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A Systematic Review on Prognosis of Autism Using Machine Learning Techniques
Quality of life (QoL) and QoL predictors have become crucial in the pandemic. Neurological anomalies are at the highest level of QoL threats. Autism is a multisystem disorder that causes behavioural, neurological, cognitive, and physical differences. Recent studies state that neurological disorders can result in dysfunction of the brain or whole nervous system which may cause other symptoms of Autism. The paper focuses on reviewing various Machine Learning techniques used for diagnosing Autism at an early age with the help of multiple datasets. The study of brain Magnetic Resonance Imaging (MRI) provides astute knowledge of brain structure that helps to study any minor to significant changes inside the brain that have emerged due to the disorder. Early diagnosis leads to a healthy life by getting timely treatment and training. "Early diagnosis of autism spectrum disorder" is an objective and one of the prime goals of health establishments worldwide. The research paper aims to systematically review and find which machine learning algorithms are efficient for the prognosis of autism. The Electrochemical Society -
A Systematic Review on the Identification and Classification of Patterns in Microservices
Determining patterns in monolithic systems to help improve the overall system development and maintenance has become quite commonplace. However, recognizing the patterns that have emerged (or are emerging) in cloud computing - especially with respect to microservices, is challenging. Although numerous patterns have been proposed through extensive research and implementation, the quality assessment tools that are currently available fall short when it comes to accurately recognizing patterns in microservices. It has been identified that a completely autonomous tool for the identification and classification of patterns in microservices has not been developed so far. Moreover, classification of services is an approach that has not been considered by researchers that are working in this field. This paper aims to perform a detailed systematic literature review that can help to explore the various possibilities of identifying and classifying the patterns in microservices. The article also briefly lists out a set of tools that is used in the industry for the implementation of patterns in microservices. 2023 IEEE. -
A systematic review on the impact of e-tailing on Indian retail industry
The paper initially focusses on the growth and issues concerning internet retailing in India. The study also aims to explore exhaustively on the growth potential of internet retailers and their prospects in the Indian retail market. The profile of the country's customers is also discussed in detail including their expectations and the complications posed by their demography. The study also intends to analyze the challenges confronting the e-tail players, their hardships, and investments. Using a meta-analysis framework study reviewed articles from national and international journals, newspapers, and books on drivers of e-tail growth in India, E-tail players in India, and Indian E-tail customers. In the results and discussions, more was spoken about the advent and opportunity exposed by the digital wallets to promote internet retailing. In the summary and conclusions investigators talked about the digital learning mania and the dynamic online shopping behavior exercised by the subscribers of e-tailing. 2020 by Advance Scientific Research. This is an open-access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) -
A Systematic Review on Traffic Management System and Security Flaws: Analysis Research
The number of cars on our roads has significantly increased in recent years, surpassing the advancement of our traffic and road infrastructure. Due to the ineffective traffic management caused by this imbalance, there have been noticeable increases in traffic jams, congestion, and pollution. Addressing the problem of growing traffic has become urgent on a global scale. By using cutting-edge technology, Intelligent Transportation Systems (ITSs) provide a viable solution for addressing these issues. This research explores the development of traffic control systems as well as the cutting-edge innovations that have revolutionized this field. Furthermore, it examines various techniques based on traffic signals, primarily focusing on scrutinizing their security vulnerabilities and the measures taken to enhance system performance. The review acknowledges significant strides made in implementing security measures, assessing their effectiveness through both qualitative and quantitative metrics. Additionally, this study delves into key discoveries and explores the rationale behind lessons learned, serving as a roadmap for future research endeavors. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Systematic Study on Unimodal and Multimodal Human Computer Interface for Emotion Recognition
A systematic study for human-computer interface (HCI) for emotion recognition is presented in this paper, with a focus on various methods used to identify and interpret human emotions. It delves into various methods used to identify and interpret human emotions and highlights the limitations of unimodal HCI for emotion recognition systems. The paper emphasizes the benefits of multimodal HCI and how combining different types of data can lead to more accurate results. Additionally, it highlights the importance of using multiple modalities for emotion recognition. The study has significant implications for mental health assessments and interventions as it offers insights into the latest techniques and advancements in emotion recognition. Future research can use these insights to improve the accuracy of emotion recognition systems, ultimately leading to better mental health assessments and interventions. Overall, the paper provides a valuable contribution to the field of HCI and emotion recognition, and it underscores the importance of taking a multimodal approach for this critical area of research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
A Systematic Survey of Happiness from an Analytical Perspective
The paper is a survey paper that talks about studies around happiness. We have surveyed papers about the scales of measuring happiness, in which the scales are proposed, demonstrated and examined. Happiness is affected by various factors, which can be called indicators of happiness. Some of the papers we reviewed validate the significance of such indicators with applications. The indicators include inflation, unemployment, health, loneliness, and sports. Modern technology helps researchers estimate and forecast happiness and effectively find the relation between factors affecting happiness. Researchers use different methodologies to study happiness. The data used in the papers were retrieved from surveys and existing Happiness Report, designed surveys appropriate for the study. Models were proposed for forecasting happiness using Machine Learning and Neural Networks. From the reviews, we identify research gaps in the area for future work. This paper gives an overview of the studies around the area of happiness from an analytical approach. 2022 IEEE. -
A Systemic Review on Omicron Variant of SARS-CoV-2
As the new strains spread around the world, scientists have been trying to learn more about the different strains, especially Omicron, and how SARS-CoV2 acts in general. Studying historical trends of virus spread and the structure of the virus and its strains, as well as all the mechanisms it needs to survive, can help identify the symptoms and diagnose and treat the disease. The research has shown that the new strains, including Omicron, have a higher rate of mutation and transmissibility. Additionally, due to the rapid spread of the virus, there has not been a significant amount of time to understand the severity of the infection. To better understand the novel variants, a detailed analysis of the basic pathophysiology of the virus is needed. This includes transcriptome analysis for the recombination index to identify variation in the strand. This aided in the diagnostic process, and therapeutics for mutants of the virus could be treated. The Omicron strain is particularly threatening due to its rapid transmission rate and its property of immune evasion, which can make it less vulnerable to vaccination. 2023 Biomedical & Pharmacology Journal. -
A TALE FROM GODS OWN COUNTRY: WALKING WOUNDED HILLS AND STRATEGIES TO RESTORE THE PARADISE
This article examines how a destination in crisis recoup after a devastating disaster and how the stakeholders are impacted by the disaster and explores the need of a strong destination governance policy in crisis situation. In the crisis settings the study includes a qualitative approach to understand the stakeholders impact and disaster assessment process that examines various facets of the natural disaster through a structured discussion with the impacted stakeholders. The studys primary focus is on the crisis management strategies adopted by the impacted district, Wayanad; to tackle the challenges that localities face within the devastating situation. This study proposes a framework that could facilitate speedy crisis management responses. The findings indicate the role of governing bodies, in handling crisis management scenario over internal strategies and strategy formulation. The dynamics of stakeholder engagement and tactical perspectives adopted by the state in the given prominence. This study examines how the Governing bodies and practitioners of the local governing authority; interpret the crisis situation and explores the tactical and strategic management initiatives for repositioning and rebranding tourism. The natural calamity has had a significant impact on the Livelihood and Topography of Wayanad, Kerala, India, making it extremely susceptible. This study aims to investigate how community resilience and rehabilitation were impacted by the natural catastrophe and the response of the key stakeholders. Design/ Methodology/ Approach The study analyses the impact of disaster and examine a multi-stakeholder assessment to capture varied dimensions of the disaster through a structured conversation. The research engages a qualitative approach and the data is captured through semi-structured interviews from the local community, tour operators, and destination management companies in Wayanad. A conceptual framework for community resilience competencies is proposed in the study, which may help with quick crisis management responses. The study's sample consists of key stakeholders; including representatives from the tourism industry, including leaders from the community, and local residents. The qualitative findings identify the disaster recovery strategies while, handling crisis management scenarios and examines the resilience mechanism adopted at the disaster struck regions by the stakeholders. The results strongly indicate; an imperative need for a good governance as an archetype for practitioners to lead with tactical and strategic measures to sustain the vulnerabilities. Lack of pre-disaster communications and protocols were missing elements leading to the devastating scenario during the disaster. Mechanisms for boosting tourism was lead well with the help of travel influencers and repositioning the destination image. Along with possible strategies and ideas that can help Wayanad's tourism industry, particularly in the post-disaster phase, this paper indicates strategies to restore the destinations brand identity and reputation. The study supports the "New phase of Normalcy" by including ideas and mitigative measures that the public sector should implement to address safety concerns. The State has well begun with the new reinstating tourism strategy through the new campaign Ente Keralam, Ennum Sundaram (My Kerala Ever Beautiful). 2025 Editura Universitatii din Oradea. All rights reserved. -
A taxation perspective on how domestic double taxation on corporate taxes affects Indian commerce
This paper examines the impact of domestic double taxation on corporate taxation in India after the abolition of the dividend distribution tax (DDT) and the introduction of the new tax rates and rules in 2020. Domestic double taxation occurs when the same income is taxed twice by the same jurisdiction, such as at the corporate and shareholder level. Using data from the Income Tax Department and the Reserve Bank of India, this paper estimates the effective tax rate on corporate income and dividends in India under the current tax system. It compares it with the previous tax system and the international standards. It also analyses the effect of domestic double taxation on corporate financial decisions, such as the dividend payout ratio, the retained earnings, the debt-equity ratio, and the investment rate. It evaluates the effect of domestic double taxation on corporate tax revenue, tax incidence, and tax efficiency. The authors identify that, between 2019 and 2023, corporate income tax revenue in India increased alongside nominal GDP growth, with a notable positive correlation coefficient between the two variables. The empirical analysis technique involves gathering and analyzing quantitative data to assess the real impact of new tax reforms and reduced corporate tax rates. Finally, this study proposes policy recommendations to mitigate the adverse effects of domestic double taxation and improve India's corporate taxation system and GDP. This paper contributes to the literature by providing updated and comprehensive empirical evidence on domestic double taxation and corporate taxation in India and by offering some insights and suggestions for the policymakers, the tax authorities, the corporate sector, and the academic community. 2025 Malque Publishing. All rights reserved. -
A taxation perspective on how domestic double taxation on corporate taxes affects Indian commerce
This paper examines the impact of domestic double taxation on corporate taxation in India after the abolition of the dividend distribution tax (DDT) and the introduction of the new tax rates and rules in 2020. Domestic double taxation occurs when the same income is taxed twice by the same jurisdiction, such as at the corporate and shareholder level. Using data from the Income Tax Department and the Reserve Bank of India, this paper estimates the effective tax rate on corporate income and dividends in India under the current tax system. It compares it with the previous tax system and the international standards. It also analyses the effect of domestic double taxation on corporate financial decisions, such as the dividend payout ratio, the retained earnings, the debt-equity ratio, and the investment rate. It evaluates the effect of domestic double taxation on corporate tax revenue, tax incidence, and tax efficiency. The authors identify that, between 2019 and 2023, corporate income tax revenue in India increased alongside nominal GDP growth, with a notable positive correlation coefficient between the two variables. The empirical analysis technique involves gathering and analyzing quantitative data to assess the real impact of new tax reforms and reduced corporate tax rates. Finally, this study proposes policy recommendations to mitigate the adverse effects of domestic double taxation and improve India's corporate taxation system and GDP. This paper contributes to the literature by providing updated and comprehensive empirical evidence on domestic double taxation and corporate taxation in India and by offering some insights and suggestions for the policymakers, the tax authorities, the corporate sector, and the academic community. 2025 Malque Publishing. All rights reserved. -
A Textual Analysis of Panchatantra, Enhanced by Technology from the Psychological Perspective
This research paper offers a textual analysis of the portrayal of animals in the Panchatantra tales, leveraging technology, Natural Language Processing (NLP) for enhanced insights. The study focuses on the interplay of anthropomorphism and stereotypes within these narratives, delving into the diverse stereotypes associated with specific animals in the stories. This analysis enhances our understanding of animal portrayal in children's literature. Natural Language Processing (NLP) techniques like textual classification and thematic analysis have been employed to examine the underlying archetypes embedded within the tales to comprehend stereotypes. Through a close examination of literary examples employing AntConc, a corpus analysis software, this paper provides readers with a nuanced understanding of how anthropomorphism and stereotypes influence human perceptions of animals and contribute to our understanding of the natural world. 2024 IEEE. -
A Theoretical Article: Exploring the Evolutionary Dynamics of Couples and Family Therapy
This article provides a comprehensive review of how the field of family therapy has evolved, tracing its roots from early practices influenced by eugenics to its current diverse theoretical frameworks, which are ever-expanding. In the mid-20th century, family therapy expanded beyond its eugenic roots, embracing diverse theoretical frameworks and giving rise to various therapeutic modalities like behavioral and emotionally focused family and couples therapy. However, due to cultural disparities, these concepts and models cannot holistically capture the essence of family therapy in India. They do not compute the central role of the intergenerational subsystem or understand hierarchical dynamics. What is deemed okay in the Western context does not hold true in the Indian context. Postmodern approaches show a marked improvement in dealing with these problems in cultural adaptations of family and couples therapy by integrating diverse therapeutic practices, technological advances, and cultural and diversity-sensitive practices. However, despite these advancements, the adapted modalities have a scope for improvement, posing a pressing need for research that bridges this gap. Moving forward research should focus on family change mechanisms, symptom improvement factors, and prioritizing culturally sensitive approaches to meet the unique needs of Indian families. The Author(s) 2024. -
A theoretical framework for gamified learning
The term gamified has been applied to a large number of processes in the organization. Marketing professionals have attempted to gamify customer experiences, while human resource managers have attempted to gamify employee processes like recruitment and onboarding. Being a powerful driver for goal-oriented behavioural change, gamification has the potential to revolutionise the way people work, collaborate, and develop. However, the application of gamification has met with limited success in the organization. Researchers have attributed this lack of success to incomplete understanding of the concept. The current study reviews literature in the area of Gamification in an attempt to arrive at a conceptual model explaining how gamification drives learning. The model proposed in this study is simple and draws from key theories related to Learning and use of technology. The purpose of the review is to provide a base for future researchers and a basic understanding for practitioners attempting to introduce gamified learning. BEIESP. -
A thorough investigation of various goals and responses for mobile software-defined networks
Cloud computing has caused some companies to modify their IT infrastructure and maintenance procedures and may eliminate their current hardware altogether. Conventional methods of setting up a switch or router may be error-prone and unable to make full use of the capabilities of current network architectures. As many intelligent networking designs as possible must be developed for intellectualization, activation, and customization in future networks. Due to software-defined networking (SDN) technology, it's possible to control, secure, and optimize network resources, eliminating the rigid coupling between the control plane and the data plane in traditional network architectures. Here, the chapter explores the problems, difficulties, and potential solutions associated with software-defined networks (SDN), a novel concept in computer networking. Through SDN, the network gains the ability to be programmable, quick, and adaptable thanks to its separation of data and its ability to control traffic. 2023, IGI Global. All rights reserved. -
A Thorough Review of Deep Learning in Autism Spectrum Disorder Detection: From Data to Diagnosis
Background: Autism Spectrum Disorder (ASD) is a multifaceted neurodevelop-mental condition with significant heterogeneity in its clinical presentation. Timely and precise identification of ASD is crucial for effective intervention and assistance. Recent advances in deep learning techniques have shown promise in enhancing the accuracy of ASD detection. Objective: This comprehensive review aims to provide an overview of various deep learning methods employed in detecting ASD, utilizing diverse neuroimaging modalities. We analyze a range of studies that use resting-state functional Magnetic Resonance Imaging (rsfMRI), structural MRI (sMRI), task-based fMRI (tfMRI), and electroencephalography (EEG). This paper aims to assess the effectiveness of these techniques based on criteria such as accuracy, sensitiv-ity, specificity, and computational efficiency. Methods: We systematically review studies investigating ASD detection using deep learning across different neuroimaging modalities. These studies utilize various preprocessing tools, at-lases, feature extraction techniques, and classification algorithms. The performance metrics of interest include accuracy, sensitivity, specificity, precision, F1-score, recall, and area under the curve (AUC). Results: The review covers a wide range of studies, each with its own dataset and methodolo-gy. Notable findings include a study employing rsfMRI data from ABIDE that achieved an accuracy of 80% using LeNet. Another study using rsfMRI data from ABIDE-II achieved an im-pressive accuracy of 95.4% with the ASGCN deep learning model. Studies utilizing different modalities, such as EEG and sMRI, also reported high accuracies ranging from 74% to 95%. Conclusion: Deep learning-based approaches for ASD detection have demonstrated significant potential across multiple neuroimaging modalities. These methods offer a more objective and data-driven approach to diagnosis, potentially reducing the subjectivity associated with clinical evaluations. However, challenges remain, including the need for larger and more diverse da-tasets, model interpretability, and clinical validation. The field of deep learning in ASD diagnosis continues to evolve, holding promise for early and accurate identification of individuals with ASD, which is crucial for timely intervention and support. 2024 Bentham Science Publishers. -
A Three-Species Model With Predator-Taxis Sensitivity: Hopf Bifurcation and Active Control Stabilization
This study presents an analysis of a novel fractional two preyone predator model incorporating predator-taxis sensitivity. We conduct a comprehensive stability analysis, explore the model's chaotic nature through period-doubling bifurcations, and also show the existence of limit cycles through fractional Hopf bifurcation. It is observed that the fractional-order parameter brings in a stabilizing effect and, simultaneously, a shift of the Hopf bifurcation point. At the Hopf bifurcation point, the system moves from stable equilibria to sustained oscillations. In addition, regardless of initial conditions, the system approaches a stable limit cycle, showing the robustness of the method. We also demonstrate the effectiveness of the active control method to eliminate the periodicity of the fractional system and also unravel the decelerating influence of the fractional-order parameter on the convergence time to equilibrium. These results provide valuable insights into the stabilization of ecosystem dynamics and contribute more broadly to our understanding of population dynamics in ecological systems. 2025 John Wiley & Sons Ltd. -
A top-down approach for studying the in-silico effect of the novel phytocompound tribulusamide B on the inhibition of Nipah virus transmission through targeting fusion glycoprotein and matrix protein
The proteins of Nipah virus ascribe to its lifecycle and are crucial to infections caused by the virus. In the absence of approved therapeutics, these proteins can be considered as drug targets. This study examined the potential of fifty-three (53) natural compounds to inhibit Nipah virus fusion glycoprotein (NiV F) and matrix protein (NiV M) in silico. The molecular docking experiment, supported by the principal component analysis (PCA), showed that out of all the phytochemicals considered, Tribulusamide B had the highest inhibitory potential against the target proteins NiV F and NiV M (-9.21 and ?8.66 kcal mol?1, respectively), when compared to the control drug, Ribavirin (-7.01 and ?6.52 kcal mol?1, respectively). Furthermore, it was found that Tribulusamide B pharmacophores, namely, hydrogen donors, acceptors, aromatic and hydrophobic groups, contributed towards the effective residual interactions with the target proteins. The molecular dynamic simulation further validated the results of the docking studies and concluded that Tribulusamide B formed a stable complex with the target proteins. The data obtained from MM-PBSA study further explained that the phytochemical could strongly bind with NiV F (-31.26 kJ mol?1) and NiV M (-40.26 kJ mol?1) proteins in comparison with the control drug Ribavirin (-13.12 and ?13.94 kJ mol?1, respectively). Finally, the results indicated that Tribulusamide B, a common inhibitor effective against multiple proteins, can be considered a potential therapeutic entity in treating the Nipah virus infection. 2024 Elsevier Ltd -
A Translator for Indian Sign Boards to English using Tesseract and SEQ2SEQ Model
Language translator for Indian language to English have been developed and it have proven to a challenging domain due to large combination of character in Indic scripts such as Tamil, Kannada and Hindi. In this paper we propose a system which captures Indian printed character and translates it into English, we have discussed the various method and machine learning model that was used to build this system with an accuracy of 87%. 2021 IEEE. -
A Travellers Melancholy
On possibly the most humid day in the month of June, I lay on a bed in a backpacking hostel, journalling my melancholy. 2025, The Assosiation FormAkademisk. All rights reserved. -
A turn-on bis-hydrazone fluorescent chemosensor for selective Cd2+ detection: synthesis, structural insights, and theoretical validation
Heavy metal pollution, particularly from cadmium(ii) ions (Cd2+), causes severe environmental and health risks due to its acute toxicity, carcinogenicity, and bioaccumulation, leading to kidney damage, neurological disorders, and other physiological issues. Herein, we report the one-pot synthesis of a bis-hydrazone-based fluorescent probe L2H2O (1) for selective detection of Cd2+. Probe 1 was derived from isophthalaldehyde and 3-pyridylcarbonyl hydrazine and single-crystal X-ray diffraction discloses a well-defined binding pocket with pyridyl, imine, and carbonyl donor sites suitable for Cd2+ coordination. Probe 1 exhibits weak emission in CH3CN/HEPES buffer (9?:?1, v/v, pH 7.4) due to photoinduced electron transfer (PET) and unrestricted intramolecular rotations. Upon selective binding to Cd2+, 1 displays a pronounced turn-on fluorescence response with intensity enhancements of at ?324 nm and at 420 nm, accompanied by bathochromic shifts to 327 nm (?? = 3 nm) and 445 nm (?? = 25 nm) (?ex = 295 nm). The limit of detection (LOD) for probe 1 with metal Cd2+ is 3.39 M, with a binding constant of 5 103 M?1. 1H NMR titration, DFT-optimized geometries (B3LYP/6-31+G(d)/LANL2DZ), and simulated UV-Vis spectra further confirm binding of Cd2+, blocking PET and rigidifying the structure via chelation-enhanced fluorescence (CHEF). This work presents a modular hydrazone scaffold for developing selective Cd2+ sensors with potential application in environmental and biological monitoring. The Royal Society of Chemistry and the Centre National de la Recherche Scientifique, 2026.