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Designing in situ nanostructured MWCNT-phloroglucinol modified webs for electrochemical-based dual screening of stress biomarkers
Phloroglucinol (PG), or benzene 1,3,5-triol, is an essential phenolic compound and a vital tannin. In this study, we developed a tannin-phloroglucinol (PG) derived redox mediator for the detection of glutathione (GT) and H2O2 on a glassy carbon electrode (GCE) modified with a multiwalled-chitosan composite. The PG redox platform was prepared using a cyclic voltammetric approach in pH 7 aqueous buffer media without any additional surfactant/chemical moieties. A highly stable, fouling-free surface confined redox characteristic was observed at an apparent electrode potential of E0? = ?0.196 V (A1/C1) and 0.05 V (A2/C2) vs. Ag/AgCl was observed. The as-prepared electrochemical platform achieved an ultra-low limit of detection (LOD) for glutathione (GT) of 0.16 M and LOQ of 2.08 M using a sustainable platform. In addition, it exhibited high selectivity for GT in the presence of various interfering analytes. In addition, the modified platform was extended to hydrogen peroxide (H2O2) sensing at ?0.196 V vs. Ag/AgCl with a LOD of 5.4 M in PBS buffer media at v = 10 mV s?1. The GCE/MWCNT-Chit@PG-Redox demonstrated robust performance in a proof-of-concept experiment for analyzing GT and H2O2 in real samples using a standard addition approach with good recovery values. This journal is the Owner Societies, 2026 -
Designing of a Free-Standing Flexible Symmetric Electrode Material for Capacitive Deionization and Solid-State Supercapacitors
In this work, a highly efficient free-standing flexible electrode material for capacitive deionization and supercapacitors was reported. The reported porous carbon shows a high surface area of 2070.4 m2 g-1 with a pore volume of 0.8208 cm3 g-1. The material exhibited a high specific capacitance of 357 F g-1 at 1 A g-1 in a two-electrode symmetric setup. A solid-state supercapacitor device has been fabricated with a total cell capacitance of 152.5 F g-1 at 1 A g-1 in a solid PVA/H2SO4 gel electrolyte with an energy density of 21.18 W h kg-1 at a 501.63 W kg-1power density. A long-run stability test was carried out up to 15,000 cycles at 5 A g-1 that showed capacitance retention of 99% with ?100% Coulombic efficiency. Furthermore, the electrosorption experiment was conducted by a flow-through test by coating on commercially available cellulose thread that was employed, which shows electrosorption ability up to 16.5 mg g-1 at 1.2 V in a 500 mg L-1 NaCl solution. Complete experiments were conducted with a proper procedure, provided by scientific approaches with analytical data. Thus, the reported electrode material showed bifunctional application for energy storage and environmental remediation. 2023 American Chemical Society. -
Designing of a Human-Centric Autonomous Fish-Feeding Robot: Advancing Aquaculture Sustainability Through Enhanced HumanRobot Interaction
The increasing need for sustainable and efficient aquaculture practices due to rising global seafood consumption and challenges such as labor shortages, environmental degradation, and inefficiencies in manual feeding methods highlights the crucial requirement for an autonomous fish-feeding robot. Service robots are usually designed as a mechatronic design issue focused on implementing essential technological functions, frequently neglecting the significance of intuitive operation and usability. Using the double diamond design framework, this study details the development of a human-centered autonomous fish-feeding robot for fish farming. The goal was to make it more user-friendly and sustainable. The project began the discovery phase to gather insights into aquaculture difficulties and find potential for innovation in fish-feeding procedures. Around 38 small- and medium-sized fish farmers from Andhra Pradesh, Tamil Nadu, and Odisha were interviewed to gather their needs for fish farm feeding systems. In the Define phase, the focus shifted to developing a system that optimizes feeding efficiency while considering environmental factors and user requirements. During the development process, the team utilized iterative design and prototyping, incorporating modern technology such as AI for accurate feed distribution and sensors for evaluating water quality and fish health. User comments played a vital role in enhancing the robots usability and functionality. The Deliver phase concentrated on deploying the robot in simulation environments using ROS AND GAZEBO for the technical feasibility test, assessing its influence on operational efficiency and waste reduction, and advocating for sustainable aquaculture methods. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Designing optimization frameworks for ICT-enabled e-leadership strategies
The rapid development of information and communication technology (ICT), especially, makes it easier for individuals to create, organize, as well as access information, which has significant effects on the skills required of leaders. The allocation of power and the emergence of connections in organizations might be impacted by new technologies. As a result, leadership is being placed in a new context in an information technology-enabled economy. It is crucial to consider how technological advancement and leadership interact to affect both the structure and outcomes of leadership, as well as how leadership itself may affect the adoption of cutting-edge information technology and its effects on organizations. In the internet era, leadership is undoubtedly different. As the world continues to change as a result of the apparent and astonishing advancements in computer and communications technology, it is imperative that we consider what has changed and what has stayed the same. The impact of the e-factor on leadership is one very significant setting for leadership. 2026 selection and editorial matter, Mukesh Kumar Awasthi, Ashwani Kumar, Manoj Gupta; individual chapters, the contributors. -
Designing Remote-Sensed Intelligent Visual Analytics Algorithms for Environmental Monitoring Systems
Increasing climate variability and the rapid degradation of natural ecosystems have necessitated the development of intelligent systems that can track and assess environmental changes in real-time. By combining multi-modal remote sensing data with advanced machine learning and visual analytics techniques, this paper introduces a novel framework for Remote-Sensed Intelligent Visual Analytics (RS-IVA), which aims to improve environmental monitoring systems. To offer a comprehensive, scalable, and adaptable monitoring system, the proposed framework utilizes ground sensor inputs, UAV-based aerial photography, and high-resolution satellite imaging. To identify anomalies such as deforestation, urbanization, water pollution, and changes in air quality, a hybrid deep learning-based algorithm is employed. Explainable AI (XAI) elements make sure that the decision-making process is transparent and accessible. To assist stakeholders, investigate spatiotemporal patterns, forecast environmental hazards, and enhance evidence-based policy decisions, an interactive visual analytics dashboard is being developed. Experiments using benchmark datasets demonstrate that the system is highly accurate in identifying significant environmental changes and exhibits greater adaptability across a wide range of climatic and geographic regions. Intelligent analytics and remote sensing technologies collaborate to improve situational awareness and provide early warnings for sustainable resource planning and disaster management. This research advances the development of next-generation innovative environmental monitoring systems by integrating human-in-the-loop visualization, AI-driven analytics, and remote sensing for informed ecological governance. 2025, Interdisciplinary Publishing Academia. All rights reserved. -
Designing social learning analytics for collaborative learning using virtual reality, life skill, and STEM approach
This chapter explores the design of social learning analytics for collaborative learning, incorporating virtual reality, life skills, and a STEM approach. Researchers employ social learning analytics, an emerging field that combines social network analysis and learning analytics, to gain insights into collaborative learning environments. The chapter emphasizes integrating virtual reality, life skills, and STEM in social learning analytics, covering data collection methods, data analysis techniques, and pedagogical applications. It also explores key considerations for designing social learning analytics in collaborative learning, encompassing the development of tools and assessment strategies. Finally, the chapter looks ahead to future directions and prospects for social learning analytics in collaborative learning. 2024, IGI Global. All rights reserved. -
Desiri Naturals: sustainable agriculture and eco-friendly business
Learning outcomes: After completion of the case study, the students will be able to critically analyze the business model of Desiri Naturals, analyze the pricing strategy of Desiri Naturals, examine the importance of experiential marketing in the success of an environment-friendly business, identify the challenges faced by new entrepreneurs and evaluate the sustainability practices of Desiri Naturals. Case overview/synopsis: This case study discusses the business model of an environmentally friendly business. The challenges and obstacles faced by entrepreneurs are illustrated in this case. The entrepreneurs vision to provide chemical-free food is highlighted and their business operations as a means to fulfill this vision are explained. Desiri used an age-old bull-driven method of oil extraction (Ghana). Challenges in pricing due to the availability of low-priced mass-produced edible oil using the solvent extraction process are presented in this case. The entrepreneurs faced the pricing dilemma at the inception of the business, as oil produced using the natural cold pressing method cost three times the selling pricing of solvent-extracted oil. Innovative methods of experiential marketing such as Ghana tourism are explained in this case. This case study also explains the sustainable and natural farming techniques propagated through its network of farmers. This case study provides insights into the scalability of this model and the scope for employment generation in rural India. The environmentally friendly practices followed by Desiri, such as the use of glass bottles and reusable steel containers for packaging oil are emphasized. Finally, this case presents the marketing and operational challenges faced by entrepreneurs in their quest to expand their operations. Complexity academic level: This case study can be used by postgraduate and undergraduate students studying marketing, entrepreneurship, sustainability and operations management courses in commerce and business management streams. Supplementary materials: Teaching notes are available for educators only. Subject code: CSS8: Marketing. 2024, Emerald Publishing Limited. -
Despeckling of Ultra sound Images using spatial filters - A Fusion Approach
Ultra sound images are normally affected by speckle noise which is typically multiplicative in nature. This study proposes different fusion based despeckling methods for ultra sound images. The output of existing spatial domain despeckling methods viz. Lee filter, Bayesian Non Local Means (BNLM) filter and Frost filter are fused pairwise. Fusion is implemented in two steps, first an inter-scale stationary wavelet coefficient fusion followed by an intra-scale wavelet coefficient fusion. Analysis of these projected despeckling strategies are conducted using metrics like Peak Signal to Noise Ratio (PSNR), Equivalent Number of Looks (ENL), Structural Similarity Index (SSIM) and Universal Image Quality Index (UIQI). The results show that the performance of fusion based methods is better than the respective individual filters for despeckling ultra sound images. 2019 IEEE. -
Destination governance and a strategic approach to crisis management in tourism /
Journal Of Investment And Management, Vol.5, Issue 1, pp.1-5, ISSN: 2328-7721 (Online), 2328-7713 (Print). -
Destination image and perceived meaningfulness for visitor loyalty: A strategic positioning of Indian destinations
The purpose of this study is to empirically test and validate a multi-dimensional structure of In-loco Destination Image and perceived meaningfulness using an integrated model of visitor loyalty. The model was tested using data collected from responses of foreign tourists visiting India (n = 246). The results identified six dimensions of In-loco Destination Image: Amenities, Attractions, Leisure, Culture, Support Systems, and Hospitality. In addition, the investigation observes that of the identified dimensions of perceived meaningfulness, the spiritual and societal dimensions contribute more to perceived meaningfulness than the physical well-being aspect. Further, the exploration estimated the theoretical framework developed using structural equation modelling and established the mediating role of perceived meaningfulness in developing visitor loyalty from In-loco Destination Image. The studys observations helped identify three positioning approaches, namely objective, subjective, and combined, offering suggestions to destination marketers to effectively reposition Indian destinations. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
Desymmetrisation of meso-2,4-Dimethyl-8-oxabicyclo[3.2.1]-oct-6-ene-3-ol and its Application in Natural Product Syntheses
The compounds containing chiral centers and different functional groups serve as magnificent building blocks for the preparation of various natural products that are having immense biological activity. Dimethyl-8-oxa-bicyclo[3.2.1]oct-6-en-3-ol is one of the wonderful synthons to construct multiple stereo centers at a time during the asymmetric synthesis. In this account, we discuss our research efforts toward the synthesis of various simple and complex natural products from the past three decades (19952020) by using dimethyl-8-oxa-bicyclo[3.2.1]oct-6-en-3-ol as a synthon. Moreover, the synthetic utility of this starting material was investigated and well demonstrated. Further, we executed the desymmetrization of dimethyl-8-oxa-bicyclo[3.2.1]oct-6-en-3-ol by hydroboration to get different chiral centers. After obtaining the stereocenters, we could manage either the fragment, formal or total synthesis of natural products, by simple protection and deprotection sequence followed by C?C bond formation steps. 2021 The Chemical Society of Japan & Wiley-VCH GmbH -
Detecting Abusive Comments in Mizo: A Machine Learning Approach for a Low-Resource Language
The detection of abusive language in online spaces is crucial for ensuring a safe digital environment, particularly for low-resource languages like Mizo. Mizo, a tonal Tibeto-Burman language spoken primarily in Mizoram, India, poses significant computational challenges due to its phonetic complexity and limited linguistic resources. This research presents a method based on machine learning for abusive comment detection in Mizo, addressing the lack of annotated datasets and specialized NLP tools. A structured pipeline involving data collection, preprocessing, feature engineering, and model evaluation was implemented. Our study compares the effectiveness of several conventional machine learning methods, such as Random Forest, Support Vector Machines (SVM), Logistic Regression, and XGBoost, against transformer-based models such as Multilingual BERT(mBERT) and MizBERT. According to experimental data, MizBERT achieves the highest accuracy and F1-score, outperforming all other models by a substantial margin. This work contributes to the development of computational tools for Mizo NLP, laying a foundation for automated moderation systems and fostering digital inclusivity for Mizo-Speaking communities. 2025 IEEE. -
Detecting and Countering Misinformation Through NLP-Based Approach for Fake News Detection
The rapid expansion of digital media and the seamless transmission of information have raised serious concerns about the widespread dissemination of misinformation and fake news. Combatting this issue requires robust and effective techniques that can accurately detect and classify fake news. Natural language processing (NLP) approaches have emerged as powerful tools in this endeavor, leveraging advanced text classification algorithms to identify and counteract misinformation. This study includes NLP approaches for countering misinformation through text classification, with a specific focus on fake news detection. Leveraging natural language processing techniques, the project implements a text classification pipeline for identifying and distinguishing between genuine and fake news. The pipeline encompasses essential NLP steps such as tokenization and stop word removal. Traditional machine learning algorithms, such as the gradient boosting classifier, CatBoost classifier, random forest classifier, AdaBoost classifier, logistic regression, and SVM linear kernel are trained using the transformed data to classify news articles. This study explores feature engineering techniques and model evaluation to enhance the classification performance. Experimental results indicate the effectiveness of The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Detecting Cyberbullying in Twitter: A Multi-Model Approach
With cyberbullying surging across social media, this study investigates the effectiveness of four prominent deep learning models - CNN, Bi-LSTM, GRU, and LSTM - in identifying cyberbullying within Twitter texts. Driven by the urgent need for robust tools, this research aims to enrich the field of cyberbullying detection by thoroughly evaluating these models' capabilities. A dataset of Twitter texts served as the training ground, rigorously preprocessed to ensure optimal model compatibility. Each model, CNN, Bi-LSTM, GRU, and LSTM, underwent independent training and evaluation, revealing distinct performance levels: CNN achieved the highest accuracy at 83.10%, followed by Bi-LSTM (81.90%), GRU (81.73%), and LSTM (16.07%). These differences highlight the unique strengths of each architecture in analysing and representing text data. The findings highlight the CNN model's superior performance, indicating its potential as a highly effective tool for Twitter-based cyberbullying detection. While the deep learning models explored here offer promising avenues for detecting cyberbullying on Twitter, their performance highlights the complexities inherent in this task. The limited space of tweets can often obscure the true intent behind words, making accurate identification a nuanced challenge. Despite this, the CNN model's robust performance suggests that carefully chosen architectures hold significant potential for combating online harassment. This research paves the way for further explorations in harnessing the power of AI to create a safer and more civil online experience where respectful communication can flourish even within the constraints of concision. 2024 IEEE. -
Detecting Deepfake Voices Using a Novel Method for Authenticity Verification in Voice-Based Communication
The widespread use of deepfake technology in recent years has given rise to grave worries about the alteration of audio-visual material. The integrity of voice-based communication is particularly vulnerable to the threat posed by deepfake voice synthesis. The development of cutting-edge methods for the identification of deepfake voices is examined in this paper, which also offers a thorough analysis of current approaches, their advantages, and disadvantages. The research presents a novel method for detecting deepfakes in voice recordings that uses signal processing, machine learning, and audio analysis to separate synthetic voices from authentic voices. By achieving superior accuracy in differentiating between real and deepfake voices, and proposed method supplies a strong barrier against the misuse of voice synthesis technology for malicious purposes, also go over the research some of the possible uses for this technology, like voice authentication system security and social media platform content moderation. The paper's insights will support continued efforts to strengthen the authenticity of voice communication in the digital age and reduce the risks associated with deepfake voice synthesis. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Detecting Fake Information Dissemination using Leveraging Machine Learning and DRIMUX with B-LSTM
Information integrity and public confidence are seriously threatened by the rapid expansion of fake news and misinformation that has resulted from the online broadcast of information. This work focuses on the detection of fraudulent information propagation utilizing machine learning techniques and the Digital Reputation and Influence Measurement Unit (DRIMUX) in order to address this problem. The use of Bidirectional Long Short-Term Memory (B-LSTM) networks into the detection process is something we really advocate. B-LSTM enables the capture of contextual dependencies from both past and future time steps, enhancing the understanding of sequential data. Additionally, DRIMUX provides reputation and influence measurements to assess the credibility of information sources. Experimental analyses on various datasets reveal the promising performance of the suggested methodology, highlighting its potential in preventing the spread of false information and protecting the veracity of digital information. 2024, Ismail Saritas. All rights reserved. -
Detecting Infectious Disease Based on Social Media Data Using BERT Model
Seasonal diseases are those diseases that are widespread during a particular time of the year including monsoons, winter etc. In the absence of preventative measures, the human race remains vulnerable to the hazardous effects of seasonal diseases following regular patterns of increased inci- dence and transmission which remains a global concern. Dengue, Influenza, etc. are such types of diseases where every year many people get affected globally. The primary focus of this research paper is to understand the opinion of people regarding the seasonal diseases. The research paper covers sentiment analysis on textual data from social media where people have vocalized their sentiments or thinking regarding seasonal diseases and seasonal infectious diseases. Influenza, Dengue, Malaria, Japanese Encephalitis, and Chikungunya are the seasonal diseases that have been covered in this research paper. To achieve this, the language model Bidirectional Encoder Representations from Transformers (BERT) was used to verify the sentiments about the seasonal diseases. The result of the investigation hold the potential to significantly enhance our comprehension of societal sentiments, discerning between states of tranquility and concern among individuals. The outcome of the study will help healthcare department to plan the necessary actions. 2024 IEEE. -
Detecting Student Depression Using Non-Clinical Measures with Explainable Predictive Modeling
Depression among students is a serious global mental health concern, affecting academic performance, emotional wellbeing, and long-term development. While traditional diagnostic tools like self-reported questionnaires and clinical interviews are useful, they often suffer from subjectivity, recall bias, and limited scalability. This study introduces a data driven, interpretable machine learning approach to predict student depression using both academic and nonacademic factors, without relying on clinical indicators. The dataset comprises student information on demographics, academic workload, lifestyle habits, social interactions, financial stress, and emotional state. Following thorough preprocessing including handling missing values, encoding variables, correlation-based feature reduction, and SMOTE to address class imbalance, ten supervised machine learning models were trained and assessed. Among them, a SMOTE enhanced XGBoost model achieved the highest test ROC AUC score of 0.95. To maintain transparency, SHAP (Shapley Additive Explanations) was employed to interpret the model's predictions, highlighting key risk factors such as academic pressure, poor sleep quality, financial difficulties, and low social support. These findings can help guide early interventions and build trust with stakeholders. Future work may involve incorporating longitudinal and multimodal data, deploying real time solutions in educational settings, and addressing ethical considerations around privacy, fairness, and consent in AI based mental health systems. 2025 IEEE. -
Detecting the magnitude of depression in Twitter users using sentiment analysis
Today the different social networking sites have enabled everyone to easily express and share their feelings with people around the world. A lot of people use text for communicating, which can be done through different social media messaging platforms available today such as Twitter, Facebook etc, as they find it easier to express their feelings through text instead of speaking them out. Many people who also suffer from stress find it easier to express their feelings on online platform, as over there they can express themselves very easily. So if they are alerted beforehand, there are ways to overcome the mental problems and stress they are suffering from. Depression stands out to be one of the most well known mental health disorders and a major issue for medical and mental health practitioners. Legitimate checking can help in its discovery, which could be useful to anticipate and prevent depression all-together.Hence there is a need for a system, which can cater to such issues and help the user. The purpose of this paper is to propose an efficient method that can detect the level of depression in Twitter users. Sentiment scores calculated can be combined with different emotions to provide a better method to calculate depression scores. This process will help underscore various aspects of depression that have not been understood previously. The main aim is to provide a sense of understanding regarding depression levels in different users and how the scores can be correlated to the main data. 2019 Institute of Advanced Engineering and Science. All rights reserved. -
Detection and analysis of android malwares using hybrid dual Path bi-LSTM Kepler dynamic graph convolutional network
In past decade, the android malware threats have been rapidly increasing with the widespread usage of internet applications. In respect of security purpose, there are several machine learning techniques attempted to detect the malwares effectively, but failed to achieve the accurate detection due to increasing number of features, more time consumption decreases in detection efficiency. To overcome these limitations, in this research work an innovative Hybrid dual path Bidirectional long short-term memory Kepler dynamic graph Convolutional Network (HBKCN) is proposed to analyze and detect android malwares effectively. First, the augmented abstract syntax tree is applied for pre-processing and extracts the string function from each malware. Second, the adaptive aphid ant optimization is utilized to choose the most appropriate features and remove irrelevant features. Finally, the proposed HBKCN classifies benign and malware apps based on their specifications. Four benchmark datasets, namely Drebin, VirusShare, Malgenome -215, and MaMaDroid datasets, are employed to estimate the effectiveness of the technique. The result demonstrates that the HBKCN technique achieved excellent performance with respect to a few important metrics compared to existing methods. Moreover, detection accuracies of 99.2%, 99.1%,99.8% and 99.8% are achieved for the considered datasets, respectively. Also, the computation time is greatly reduced, illustrating the efficiency of the proposed model in identifying android malwares. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.