automated recruitment processes, the influence of these tactics can be eliminated. AI-based virtual interviews reduce human bias. It also helps recruiters to hire talents across the globe. Gamification improves the candidates perception of the work and work environments. Through gamified techniques, the recruiters can understand whether a candidate possesses the required job skills. Chatbots are an interactive technique that can respond to interviewees queries. Resume screening techniques can save the recruiters time by screening and selecting the most appropriate candidates from a large pool. Hence, the chosen candidates alone can be referred to the next stage of the recruitment cycle. AI improves the efficiency of the recruitment process. It reduces mundane tasks. It saves time for the human resources (HR) team. 2023 by V. R. Uma, Ilango Velchamy and Deepika Upadhyay. -
Rectangular microstrip antenna for WLAN application
This paper deals with the design of rectangular microstrip patch antenna for Wireless applications. In this paper a modified slotted microstrip antenna design for 2.5GHz operation is proposed. This provides improved performance in terms of lower return loss and higher gain. This is possible by inclusion of slots appropriately on the patch shape. The substrate material used in this design is Duroid5880 with permittivity 2.2 and size 47.43mm 39.65mm 1.6mm. ANSOFT HFSS EM simulator has been used for design and simulation of the microstrip antenna. The various antenna parameters such as frequency, VSWR, gain and directivity are analyzed to characterize the proposed antenna. 2015 IEEE. -
Rectangular Microstrip Patch Antenna Array Based Sectored Antenna for Directional Wireless Sensor Networks
Directional wireless Sensor Network (DSN) outperforms Wireless Sensor Network (WSN) over different parameters such as transmission range, interference, spatial reusability and energy efficiency. In this paper, a Rectangular Patch antenna Array (RPA) based sectored antenna is proposed for DSN. The individual sector is composed of two-element rectangular patch antenna array with a measured peak gain of 5.2 dBi and half-power beamwidth of 45. Single Pole 8 Throw (SP8T) Radio Frequency (RF) switchboard is designed to connect the sectored antenna to MICAz WSN mote. The antenna performance analysis carried out in simulation and real-time measurement via Ansys High Frequency Structure Simulator (HFSS) and Vector Network Analyzer (VNA) exhibits higher gain, lower return loss, half-power beamwidth and Voltage Standing Wave Ratio (VSWR). 2020 IEEE. -
Rectifying Whole Brain Segmentation Errors Using a Novel Under-Segmentation Correction Method
Pre-processing is a critical step in any data-driven study, particularly in the field of medical imaging, where it significantly enhances the reliability of disease and disorder diagnosis. In this context, medical image segmentation allows for more precise data analysis by isolating the regions of interest. Accurate segmentation of these regions can reveal influential variabilities in analysis, potentially leading to unique scientific findings. This article presents a novel under-segmentation error correction technique specifically designed for whole-brain segmentation. Additionally, it performs a set of pre-processing steps for the structural magnetic resonance imaging (sMRI) images, which are necessary to maintain the structural integrity and uniformity of MRI scans across different subjects. The proposed algorithm effectively eliminates under-segmentation errors, thereby improving the accuracy of whole-brain segmentation, particularly for structurally intact brain images. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Recurrent Neural Networks in Predicting the Popularity of Online Social Networks Content: A Review
An online social network is a web platform that individuals use to make social relationships with people who share similar interests, activities, connections, and backgrounds. All online social networks differ in the number of features they provide and their format. In recent years, drastic growth has been seen in the users of online social networks like Flickr, Instagram, Pinterest, Twitter, etc. Among all the features of online social networks, content sharing is the one being widely used by individual users and large organizations. Due to this, content popularity prediction has been extensively studied nowadays, considering various aspects related to it. The study throws light on the use of machine learning techniques in this field. Various algorithms have been used to handle popularity prediction, including classification, regression, and clustering techniques. It is feasible to extract the essential information from such content using machine learning algorithms and utilize the retrieved information in a variety of ways, the majority of which are commercial in nature. The goal of this study is to review and analyze various recurrent neural network (RNN) approaches for predicting the popularity of social media content. The Electrochemical Society -
Recyclable layered chromite-based porous film for water cleaning
The oil spillage and pollutants in water bodies are a significant concern in the present time. To address this concern, a porous and superhydrophobic nanocomposite film containing layered natural chromite ores with a polymer was fabricated using a simple solution casting method. The flexible film exhibited a good tensile strength of 1.022 kg mm?2 and self-cleaning properties. It showed an excellent oil adsorption of up to ?268% for castor oil and an adsorption efficiency of ?90% for toxic cationic dyes. The presence of high surface charges on the chromite nanosheets enhanced its adsorbing capability. Furthermore, even after being resynthesized from old used film, the composite film maintained its mechanical strength, hydrophobicity, and adsorbing capabilities. Therefore, we believe that the present work can help in cleaning oil and other pollutants from large water bodies and consequently preserving aquatic life. 2025 The Royal Society of Chemistry. -
Recycle of sea food waste into mobile cases by 3D printing /
Patent Number: 202141028807, Applicant: A. Joseph Arockiam.
The problem of plastic recycling has recently become one of the significant environmental protection and waste management concerns. In many sectors of everyday life and business, polymer materials have been discovered to have applicable. In addition to their widespread usage, plastic wastes were also a concern since they remained persistent and harmful waste after their removal from use. The invention will lead to the first step towards reducing pollution via the replacement of synthetic polymer for mobile accessories into biodegradable waste (seafood waste). -
Recycled Surgical Mask Waste as a Resource Material in Sustainable Geopolymer Bricks
With the advent of the COVID-19 pandemic, the global consumption of single-use surgical masks has risen immensely, and it is expected to grow in the coming years. Simultaneously, the disposal of surgical masks in the environment has caused plastic pollution, and therefore, it is exigent to find innovative ways to handle this problem. In this study, surgical masks were processed in a laboratory using the mechanical grinding method to obtain recycled surgical masks (RSM). The RSM was added in doses of 0%, 1%, 2%, 3%, and 4% by volume of geopolymer bricks, which were synthesized with ground granulated blast furnace slag (GGBS), rice husk ash (RHA), sand, and sodium silicate (Na2SiO3) at ambient conditions for a duration of 28 days. The developed bricks were tested for compressive strength, flexural strength, density, water absorption, efflorescence, and drying shrinkage. The results of the study reveal that compressive strength and flexural strength improved with the inclusion of RSM in the bricks. The highest values of compressive strength and flexural strength were 5.97 MPa and 1.62 MPa for bricks with 4% RSM, respectively. Further, a reduction in the self-weight of the bricks was noticed with an increase in RSM. There was no pronounced effect of RSM on the water absorption and efflorescence properties. However, the RSM played a role in reducing the drying shrinkage of the bricks. The sustainability analysis divulges the catalytic role of RSM in improving material performance, thereby proving to be a potential candidate for low-carbon material in the construction industry. 2023 by the authors. -
Recycling carbon tax for inclusive green growth: A CGE analysis of India
In this decade, India has been pursuing a low carbon inclusive growth strategy. However, carbon tax, the most direct price instrument to reduce carbon emissions, has not found favour with policymakers because of its supposed detrimental effects on economic growth and income distribution. In the Indian context, the literature indicates that though carbon tax is extremely effective in abating carbon emissions, it simultaneously leads to reductions in GDP. There is, thus, an undesirable trade-off between economic growth and climate change mitigation. However, in trying to overcome this trade-off through a double-dividend from carbon tax, these studies have not really explored all possible options. Whether the carbon tax will yield a double-dividend or not, will depend upon how the carbon tax revenue is recycled. The present paper fills this gap in the literature on recycling carbon tax for inclusive green growth by exploring the consequences of using carbon tax revenue for investment to build capacity in all sectors or exclusively in the clean energy sectors and to execute transfers to households to improve the distribution of income. This analysis has been done with a recursively dynamic India-specific CGE model having a disaggregated energy sectors and an endogenous income distribution module. 2020 Elsevier Ltd