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How to win back the disgruntled consumer? The omni-channel way /
Journal of Business And Retail Management Research, Vol.12, Issue 4, pp.200-207, ISSN No. 2056-6271. -
How well the log periodic power law works in an emerging stock market?
A growing body of research work on Log Periodic Power Law (LPPL) tries to predict market bubbles and crashes. Mostly, the fitment parameters remain con?ned within certain specific ranges. This paper examines these claims and the robustness of the reformulated LPPL model of Filimonov & Sornette (2013) for capturing large falls in the S&P BSE Sensex, an Indian heavyweight index over the period 20002019. Thirty-five mid to large-sized crashes are identified during this period, forming a clear LPPL signature. This confirms the possibility to predict the embedded risk of future uncertain events in the Indian stock market with the LPPL approach. 2020 Informa UK Limited, trading as Taylor & Francis Group. -
HQA Bot: Hybrid AI Recommender Based Question Answering Chatbot
The COVID pandemic has presented a number of challenges for education, particularly when it comes to reaching and engaging students. As a result, online education has become increasingly important, and artificial intelligence (AI) has played a crucial role in supporting this shift. The proposed tutor assistance question-answering system uses AI to automatically generate responses to student questions. This system includes a feedback mechanism, known as a satisfaction index that measures the efficiency of the generated responses and suggest relevant follow-up questions. The proposed Hybrid Recommender-based Dijkstras algorithm (HRD) improves the system's accuracy. This algorithm uses a combination of techniques to group relevant questions based on context, which improves the accuracy of identifying the next relevant question. In our customized dataset, this approach achieved an accuracy of 96% and an average accuracy of 82% across benchmarked datasets. With this system, we aim to bridge the gap between students and education by providing a more engaging and personalized learning experience. 2023, Ismail Saritas. All rights reserved. -
HTLML: Hybrid AI Based Model for Detection of Alzheimers Disease
Alzheimers disease (AD) is a degenerative condition of the brain that affects the memory and reasoning abilities of patients. Memory is steadily wiped out by this condition, which gradually affects the brains ability to think, recall, and form intentions. In order to properly identify this disease, a variety of manual imaging modalities including CT, MRI, PET, etc. are being used. These methods, however, are time-consuming and troublesome in the context of early diagnostics. This is why deep learning models have been devised that are less time-intensive, require less high-tech hardware or human interaction, continue to improve in performance, and are useful for the prediction of AD, which can also be verified by experimental results obtained by doctors in medical institutions or health care facilities. In this paper, we propose a hybrid-based AI-based model that includes the combination of both transfer learning (TL) and permutation-based machine learning (ML) voting classifier in terms of two basic phases. In the first phase of implementation, it comprises two TL-based models: namely, DenseNet-121 and Densenet-201 for features extraction, whereas in the second phase of implementation, it carries out three different ML classifiers like SVM, Nae base and XGBoost for classification purposes. The final classifier outcomes are evaluated by means of permutations of the voting mechanism. The proposed model achieved accuracy of 91.75%, specificity of 96.5%, and an F1-score of 90.25. The dataset used for training was obtained from Kaggle and contains 6200 photos, including 896 images classified as mildly demented, 64 images classified as moderately demented, 3200 images classified as non-demented, and 1966 images classified as extremely mildly demented. The results show that the suggested model outperforms current state-of-the-art models. These models could be used to generate therapeutically viable methods for detecting AD in MRI images based on these results for clinical prospective. 2022 by the authors. -
Hubble Space Telescope Captures UGC 12591: Bulge/disc properties, star formation and 'missing baryons' census in a very massive and fast-spinning hybrid galaxy
We present Hubble Space Telescope ( HST ) observations of the nearby, massive, highly rotating hybrid galaxy UGC 12591, along with observations in the UV to FIR bands. HST data in V , I , and H bands is used to disentangle the structural components. Surface photometry shows a dominance of the bulge o v er the disc with an H-band B / D ratio of 69 per cent . The spectral energy distribution (SED) fitting reveals an extremely low global star formation rate (SFR) of 0 . 1-0 . 2 M yr-1 , exceptionally low for the galaxy's huge stellar mass of 1 . 6 0 11 M, implying a strong quenching of its SFR with a star formation efficiency of 3-5 per cent. For at least the past 10 8 yr, the galaxy has remained in a quiescent state as a sterile, 'red and dead' galaxy. UGC 12591 hosts a supermassive black hole (SMBH) of 6 . 18 0 8 M, which is possibly quiescent at present, i.e. we neither see large (1kpc) radio jets nor the SMBH contributing significantly to the mid-IR SED, ruling out strong radiative feedback of AGN. We obtained a detailed census of all observable baryons with a total mass of 6 . 46 0 11 M within the virial radius, amounting to a baryonic deficiency of 85 per cent relative to the cosmological mean. Only a small fraction of these baryons reside in a warm/hot circumgalactic X-ray halo, while the majority are still unobservable. We discussed various astrophysical scenarios to explain its unusual properties. Our work is a major step forward in understanding the assembly history of such e xtremely massiv e, isolated galaxies. 2022 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Human behavior analysis of BBC-news comments posted on facebook using lexicon-rule based approach
Today people spend a considerable part of their time on online platforms say, social media than with the real world. Social media, particularly Facebook is the platform for the users to post, share, like, tag and comment any photos and videos. This paper deals with the Facebook platform to study the human behavior based on the comments of five posts from BBC-news Facebook page. For every post in Facebook we can get different opinion or emotional behavior by different users. The behavior of people to the same event need not be similar, they can be different. A response through comments and smileys for a post portrays behaviors of people. Here the behavior analysis is performed on comments of the BBC news Facebook posts. The comments of the post are fetched by the online extractor named Socialfy [12]. This paper considered five news from unique from BBC-news Facebook page. The human behavior analysis performed using Python VADER (Valence Aware Dictionary and Sentiment Reasoner) package. This work uses the Lexicon approach to assign scores for the words and rule-based approach used to find the polarity type of words. The polarity of a post is the sentimental behavior of the people towards the post. The total polarity of this work tends towards neutral so, we could conclude that for each situation behavior of man can take positive or negative poles. 2019, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Human behavior analysis on political retweets using machine learning algorithms
The exponential rise in the use of social media has resulted in a massive increase in the volume of unstructured text created. This content is presented through messages, conversations, postings, and blogs. Microblogging has become a popular way for people to share what they are thinking. Many people express their thoughts on various issues relating to their hobbies. As a result, microblogging websites have become a valuable resource for opinion mining and sentiment research. Twitter is a well-known microblogging network, with over 500 million new tweets posted daily. The goal of this study was to mine tweets for political sentiments. The extraction of tweets relating to India's well-known political leaders of different states & parties in India and applying the polarity detection analysis of human behavior on the retweeted messages As a result, the sentiment classification algorithm is designed to determine whether tweets are more likely to predict the popularity of certain politicians among the general public. The subjectivity and polarity present in the tweets of political leaders are compared. The engagements of these leaders are then taken into account to determine their popularity. All these comparisons are then portrayed using data visualizations. 2023 The Authors -
Human cognition and emotional response towards visual environmental features in an urban built context: a systematic review on perception-based studies
Urban built environments can influence human cognitive and emotional comforts. Human comfort in the built environment has challenged architects and urban designers while developing comfortable spaces. Emerging cognitive-architectural studies in architecture engineering inform new directions for improvising human spatial design practices. This paper intends to present a systematic meta-analysis of selected empirical studies to identify the gaps and future scope of research in human cognition and built environments. However, the scope of the literature review is to concentrate on experiments that consider physiological reading in different environments, such as nature and architectural spaces in cognitive study areas. The peer-reviewed literature published from 2010 to 2021 illustrates that only limited design parameters are considered in these experiments. The study analyses the extensive consideration of experimental medium, simulation categories, and participant factors like gender and age in this research domain. The survey recommends considering more visual features, contextual conditions, and ethnic groups. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
Human Resource Development Climate and its Impact on Stress Among Teachers in Unaided Education Institutions in Bangalore
Global Journal of Finance & Management, Vol-4 (4), ISSN-0975-6477 -
Human rights and religion : Perspectives and retrospectives /
Asian Journal Of Research In Social Science & Humanities, Vol.6, Issue 1, pp.80-89, ISSN: 2249-7315. -
Humanising History through Graphic Narratives: Exploring Stories of Home and Displacement from the North-East of India
Literature from the North-East has responded to national, global and local issues, including questions on immigration and ethnic violence. They have resisted the colonial framework of representation and have invoked a sense of cultural and ethnic particularity (Sarma, 2013). This literature has adopted a multilingual register to respond to 1) patriarchal and 2) ethnonationalist discourses that have a forced and overbearing presence in the everyday lives of people and their stories. These writings evoke an ethno-critical approach that engages otherness and difference in such a way as to provoke an interrogation of and a challenge to our familiar realms of experience and is consistent with a recognition and legitimation of heterogeneity (Sarma, 2013). Select stories from First Hand (Volume II, 2018) - The Lonely Courtyard (2018), My Name is Jahanara (2018), and A Market Story (2019) by Kumdo Yumnam provide the heterogeneity that is characteristic of the works of literature emerging from the North-East, thereby resisting the homogeneity often indicative of the term 'North-East'. The analysis will explore how the selected texts negotiate textuality and visuality in a specific manner to present an archive of everyday life that humanises history. 2022 Aesthetics Media Services. All rights reserved. -
Humour as a Moderator Between Hassles and Well-Being
Humour is a universal phenomenon that offers several physiological and psychological benefits across cultures. The objectives of this study were to examine the relationships between daily hassles, humour and well-being; and to investigate the moderating effect of humour on the relationship between hassles and well-being. A correlational design was adopted to collect data from 644 participants (men = 300, women = 344), aged between 18 and 58years using purposive and snowballing sampling techniques. The Daily Hassles Scale, Sense of Humour Questionnaire (SHQ-R) and the Personal Well-Being IndexAdult (PWI-A) were administered to the sample. The self-report measures were appropriately scored and the collective data were analyzed. Statistical analyses revealed a positive relationship between sense of humour and well-being. A negative relationship was observed between sense of humour and hassles; and between well-being and hassles. Further, sense of humour was found to be moderating the relationship between daily hassles and well-being. This study highlights the role of humour in softening the impact of hassles on the well-being of the Indian population. This strengthens the construct of humour in the context of positive psychology. The Author(s) under exclusive licence to National Academy of Psychology (NAOP) India 2024. -
Humour as a moderator of stress and defence based coping mechanisms among the youth of Kerala, India
The goal of this study was to examine the effect of the moderators of adaptive and maladaptive humour on stress and on the four levels of defence based coping mechanism amongst the youth of Kerala, India. Four hundred and fifty-three youth between the age of 18 and 40, selected from two different cities of North Kerala, India (Calicut, Malappuram) and Central Kerala, India (Cochin, Trissur), were asked to fill out three questionnaires assessing stress, coping and humour. Pearson's test of product-moment correlation indicated that stress had a positive and moderate statistically significant correlation with the first three levels of defence based coping mechanism (pathological defences, immature defences and neurotic defences). Furthermore, there was a positive and weak statistically significant correlation between stress and level-IV coping (mature defences). When positive and moderate correlation was found for stress with maladaptive humour, no significant correlation was found with adaptive humour. When coping was studied in relationship with humour, a negative and weak statistically significant correlation was found for level-I coping (pathological defences) with adaptive humour, whereas a positive and moderate statistically significant correlation was found with maladaptive humour. Here level-IV coping (mature defences) was found to have a positive and moderate statistically significant relationship with adaptive and maladaptive humour. Moderator analysis showed that maladaptive humour moderated the association between stress and four levels of defence based coping mechanism. The study implied that youth should be trained to use more of mature means of coping and adaptive humour styles in life. Universiti Putra Malaysia Press -
HunterPrey Optimization Algorithm for Optimal Allocation of PV, DSTATCOM, and EVCS in Radial Distribution Systems
This research article instigates a seminal approach for optimizing reactive power in renewable energy sources (RES) and electric vehicles (EVs) coalescing distribution systems, using the innovative HunterPrey Optimization (HPO) algorithm in conjunction with DSTATCOM as a reactive power compensator. The proposed methodology aims to minimize losses, enhance voltage stability, and improve overall system performance by simultaneously optimizing reactive power flows in photovoltaic RES (PV_DG), EV charging stations (EVCS), and DSTATCOMs within the distribution system. Simulations carried on IEEE-33, IEEE-69, and IEEE-118 test bus systems in MATLAB environment demonstrate that the HPO-based approach achieves a 91.47% and 96.61% reduction in real power losses and an improvement in voltage profile with a minimum voltage value of 0.991 and 0.994 p.u. (respectively for IEEE-33 and 69 bus systems), compared to traditional algorithms. These results highlight the lofty performance of the HPO method, effectively addressing the challenges posed by the integration of RES and EVs along with DSTATCOM. 2024 John Wiley & Sons Ltd. -
Hybrid (ND-Co3O4/EG) nanoliquid through a permeable cylinder under homogeneous-heterogeneous reactions and slip effects
Modeling and computations are performed to study the ND-Co3O4/EG hybrid nanoliquid mixed convective flow past a vertical porous cylinder. The flow analysis and formulation are given accounting for slip effects and homogeneous-heterogeneous reaction impacts. The governing complex equations formed with prescribed boundary conditions are simplified into self-similar equations through the use of suitable transformations. The numerical solutions of the drag coefficient, Nusselt number, liquid velocity, liquid temperature, and the liquid concentration are explored through graphs with the setting of pertaining parameter values. From the results, it is noticed that an ND-Co3O4/EG nanofluid plays a more impressive role in the process of energy transfer than a Co3O4/EG nanofluid. Further, it is found that the heterogeneous reaction parameter decreases the concentration whereas multiple slips enhance the temperature. 2020, Akadiai Kiad Budapest, Hungary. -
Hybrid AODV: An Efficient Routing Protocol for Manet Using MFR and Firefly Optimization Technique
A MANET is a category of ad hoc protocol that could vary positions and track itself on the flutter. It utilizes wireless connections that are attached to several networks. They include wirelessly in a self-configured, self-healing network while not having permanent communication linked in a collection of mobile networks. The network topology of nodes typically varies in MANET, and nodes are free to stir errantly and independently as a router as they accelerate traffic to more nodes within the network. Ad hoc on-demand distance vector (AODV) was employed for node selection to attain the shortest path strategy in existing techniques. In the proposed system, the hybrid AODV (HAODV) technique incorporates the MFR (Most Forward within Radius) technique to detect the shortest path routing algorithm. The MFR method was deployed for selecting the neighbor node, while HAODV was deployed to find the shortest path. To find the shortest path based on the updating equation, the Firefly algorithm is also implemented into the Hybrid AODV. The proposed work's performance is calculated by different network parameters like the end to end delay, average routing overhead, throughput, and packet delivery ratio. After comparing AODV and DSR algorithms, the proposed algorithm (HAODV) shows improvement in packet delivery ratio, end-To-end delay, Routing overhead, and throughput. 2021 World Scientific Publishing Company. -
Hybrid Approach to Document Anomaly Detection: An Application to Facilitate RPA in Title Insurance
Anomaly detection (AD) is an important aspect of various domains and title insurance (TI) is no exception. Robotic process automation (RPA) is taking over manual tasks in TI business processes, but it has its limitations without the support of artificial intelligence (AI) and machine learning (ML). With increasing data dimensionality and in composite population scenarios, the complexity of detecting anomalies increases and AD in automated document management systems (ADMS) is the least explored domain. Deep learning, being the fastest maturing technology can be combined along with traditional anomaly detectors to facilitate and improve the RPAs in TI. We present a hybrid model for AD, using autoencoders (AE) and a one-class support vector machine (OSVM). In the present study, OSVM receives input features representing real-time documents from the TI business, orchestrated and with dimensions reduced by AE. The results obtained from multiple experiments are comparable with traditional methods and within a business acceptable range, regarding accuracy and performance. 2020, Institute of Automation, Chinese Academy of Sciences and Springer-Verlag GmbH Germany, part of Springer Nature. -
Hybrid approach: Naive bayes and sentiment VADER for analyzing sentiment of mobile unboxing video comments
Revolution in social media has attracted the users towards video sharing sites like YouTube. It is the most popular social media site where people view, share and interact by commenting on the videos. There are various types of videos that are shared by the users like songs, movie trailers, news, entertainment etc. Nowadays the most trending videos is the unboxing videos and in particular unboxing of mobile phones which gets more views, likes/dislikes and comments. Analyzing the comments of the mobile unboxing videos provides the opinion of the viewers towards the mobile phone. Studying the sentiment expressed in these comments show if the mobile phone is getting positive or negative feedback. A Hybrid approach combining the lexicon approach Sentiment VADER and machine learning algorithm Naive Bayes is applied on the comments to predict the sentiment. Sentiment VADER has a good impact on the Naive Bayes classifier in predicting the sentiment of the comment. The classifier achieves an accuracy of 79.78% and F1 score of 83.72%. Copyright 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Hybrid architecture of digital filter for multi-standard transceivers
This paper addresses on three different architectures of digital decimation filter design of a multi-standard RF transceivers. Instead of using single stage decimation filter network, the filters are implemented in multiple stages using FPGA to optimize the area and power. The proposed decimation filter architectures reflect the considerable reduction in area & power consumption without degradation of performance. First, the types of decimation filter architectures are tested and implemented using conventional binary number system. Then the two different encoding schemes i. e. Canonic Signed Digit (CSD) and Minimum Signed Digit (MSD) are used for filter coefficients and then the architecture performances are tested using FPGA. The results of CSD and MSD based architectures show a considerable reduction in the area & power against the conventional number system based filter design implementation. The implementation results reflect that considerable reduction in area of 25. 64% and power reduction of 16. 45% are achieved using hybrid architecture. Research India Publications. -
Hybrid architecture of Multiwalled carbon nanotubes/nickel sulphide/polypyrrole electrodes for supercapacitor
A hybrid electrode structure consisting of amino functionalised multi-walled carbon nanotube, nickel sulphide, and polypyrrole is successfully synthesized using a two-step synthesis such as hydrothermal and in-situ polymerization method. The resulting MWCNT/NiS/PPy composite exhibits a distinct tube-in-tube morphology with excellent stratification. The combination of different components and the unique structure of the composite contribute to its impressive specific capacitance of 1755 F g?1 at 3 A g?1. The prepared ternary composite enables ample exposure of numerous active sites while improving structural stability, ultimately leading to enhanced energy storage capabilities. They do this by combining the advantages of constituent components, a hierarchical assembly approach, and an integrated composite structure. Furthermore, even after undergoing 10,000 charge-discharge cycles, the supercapacitor retains more than 97% of columbic efficiency. An asymmetric coin cell was fabricated using MWCNT/NiS/PPy//AC device which delivered an energy density and power density of 33.12 Wh Kg?1 and 6750 W kg?1 respectively. These findings highlight the exceptional potential of the fabricated device for future applications in hybrid energy storage systems. 2024 Elsevier Ltd