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Efficient handwritten character recognition of modi script using wavelet transform and svd
MODI script has historical importance as it was used for writing the Marathi language, until 1950. Due to the complex nature of the script, the character recognition of MODI script is still in infancy. The implementation of more efficient methods at the various stages of the character recognition process will increase the accuracy of the process. In this paper, we present a hybrid method called WT-SVD (Wavelet Transform-Singular Value Decomposition), for the character recognition of MODI script. The WT-SVD method is a combination of singular value decomposition and wavelet transform, which is used for the feature extraction. Euclidean distance method is used for the classification. The experiment is conducted using Symlets and Biorthogonal wavelets, and the results are compared. The method using Biorthogonal wavelet feature extraction achieved the highest accuracy The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Offline Character Recognition of Handwritten MODI Script Using Wavelet Transform and Decision Tree Classifier
MODI script is derived from the N?gari family of scripts, and it was used for writing Marathi until twentieth century. Though currently not used as an official script, it has historical importance, as a large volume of manuscripts are preserved at various libraries across India. With the use of an appropriate recognition system, the handwritten documents can be transferred into digital media, so that it can be conveniently viewed, edited, or transliterated to other scripts. The research on MODI script is still in the initial stages, and there is a considerable demand for more research in this field. An implementation of wavelet transform-based feature extraction for MODI scripts character recognition is discussed in this paper. The experiment is performed using Daubechies, Haar, and Symlet wavelets, and performance comparison between these different mother wavelets is carried out. Decision tree classifier is used for the classification process, and the results indicate that the feature extraction using Daubechies wavelet yielded better character recognition result. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Character Recognition of MODI Script Using Distance Classifier Algorithms
Machine simulation of human reading is an active research area since the introduction of digital computers. Optical character recognition aims at the recognition of printed or handwritten text from document images and converting the same into a machine-readable form. The focus of this work is handwritten character recognition of MODI Script. A proper recognition system for handwritten documents enables it to be conveniently viewed, edited, and shared via electronic means. The development of a character recognition system for some of the ancient script is still a challenging task due to the complex nature of the script. MODI script is one such script which is the shorthand form of the Devanagari script in which Marathi was written. Though at present MODI script is not an official script, there exists a huge collection of MODI documents in various libraries. In addition, it is observed that scholars and historians are taking serious effort to revive the script. The purposed study based on the implementation of two algorithms for the classification of handwritten MODI script. The algorithms use distance classifier method. The first experiment is done using Euclidean distance classifiers and the second one is with Manhattan distance classifier and the accuracy achieved is 99.28% & 94% respectively. Springer Nature Singapore Pte Ltd 2020. -
The Efficacy of Multi-Component Intervention for Adolescents with Problematic Video Gaming in a Community-Based Setting
Video gaming is a popular leisure activity enjoyed by millions globally, helping with socialisation, interaction, and relieving stress. It may also become a maladaptive coping mechanism to evade distress and negative emotions, leading to problematic usage. Research evidence shows that problematic gaming is associated with different psychosocial issues. Video games can be a way of negative coping and escaping reality, and problematic usage can hide other problems of players in real life. Adolescents are vulnerable to problematic use due to their developmental stages, and those with specific vulnerabilities and disabilities are at greater risk. No one psychotherapy has all the answers, and the multi-component intervention technique might have better treatment utility than a solitary behaviour intervention. The research aims to show the effectiveness of the intervention for problematic video game usage in a community-based setting. The study focuses on adolescents in seventh through ninth grade who were identified as problematic video gamers (not addictive users) from a selected group of schools in Kerala. The study employed an experimental design, encompassing both intervention and control groups, to systematically assess the effects of the experimental manipulation and establish a baseline measurement. The paired t-test results showed no significant decrease in the intervention groups Gaming Addiction Scale at the post-test, but it did lower the addiction scores. By conducting the research, we provide psychological care for adolescents and help them identify and prevent problematic gaming experiences. The research underscores the significance of early identification and prevention of problematic video game usage among adolescents, advocating for a holistic approach incorporating diverse components. 2024 selection and editorial matter, Dr. Sundeep Katevarapu, Dr. Anand Pratap Singh, Dr. Priyanka Tiwari, Ms. Akriti Varshney, Ms. Priya Lanka, Ms. Aankur Pradhan, Dr. Neeraj Panwar, Dr. Kumud Sapru Wangnue; individual chapters, the contributors. -
COOPERATIVE FEDERALISM IN A MULTINATIONAL COUNTRY: Examining the Case of Pakistan
Pakistan, as a multilingual and multiethnic country, has had to deal with issues of ethnic conflict and separatism. Cooperative federalism is used as a device by countries across the world to accommodate and manage the immense diversities they possess. This chapter examines the need for cooperative federalism in a multinational country like Pakistan to strengthen its federal model, ensuring that ethnic groups in the country do not feel insecure and alienated from the union, demanding secession. Beyond national security concerns, cooperative federalism in Pakistan will ensure economic security, human rights, social security, effective policymaking and much more, which form the basis of a welfare state. 2024 selection and editorial matter, M.J. Vinod, Stefy V Joseph, Joseph Chacko Chennatuserry and Dimitris N. Chryssochoou; individual chapters, the contributors. -
Nifty index: Integrating deep learning models for future predictions and investments
The Indian stock market, led by the NSE and BSE, has witnessed remarkable growth, exemplified by the NIFTY 50 index surpassing INR 176 trillion in market capitalization. Post the transformative New Economic Policy reforms in 1991, the market underwent significant expansion due to increased accessibility. This chapter focuses on predicting Nifty index prices for the upcoming 10-day period, aiming to provide valuable insights for investment decisions. Despite the markets inherent complexity, exacerbated by various factors like economic conditions and investor sentiment, the objective of the research study is clear: to boost profitability, mitigate risk, and safeguard traders capital. Leveraging Long Short-Term Memory (LSTM) and Vector Autoregression (VAR) models, the research study rigorously evaluates prediction accuracy using the Root Mean Square Error (RMSE) metric. The study underscores the potential of deep learning techniques in achieving reasonable accuracy, especially for short-term forecasts, while acknowledging the markets inherent unpredictability. Notably, the findings demonstrate that the LSTM model excels in predicting Nifty Bank prices, with an impressive RMSE score of 242.55 compared to VAR models. Furthermore, optimal data splitting, at an 8:2 ratio, significantly enhances prediction accuracy across all models, emphasizing the critical role of high-quality data in training. In conclusion, this study unequivocally recommends LSTM as the preferred model for Nifty index price prediction, providing practitioners with a robust tool to navigate the complexities of the Indian stock market with enhanced precision and confidence. 2025 selection and editorial matter, Vivek S. Sharma, Shubham Mahajan, Anand Nayyar and Amit Kant Pandit; individual chapters, the contributors. -
EFFECTIVENESS OF COGNITIVE BEHAVIOURAL THERAPY FOR ADULTS WITH DEPRESSION AND ANXIETY DURING COVID-19: A Systematic Review of Randomised Controlled Trials
Introduction: The COVID-19 pandemic has forced the administration of Cognitive Behavioural Therapy (CBT) either face-to-face or online. This systematic review aims to assess the effectiveness of CBT and Internet-Delivered CBT (iCBT) in treating depression and anxiety disorders during the COVID-19 outbreak. Methods: Three independent reviewers searched the Web of Science, PubMed, Cochrane Library, and Clinical Trial Databases using specific search phrases. PubMed searches included Cognitive Behavioural Therapy/Intervention and COVID-19 and 2019 Coronavirus Disease or 2019-nCoV, internet-administered/internet-based cognitive behavioural therapy, CBT, cognitive behavioural treatment. Two independent reviewers evaluated the risk of bias at the study level, with disagreements settled through discussion with other research team members. The study findings were reported as per the PRISMA guidelines. Results: Thirty-one studies met the inclusion criteria, and 17 were randomised controlled trials. The studies demonstrated that CBT and iCBT effectively treated depression and anxiety disorders during the COVID-19 pandemic. However, a hybrid CBT modality was more beneficial from a long-term perspective. Conclusion: The findings suggest that CBT and iCBT effectively treat depression and anxiety disorders during the COVID-19 pandemic. However, further research is needed to establish these interventions long-term effectiveness and identify the optimal mode of delivery for different populations. 2024 selection and editorial matter, Dr Rajesh Verma, Dr Uzaina, Dr Tushar Singh, Dr Gyanesh Kumar Tiwari, and Prof Leister Sam Sudheer Manickam. -
Unraveling the complexity of thyroid cancer prediction: A comparative examination of imputation methods and ML algorithms
Despite being relatively rare, thyroid cancer is being identified more often as a result of improved awareness and detection. Even if it has a high survival rate, it is crucial to comprehend its forms, risk factors, and therapies. Better results and prompt intervention are made possible by the early detection of thyroid cellular alterations made possible by evolving machine learning (ML) techniques. The USA Cancer Data Access System's Thyroid Cancer Factor Data, gathered from patient questionnaires, are used in this study. Missing values and imbalance in the dataset are addressed using resampling techniques (SMOTE, under-sampling) and imputation techniques (Median, KNN). To increase the accuracy of thyroid cancer prediction and improve early identification and prognoses for improved patient care, a comparative analysis of machine learning algorithms (ML) (Logistic Regression, LDA, KNN, Decision Tree, SVM, Naive Bayes) with imputation and resampling techniques is being conducted. 2024, IGI Global. All rights reserved. -
AN ANALYSIS OF PERCEPTION AND AWARENESS OF UNDERGRADUATE YOUTH TOWARDS CYBERCRIME
The perception of a situation or reality determines how one responds and awareness is the first step towards understanding, knowing or recognizing it. The majority of the public and the police may be familiar with the phrase cybercrime, but all of the mare fully informed ofthe nature and scope of these crimes, as well as of the cybercriminals and cyber victims, which has an impact on how they see these issues. This studys main goal was to examine the perception and awareness of cybercrime among undergraduate youth studying in BBA or BCA courses. In this study, we discovered that young peoples responses to cybercrime mostly depend on their perceptions of it and their awareness level. To accomplish the studys objective, a thorough examination of existing literature was undertaken. Primary data of200 students were collected through Google Forms. Percentile analysis, correlation analysis and t-test are done to test the hypotheses. The results of this study may help college administrators better comprehend the mind set of todays youth as they develop laws and policies aimed at reducing cybercrime among students. The results of this study show that the youngsters surveyed have high levels of awareness and a good perception. 2024 Kiran Joshi and Priyanka Kaushik. -
Algae-Based Nanoparticles for Contaminated Environs Nanoremediation
Currently, the rapidly growing human interference has increased the percentage of pollutants that include organic and inorganic and this has been threatening the ecosystems. Remediation by conventional physicochemical methods, bioremediation has gained immense acceptance due to their ecofriendly, economical, and sustainable approach. Microbial-based nanoparticles act as facilitators in remediating contaminants by microbial growth and immobilization of remediating agents, by inducing microbial remediating enzymes or enhanced biosurfactants that helps to improve solubility of hydrophobic hydrocarbons to create a conducive milieu for remediation. Algal-NPs can be produced easily using low-cost medium and simple scaling up process which is economically feasible. Silver nanoparticles (AgNPs) and gold nanoparticles (AuNPs) have been synthesized using Nannochloropsis sps (NN) and Chlorella vulgaris (CV), while, brown seaweeds Petalonia fascia, Colpomenia sinuosa, and Padina pavonica were used with iron oxide NPs along with their aqueous extracts. These applications have shown to be promising alternative bioremediating methods that are safe. Algal-based NPs can act as a pollution abatement device that can help to effectively target the pollutants for efficient nanobioremediation and helps to promote environmental clean-up for eliminating heavy metals, dyes, and other organic and inorganic waste from the environment. 2025 by Apple Academic Press, Inc. -
An advanced machine learning framework for cybersecurity
The world is turning out to be progressively digitalized raising security concerns and the urgent requirement for strong and propelled security innovations and procedures to battle the expanding complex nature of digital assaults. This paper examines how AI is being utilized in digital security in both resistance and offense exercises, remembering exchanges for digital attacks focused on AI models. Digital security is the assortment of approaches, systems, advancements, and procedures that work together to ensure the confidentiality, trustworthiness, and accessibility of processing assets, systems, programming projects, and information from attacks. Machine learning-based examination for cybersecurity is the following rising pattern in digital security, planned for mining security information to reveal progressed focused on digital threats and limiting the operational overheads of keeping up static relationship rules. In this paper, we are mainly focusing on the detection and diagnosis of various cyber threats based on machine learning. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. -
Women in tourism: Gender bias and constraints
Tourism could be spending time away from home in search of leisure, relaxation, and pleasure while utilizing the commercial provision of services. The capacity of tourism to improve livelihoods by re- ducing poverty, guarantee or enhance environmental sustainability, and promote gender equality and women's empowerment are some of its strongestpoints. "Women's empowerment" can mean a variety of things, such as respecting women's opinion, making an effort to find them, and improving women's status through training, awareness, and education. They may have the chance to redefine gender norms and other similar roles, giving them more flexibility to pursue their objectives. The main aim of the chapter is to identify the major challenges faced by women in the tourism sector, the ways to overcome constraints, gender stereotypes, and the social stigmas while they juggle between multiple responsibili- ties. With this chapter, the authors aim to gain more knowledge more specifically on how women can be empowered in the tourism and hospitality sector. 2023, IGI Global. All rights reserved. -
A Smart Internet of Things (IoT) Enabled Agricultural Farming System
Industry 4.0 has brought about a profound revolution in recent times. This advancement profoundly impacted technology usage in every aspect and has significantly improved businesses. Agriculture is one of the evergreen economic contributors to Indias GDP. With improvements in adaptability in this sector, the time is ripe for instituting IoT (Internet of Things)-based smart agriculture. Water scarcity and drastic climate change are real issues affecting crop yields, leading to the failure in the timely fulfillment of market demand (Nawandar 2019). The authors have collaborated to address these concerns by creating a system comprising a functional hardware prototype and an android application for regulating irrigation and temperature. The introduction of IoT (Internet of Things) automates crop monitoring and reduces labor costs. By using IoT, (Internet of Things) an earmarked agricultural field is covered with sensors. The sensors are concealed so as not to be affected by the bleakness of the external environment. These sensors work in tandem with drip irrigation following the sensed climatic conditions. The water is pumped directly to the root zone in an optimally sensed manner. The authors developed and tested the system successfully in a greenhouse system. The process initially aims to extract the values of soil parameters by using IoT (Internet of Things) sensors and appropriately control the watering of crops, thus enabling the cultivation of crops even in a hot and dry climate. Crops can be irrigated from a remote location and their temperature can be meticulously regulated to ensure they remain within an optimal range. Water utilization for agricultural crops is optimized with the use of automated irrigation systems that use W.S.N (Wireless - Sensor-Networks) and G.P.R.S (General-Packet-Radio-Service) modules. The algorithm employed in the system to control water usage is based on the needs of the crop and the terrain. The entire system is powered by photovoltaic panels, which are useful in rural and isolated areas without electricity (Raut and Shere 2014). A cellular network is used for duplex communication. Continuous monitoring and irrigation schedule programming are used by web apps to manage irrigation. This is also possible using a browser and web pages. A system with three identical automatic irrigation systems can save water use by up to 90%. 2024 by Nova Science Publishers, Inc. All rights reserved. -
Innovative constraints formulation in timetable planning for efficient resource allocation in academic institutions
Many developing nations still rely on manual timetable scheduling in academic institutions, leading to inefficiencies. However, advancements in technology have introduced software solutions such as FET (free evolutionary timetabling) to automate the process. In a study, the authors successfully implemented FET to automate timetable creation at a university, reducing the time required from days to seconds. Scheduling in universities with elective courses poses challenges, includingavailable rooms, faculty availability, and guest lecturers. The authors propose a unique timetable generation process that considers post-pandemic social distancing measures. This process addresses various complex constraints faced by academic institutions and holds potential for reopening institutions in a cautious manner following the pandemic. 2023, IGI Global. All rights reserved. -
Value addition to international students' exchange programs through engagement in services
Social responsibility has been an emerging concept in Higher Educational Institutions in India. Promoting social responsibility through international students' exchange programs helps students' capacity to improve their cultural, social and service knowledge to bring about sustainable and meaningful development. This chapter looks at the impact of the interventions of international students in slum communities, especially working with children and women for their academic, health and economic empowerment. This was a qualitative study using a self-structured interview schedule. Data were collected from twenty international students from universities of Norway and the Netherlands who were placed in urban slums for five years and thirty children and women from urban slums of Bangalore who benefitted from this program. A purposive sampling method was used, and the data were analyzed using thematic analysis. This chapter reveals the development of children and women through international students' programs and helps showcase further planning for innovative programs for vulnerable populations. Attitudes of both groups towards cultural differences and the expectation and effectiveness of the exchange program may also be described in this chapter. This chapter intends to help plan international exchange programs from different dimensions benefiting the slum communities for their development and sensitizing cultural differences from different perspectives. 2024 Nova Science Publishers, Inc. -
Neuroleadership strategies: Elevating motivation and engagement among employees
In the ever-evolving landscape of the modern era, organizations face the ongoing challenge of maintaining motivated and engaged employees. Despite the substantial body of research on this topic, many organizations still struggle to effectively promote engagement and motivation among their employees. This research aims to investigate the application of neuroleadership strategies in addressing this issue. The SCARF model, based on neuroscience principles, provides a valuable framework for understanding neuroleadership strategies which address social and emotional triggers that impact engagement and motivation. It can be effectively used to drive motivation and engagement in the workplace by addressing the fundamental social and emotional needs of employees. This study employs a quantitative approach which assesses the 321 employees from different organizations in India. The results of the study would provide leaders with practical insights to boost motivation and engagement in organizations and thereby improve the effectiveness of the organization. 2024, IGI Global. All rights reserved. -
Transforming workplace stress: The importance of neuroleadership for building resilient work environment
Workplace stress is a common issue that can significantly impact both employees and employers. This study explores the dynamic intersection of workplace stress and the emerging field of neuroleadership, offering insights into fostering resilient work environments. Drawing on the principles of neuroleadership, the chapter highlights how an understanding of neuroscience can inform leadership practices and contribute to creating resilient workplaces. This chapter discusses the neurological basis of stress and the role of leaders in mitigating its effects. It explores emotional intelligence in leadership and the impact of organisational culture on stress resilience. The chapter suggests practical interventions like mindfulness practices and supportive work environment initiatives grounded in neuroscience to cultivate a culture of wellbeing. By adopting resilient leadership strategies and understanding the neuroscience of stress, organisations can create environments that promote employee wellbeing and navigate the challenges of the modern workplace. 2024, IGI Global. All rights reserved. -
Detection of cyber crime based on facial pattern enhancement using machine learning and image processing techniques
Cybercrime has several antecedents, including the rapid expansion of the internet and the wide variety of users around the world. It is now possible to use this data for a variety of purposes, whether for profit, non-profit, or purely for the benefit of the individual. As a result, tracing and detecting online acts of terrorism requires the development of a sound technique. Detection and prevention of cybercrime has been the subject of numerous studies and investigations throughout the years. An effective criminal detection system based on face recognition has been developed to prevent this from happening. Principle component analysis (PCA) and linear discriminant analysis (LDA) algorithms can be used to identify criminals based on facial recognition data. Quality, illumination, and vision are all factors that affect the efficiency of the system. The goal of this chapter is to improve accuracy in the facial recognition process for criminal identification over currently used conventional methods. Using proposed hybrid model, we can get the accuracy of 99.9.5%. 2022, IGI Global. All rights reserved. -
A Novel Auto Encoder- Network- Based Ensemble Technique for Sentiment Analysis Using Tweets on COVID- 19 Data
The advances in digitalization have resulted in social media sites like Twitter and Facebook becoming very popular. People are able to express their opinions on any subject matter freely across the social media networking sites. Sentiment analysis, also termed emotion artificial intelligence or opinion mining, can be considered a technique for analyzing the mood of the general public on any subject matter. Twitter sentiment analysis can be carried out by considering tweets on any subject matter. The objective of this research is to implement a novel algorithm to classify the tweets as positive or negative, based on machine learning, deep learning, the nature inspired algorithm and artificial neural networks. The proposed novel algorithm is an ensemble of the decision tree algorithm, gradient boosting, Logistic Regression and a genetic algorithm based on the auto-encoder technique. The dataset under consideration is tweets on COVID-19 in May 2021. 2024 Taylor & Francis Group, LLC. -
Generative AI and its impact on creative thinking abilities in higher education institutions
Generative AI technologies such as ChatGPT have started gaining increased popularity among higher education institutions. Students, as well as teaching professionals, can utilize these tools for various academic purposes due to the immense benefits they provide by way of customization of data generated and ease of access to data. However, this chapter seeks to analyze how such tools may impact students' creative thinking ability. It also analyses the drawbacks faced by teachers after implementation of such tools. The methodology adopted for the study was two surveys: one administered to gather students' opinions and the other for understanding teachers' perspectives. The analysis of the data collected shows that the over-reliance of students on such generative AI tools might hinder students' ability to think creatively to some extent. The chapter also suggests some of the strategies that can be adopted by teachers to ensure students' capabilities are assessed accurately. 2024, IGI Global. All rights reserved.