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A Reflection on the Current Status of Animal-Assisted Therapy in India
The field of animal-assisted therapy is advancing quickly throughout the world gaining popularity as a complementary therapy. Several countries, especially in the East, are still in their nascent phase in utilizing animal-assisted therapy and a more realistic presentation of their status should drive them towards effective initiatives to promote the field. The primary objective of this paper is to throw light on the current scenario of animal-assisted therapy in India. The relevant databases such as Scopus, Google Scholar, Proquest, PubMed, and JSTOR were searched to identify the research literature. The organizational websites, news, and blog articles, as well as institutional repositories, were explored to maximize the evidence. A total of 24 articles were found which included published research articles as well as unpublished conference papers. Results found a dearth of practice and research throughout the country indicating an urgent need to direct steps in promoting the growth of the field. The contemporary issues in the implementation of animal-assisted therapy such as cultural and religious beliefs, lack of awareness, lack of practising organizations and therapists warrant immediate attention. Reducing the research and practice gap alongside focusing on creating awareness, changing public perception, introducing coursework in educational institutions, the publication of evidence-based research will help in the acceptance and growth of this novel therapeutic field. 2021, The Author(s), under exclusive licence to Springer Nature Switzerland AG. -
Animal-Assisted Therapy for the Promotion of Social Competence: a Conceptual Framework
Developmental disorders have a substantial effect on the social competence of children affecting their overall psychosocial functioning. Social competence entails the process of being socially mature by establishing stable and adaptive patterns of social behavior. Animal-assisted therapy, as an alternative treatment modality, has offered some new prospects for improving social cognition. This conceptual paper, thus, attempts to throw light on how animal-assisted therapy can help improve social competence. The paper draws its knowledge from the existing theories and empirical work done to propose a conceptual framework that can enhance social competence by incorporating therapy animals. It can be concluded that animal-assisted therapy has found to improve different dimensions crucial for development of social competence. This further suggests the dire need to explore the effectiveness of human-animal interactions by utilizing it for improving social competence. 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG. -
Animal-assisted therapy for children and adolescents with neurodevelopmental disorders: A review
The increase in neurodevelopmental disorders presents the need for complementary and alternative treatment modalities to support well-being in the maximum possible way. This narrative review was conducted with the aim to explore how animal-assisted therapy as a complementary treatment approach is beneficial for children and adolescents with neurodevelopmental disorders. A search in various databases was conducted to identify articles published in the field of animal-assisted interventions. The review comprised of a total of 32 studies. The discussion of the results was presented in terms of different therapy animals incorporated into the therapeutic environment. The review indicated that animal-assisted therapy has the potential to improve symptoms and various psycho-social variables in individuals suffering from different developmental disabilities. 2024, IGI Global. All rights reserved. -
Canine-assisted Therapy in Neurodevelopmental Disorders: A Scoping Review
Introduction: Animal-Assisted Therapy has been advocated to benefit individuals with neurodevelopmental disorders. Among all the various kinds of animals used in the therapy, dogs are the most utilized because of their temperament and accessibility. Methods: This systematic scoping review was carried out to present the existing literature employing canine-assisted therapy in the diverse population of neurodevelopmental disorders. The study used the Arksey and O'Malley framework for scoping reviews. Several databases including the gray literature were searched for publications on animal-assisted therapy. Results: The search yielded 4898 articles of which 41 articles were eliigible for inclusion into the review. Conclusions: Scrutiny of the articles suggested a dearth of studies in the various sub-diagnostic categoriesfor neurodevelopmental disorders along with a lack of focus on adult populations with this diagnosis. In addition, the critical need for standardization of therapy guidelines and promotion of animal welfare is reaffirmed. 2022 Elsevier GmbH -
Efficacy of Canine - Assisted Therapy on Social Competence among INdividuals with Autism Spectrum Disorder
Adults with Autism Spectrum Disorder (ASD), because of their deficits in social newlinecompetence, often suffer a great deal in the community. Canine-assisted therapy (CAT) seems to be a useful approach to improve social functioning. This study aimed to investigate whether CAT can assist in improving the social competence of adults newlinewith ASD. The study employed an ABAB single case experimental design with four newlineparticipants. The social performance and social interactions with the therapy dog and newlinetherapist were the target measures of the study, and they were assessed using the newlineVellore Assessment of Social Performance and Animal-assisted Therapy Flowsheet. The baseline measures for the study were taken four times for 4 weeks for two newlinebaseline/reversal phases of the study. CAT was delivered by a trained dog along with newlinea certified animal-assisted practitioner twice a week for 4 weeks for 45-60 minutes newlineduring two intervention phases. The results were interpreted using descriptive, newlinegraphical, and numerical analysis. The mean scores indicated improvement in social newlineperformance and social interaction scores in the intervention phases. The visual newlineinspection showed similar results as indicated by the increasing trend line in newlineintervention phases. The results of the non-overlap of all pairs showed a medium to newlinestrong effect of CAT on improving social performance. The results validated the use newlineof CAT in the enhancement of social competence among adults with ASD. The study newlinesignificantly contributed to the field of CAT as well as has implications for aiding the newlineintegration of adults with ASD into mainstream society by enhancing their social newlinefunctioning. -
Cries of war: Securitization of fluid transnational identities during war (a comparative study of securitization of Chinese Indians and Japanese Americans)
Fluid transnational identities are an omnipresent reality in the contemporary world, but what happens when war becomes a reality or the threat of war is imminent in a State which contains fluid transnational identities? This article tries to explore these dynamics to determine if the threat from transnational identities is an actual threat during war or an act of an elite few, and what follows after the war, by comparing the experiences of Chinese Indians and Japanese Americans. The study heavily leans on securitization theory to explore the questions posed and elaborate on the situations when habeas corpus was denied thereby incarceration and internment as a practice were justified. The relationship between the transnational population and the State under the Copenhagen School is also further elaborated on. The Author(s) 2021. -
Brain Tumor Detection using Hyper Parameter Tuning and Transfer Learning
Brain Tumor is the development of abnormal cells in our brain. There are cancerous and noncancerous brain tumors. Because they can press against healthy brain tissue or spread there, brain tumors are harmful. The early diagnosis of brain tumors is a highly challenging assignment for radiologists. The typical size of a brain tumor doubles in just twenty-five days due to its rapid growth. If not properly cared for, the patient's survival rate typically does not exceed six months. It may quickly result in death. For the purpose of early brain tumor identification, an automatic method is necessary. In this study, an automated strategy is suggested for quickly distinguishing between malignant and non-cancerous brain images. Most of the time, it can be treated if caught during the early stages. Hence the need for more and improved brain tumor detection. The most crucial part here is image processing. The medical images obtained during the test have to be appropriately analysed. Various methods such as MobileNet, EfficientNetB7, and EfficientNetV2 have been used and their efficiency has been analysed. Here we classify the dataset containing 300 images into two. The suggested system will offer improved clinical support for the field of medicine. 2023 IEEE. -
Transformative Insights: Unveiling the Potential of Artificial Intelligence in the Treatment of Sleep Disorders - A Comprehensive Review
Disruptions to sleep have a substantial influence on people's overall health and quality of life. The conventional techniques for diagnosing and managing sleep disorders usually rely on subjective assessments and qualitative evaluations, that may have some accuracy and efficacy limitations. Nevertheless, recent developments in the field of artificial intelligence (AI) have presented new opportunities for better diagnosing and treating problems with insomnia. The paper reviews in depth the uses of AI in the domain of medical sleep medicine. We look at the use of algorithmic techniques for deep learning and machine learning for identifying indicators of sleep-related issues, the assessment of sleep quality, sleep tracking, and the establishment of individualized sleep therapeutics. We also discuss how AI is being used to construct forecasting models that may be used to identify individuals who are at risk of experiencing sleep issues and improve treatment strategies. In addition, we talk about the challenges and potential outcomes of incorporating AI-based techniques into clinical practice. Overall, our research highlights how AI has the potential to transform the field of sleeping medicine and improve outcomes for people with sleep-related conditions. 2023 IEEE. -
Detection of toxic comments over the internet using deep learning methods
People now share their ideas on a wide range of topics on social media, which has become an integral part of contemporary culture. The majority of people are increasingly turning to social media as a necessity, and there are numerous incidents of social media addiction that have been reported. Socialmedia channels. Socialmedia platforms have established their worth over time by bringing individuals from different backgrounds together, but they have also shown harmful side effects that could have serious consequences. One such unfavourable result is how extremely poisonous many discussions on social media are. Online abuse, hate speech, and occasionally outrage culture are now all considered to be toxic. In this study, we leverage the Transformers Bidirectional Encoder Representations to build an efficient model to detect and classify toxicity in user-generated content on social media. The Kaggle dataset with labelled toxic comments, was used to refine the BERT pre-trained model. Other Deep learning models, including Bidirectional LSTM, Bidirectional-LSTM with attention, and a few other models, were also tested to see which performed best in this classification task. We further evaluate the proposed models utilising dataset obtained from Twitter in order to find harmful content (tweets) using relevant hashtags. The findings showed how well the suggested methodology classified and analysed toxic comments. 2024 selection and editorial matter, Arvind Dagur, Karan Singh, Pawan Singh Mehra & Dhirendra Kumar Shukla; individual chapters, the contributors. -
Detection of toxic comments over the internet using deep learning methods
People now share their ideas on a wide range of topics on social media, which has become an integral part of contemporary culture. The majority of people are increasingly turning to social media as a necessity, and there are numerous incidents of social media addiction that have been reported. Socialmedia channels. Socialmedia platforms have established their worth over time by bringing individuals from different backgrounds together, but they have also shown harmful side effects that could have serious consequences. One such unfavourable result is how extremely poisonous many discussions on social media are. Online abuse, hate speech, and occasionally outrage culture are now all considered to be toxic. In this study, we leverage the Transformers Bidirectional Encoder Representations to build an efficient model to detect and classify toxicity in user-generated content on social media. The Kaggle dataset with labelled toxic comments, was used to refine the BERT pre-trained model. Other Deep learning models, including Bidirectional LSTM, Bidirectional-LSTM with attention, and a few other models, were also tested to see which performed best in this classification task. We further evaluate the proposed models utilising dataset obtained from Twitter in order to find harmful content (tweets) using relevant hashtags. The findings showed how well the suggested methodology classified and analysed toxic comments. 2024 The Author(s). -
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. -
Literary Cartography of Performance Ecologies in Sheela Tomys Valli
The shift towards posthumanism is characterized by blurring boundaries between humans and other species alongside emerging narratives centred on climate catastrophes and ecological crises. Sheela Tomys Valli (2022) is one of the most recent works of Indian fiction that actively promotes ecological consciousness. Set against the picturesque landscape of Wayanad, Valli intricately captures the essence of the indigenous community, weaving their stories into its narrative. The paper suggests that reading Valli through a cartographic lens transforms the narrative into an intelligent discourse on spatial politics. The performances in Valli are understood through the lens of performance ecology (Jeff Grygny), reflecting ongoing contemporary ecological debates. Their interrelation is explored by mapping spatial memory and schema of the characters, based on Robert T. Tallys theory of literary cartography (2013). Additionally, the paper will provide an overview of the ecopolitics of Wayanad, with a specific focus on the socio-political conditions of the Paniyar and Kuruchiyar scheduled tribes from which the characters are drawn. The study will underscore the triad of space, performance, and ecology in Valli, invoking a sense of ecoprecarity essential for rethinking and potentially expanding our notion of sustainability. 2024, University of Malaya. All rights reserved. -
A comprehensive LR model for predicting banks stock performance in Indian stock market
The study focusses on developing a Logistic Regression model to distinguish between Good and Poor Performance of Bank-stocks which are traded in Indian stock market with regard to the financial ratios. The study- sample comprises of financialratios of 40 nationalised and private banks, for a period of six years. The study ascertains and scrutinizes eleven financial ratios that can categorize the Banksbroadly into two categories as good or poor, up to the accuracy level of 78 percent, based on their rate of return. First, the study predicts the performance of banks by using financial ratios and tries to build the goodness of fit by using Logistic Regression approach. The study also emphasizes that this model can enrich an investors ability to forecast the price of various stocks. However, the paper confers the real-world implications of Logistic Regression model to envisage the performance of Banks in the stock market. The study reveals that the model could be useful to potential investors, fund managers, and investment companies to improve their strategies and to select the out-performing Bank-stocks. Serials Publications Pvt. Ltd. -
Intersection of AI and business intelligence in data-driven decision-making
In today's rapidly evolving business landscape, organizations are inundated with vast amounts of data, making it increasingly challenging to extract meaningful insights and make informed decisions. The traditional business intelligence (BI) approach must often address the complexity and speed required for effective decision-making in this data-rich environment. As a result, many businesses need help to leverage their data to drive sustainable growth and remain competitive. Intersection of AI and Business Intelligence in Data-Driven Decision-Making presents a transformative solution to this pressing challenge. By exploring the convergence of artificial intelligence (AI) and BI, our book provides a comprehensive framework for leveraging AI-powered BI to revolutionize data analysis, predictive modeling, and decision-making processes. Readers will gain valuable insights into practical applications, emerging trends, and ethical considerations, inspiring and exciting them about the potential of AI in driving business success. Through in-depth discussions, case studies, and best practices, this book equips professionals, researchers, and students with the knowledge and tools needed to navigate the complexities of AI-powered business intelligence. Whether you're looking to predict trends, analyze consumer behavior, or optimize supply chains, this book offers actionable strategies and techniques for implementing AI-powered BI solutions in your organization. 2024 by IGI Global. All rights reserved. -
Cyber secure man-in-the-middle attack intrusion detection using machine learning algorithms
The main objective of this chapter is to enhance security system in network communication by using machine learning algorithm. Cyber security network attack issues and possible machine learning solutions are also elaborated. The basic network communication component and working principle are also addressed. Cyber security and data analytics are two major pillars in modern technology. Data attackers try to attack network data in the name of man-in-the-middle attack. Machine learning algorithm is providing numerous solutions for this cyber-attack. Application of machine learning algorithm is also discussed in this chapter. The proposed method is to solve man-in-the-middle attack problem by using reinforcement machine learning algorithm. The reinforcement learning is to create virtual agent that should predict cyber-attack based on previous history. This proposed solution is to avoid future cyber middle man attack in network transmission. 2022 by IGI Global. All rights reserved. -
Cyber secure man-in-the- middle attack intrusion detection using machine learning algorithms
The main objective of this chapter is to enhance security system in network communication by using machine learning algorithm. Cyber security network attack issues and possible machine learning solutions are also elaborated. The basic network communication component and working principle are also addressed. Cyber security and data analytics are two major pillars in modern technology. Data attackers try to attack network data in the name of man-in-the-middle attack. Machine learning algorithm is providing numerous solutions for this cyber-attack. Application of machine learning algorithm is also discussed in this chapter. The proposed method is to solve man-in-the-middle attack problem by using reinforcement machine learning algorithm. The reinforcement learning is to create virtual agent that should predict cyber-attack based on previous history. This proposed solution is to avoid future cyber middle man attack in network transmission. 2020, IGI Global. -
Transparent Data Encryption: Comparative Analysis and Performance Evaluation of Oracle Databases
This Transparent Data Encryption (TDE) can provide enormous benefits to the Relational Databases in the aspects of Data Security, Cryptographic Encryption, and Compliances. For every transaction, the stored data must be decrypted before applying the updates as well as should be encrypted before permanently storing back at the storage level. By adding this extra functionality to the database, the general thinking denotes that the Database (DB) going to hit some performance overhead at the CPU and storage level. However, The Oracle Corporation has adversely claimed that their latest Oracle DB version 19c TDE feature can provide significant improvement in the optimization of CPU and no overhead at the storage level for data processing. Impressively, it is true. the results of this paper prove too. Most interestingly the results also revealed about highly impacted components in the servers which are not yet disclosed in any of the previous research work. This paper completely concentrates on CPU, IO, and RAM performance analysis and identifying the bottlenecks along with possible solutions. 2020 IEEE. -
Ensemble Model of Machine Learning for Integrating Risk in Software Effort Estimation
The development of software involves expending a significant quantum of time, effort, cost, and other resources, and effort estimation is an important aspect. Though there are many software estimation models, risks are not adequately considered in the estimation process leading to wide gap between the estimated and actual efforts. Higher the level of accuracy of estimated effort, better would be the compliance of the software project in terms of completion within the budget and schedule. This study has been undertaken to integrate risk in effort estimation process so as to minimize the gap between the estimated and the actual efforts. This is achieved through consideration of risk score as an effort driver in the computation of effort estimates and formulating a machine learning model. It has been identified that risk score reveals feature importance and the predictive model with integration of risk score in the effort estimates indicated an enhanced fit. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Growth of some urinary crystals and studies on inhibitors and promoters. II. X-ray studies and inhibitory or promotery role of some substances
Best conditions were established for the gel growth of three urinary crystals viz., calcium oxalate monohydrate, calcium hydrogen phosphate dihydrate and ammonium magnesium phosphate hexahydrate. The crystals grown were characterized using single crystal X-ray diffraction techniques and density measurements. Crystal growth experiments were carried out by incorporating the extracts or juices of some natural products in the gel media. By carefully observing the changes in the growth of crystals (compared to control experiments carried out at the same conditions), results about the inhibitory or promotery role of the substance incorporated were obtained. It was found that the extracts or juices of many of the naturally occurring substances have interesting inhibitory or promotery effects. These results may have useful applications in the treatment of recurrent stone patients.