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Modalities in data: understanding text, images, and audio
Data modalities, encompassing diverse forms such as text, audio, image, and video, play a pivotal role in shaping modern data analysis and machine learning applications. Each modality represents information in a unique format, requiring specific processing and interpretation methods. The integration of multiple modalities, known as multimodal data, enhances decision-making and predictive accuracy, particularly in complex systems like sentiment analysis, speech recognition, and medical diagnostics. Deep learning techniques have facilitated the seamless fusion of multimodal data, enabling a more comprehensive understanding across various fields, from healthcare to social media analytics. For example, combining text with images improves sentiment analysis, while integrating audio and video aids in more accurate speech recognition. However, the incorporation of multimodal data presents challenges, including data heterogeneity, synchronization issues, and dimensionality concerns. Data formats differ across modalities, and aligning them for cohesive analysis requires sophisticated algorithms and computational power. Despite these obstacles, multimodal data offers significant benefits, such as enhanced customer experience in business and increased diagnostic accuracy in health care. Furthermore, the rise of large datasets and artificial intelligence (AI) technologies has fueled innovation, enabling the development of more efficient models capable of uncovering intricate relationships within data. This chapter discusses various modalities, their applications, and the technological advancements driving their integration. It also highlights the challenges in multimodal data processing and the solutions being developed to address these complexities, offering valuable insights for businesses, researchers, and AI practitioners. 2026 Elsevier Inc. All rights reserved. -
The Effect of Short-Term Training of Vipassanas Body-Scan on Select Cognitive Functions
This experiment examined the effect of a short-term body-scan meditation technique of vipassana practice on select cognitive functions. Participants (n = 77) were randomly divided into an experimental group (n = 37) and an active control group (n = 40). The average age of participants in the experimental group and the active control group was 21.67 1.16 and 21.40 3.14years, respectively. The experimental group practiced body-scan mindfulness, one session per day for 6 days with each session lasting for 25min. Participants in the active control group spent an equal amount of time reading fiction of their choice and listening to soothing music. Variables that were studied included five cognitive functions, namely reaction time, attention, learning, working memory, and social-emotional cognition. Results showed that short-term mindfulness meditation decreased reaction time and increased attention, with mild effect size. It may be concluded that short-term mindfulness practice might be an alternative for individuals who, due to various reasons, cannot practice long-term courses. 2018, National Academy of Psychology (NAOP) India. -
Concept Mapping of Issues of Students Life in University
The undergraduate student body forms around 85.9% of the total number of students enrolled in India, which is a significant population. It has become imperative to understand the issues that these students face during their undergraduate years as a precursor to developing mechanisms and strategies to enable student progress, both academically and developmentally. This study aimed at developing a concept map to outline the various aspects and issues of the undergraduate students life in India utilizing the concept mapping method. Data from participants (n = 141) at different phases was analysed resulting in 49 unique life issues and aspects and 8 clusters. The emerging issues have relevance and implications for teachers, parents, administrators and other stakeholders in structuring and developing services targeted towards undergraduate students in India. 2015, National Academy of Psychology (NAOP) India. -
Study of Effect of Vipassana on Anxiety and Depression
International Journal of Psychology and Behavioral Sciences, Vol-2 (6), pp. 274-276. -
The Search for Universal Values
IOSR Journal of Humanities and Social Science Vol. 2, Issue 1, pp 69-72, ISSN No. 2279-0837 -
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. -
An Intelligent Method for Fraud Detection in Digital Payments based on SVR with GC-RF Approach
The use of automated algorithms to detect fraud on electronic payment networks is challenging. Digital payment systems and their users are vulnerable to cybercriminals who take advantage of security holes or users' negligence to steal passwords, perpetrate fraud, launder money, and carry out other malicious acts. Conventional methods of fraud detection are challenging to execute because of the difficulties of acquiring massive volumes of manually annotated data. It is tough to notice new trends because fraudsters are often changing their techniques. Feature extraction, model training, and data preprocessing were the main areas of emphasis in this systematic research. Data pretreatment encompassed tasks such as acquiring training sample data, cleaning, converting, integrating, and altering the data. Feature extraction is the backbone of SVR-GC-RF model training; it takes all the data in a dataset and turns it into features. The suggested method outperformed SVR and RF in terms of accuracy by 95.23 percent. The importance of hierarchical fraud detection in online payment systems is highlighted in this paper. Through the use of effective feature extraction and model training, the study enhances fraud detection. Methods for detecting fraud need to change if they are to keep up with the criminals. 2025 IEEE. -
Enhanced dielectric and supercapacitive properties of spherical like Sr doped Sm2O3@CoO triple oxide nanostructures
Integrating the hybrid nanostructures exhibiting enhanced storage and electrical properties requires tuning of composition of constituents. To address this issue, we prepared Sr2+ nanoparticles (NPs) decorated over Sm2O3@CoO nanostructures (NS) by chemical precipitation. The structure integrity of the composite was determined by analytical tools. Based on the strongest peak of X-ray diffraction (XRD), crystallite size of the nanoparticles was determined to be 26.14 nm, indicating a mixed phase of monoclinic and tetragonal crystal formation. FESEM revealed a spherical-like morphology with a homogeneous distribution of microstructures with average sizes ranging from 68 nm to 60 nm. The optical absorptivity revealed a redshift in absorption bands centred at 337.0 nm, 343.9 nm, and 353.0 nm in UV-region. The optical band gap of NS was found to be in the range of 3.38 eV to 3.15 eV, and the BET surface area of Sr15%:Sm2O3@CoO was found to be 458469 cm2/g with a corresponding pore size of 13.17 nm. All Sr-doped Sm2O3@CoO NS exhibited higher ionic conductivity and dielectric constant than undoped material. In an aqueous KOH electrolyte, the NS showed a specific capacity of 234.2C/g (65.1mAh/g) demonstrating the material as potential candidate in energy storage and dielectrics. 2022 Elsevier Ltd -
Enhanced electrical properties of CuO:CoO decorated with Sm2O3 nanostructure for high-performance supercapacitor
In the present investigation, we have synthesized samarium (Sm) nanoparticles (NPs) and anchored them onto the surface of CuO:CoO nanostructure (NS) by utilizing a simple chemical precipitation method. Nanostructures (NS) were characterized utilizing powdered X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS), scanning electron spectroscopy (SEM), transmission electron spectroscopy (TEM), UVvisible spectroscopy (UVVis), and BrunauerEmmettTeller (BET) studies. Resulting Smx CuO: CoO (x = 1%, 5%, 10%, and 12%) NS were investigated for their anomalous electrical and supercapacitive behavior. NS energy storage performance was experimentally determined using cyclic voltammetry (CV), galvanostatic chargedischarge (GCD), and electrochemical impedance spectroscopy (EIS). Sm10%CuO:CoO exhibited better electrochemical response than other samples and showed a maximum specific capacitance of 283.6F/g at 0.25A/g in KOH electrolyte. However, contrary to our expectation, NS displayed rectifying nature in I-V, intercalative nature in C-V, and polaronic permittivity in all concentrations of Sm2O3 doping as compared with undoped CuO:CoO NS. The outstanding properties of Smx CuO:CoO NS are attributed to the synergy of high charge mobility of Sm NPs, leading to significant variation in dielectric permittivity, currentvoltage (I-V) response, capacitancevoltage (C-V) behavior, with the formation of Sm3+ ionic cluster. The clusters lead to a change in dipole moment creating a strong local electric field. Additionally, a CR2032 type symmetric supercapacitor cell was fabricated using Sm10%CuO:CoO, which exhibited a maximum specific capacitance of 67.4F/g at 0.1A/g. The cell was also subjected to 5000 GCD cycles where it retained 96.3% Coulombic efficiency. Graphical Abstract: [Figure not available: see fulltext.] 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Facile synthesis of novel SrO 0.5:MnO 0.5 bimetallic oxide nanostructure as a high-performance electrode material for supercapacitors
Perovskite bimetallic oxides as electrode material blends can be an appropriate method to enhance the supercapacitor properties. In the present research, SrO 0.5:MnO 0.5 nanostructures (NS) were synthesized by a facile co-precipitation method and calcinated at 750800C. Crystal structure of SrO 0.5:MnO 0.5 NS were characterized by X-ray diffraction, surface chemical composition and chemical bond analysis, and dispersion of SrO into MnO was confirmed by X-ray photoelectron spectral studies. Structural morphology was analyzed from scanning electron microscopy. Optical properties of SrO 0.5:MnO 0.5 NS were studied using UV-Visible spectrophotometer and SrO 0.5 and MnO 0.5 NS showed ?75nm grain, ? 64nm grain boundary distance, with two maxima at 261nm and 345nm as intensity of absorption patterns, respectively. The synthesized SrO 0.5:MnO 0.5 NS exhibited high specific capacitance of 392.8F/g at a current density of 0.1A/g. Electrochemical impedance spectroscopy results indicated low resistance and very low time constant of 0.2s ?73% of the capacitance was retained after 1000 galvanostatic charge-discharge (GCD) cycles. These findings indicate that SrO 0.5:MnO 0.5 bimetallic oxide material could be a promising electrode material for electrochemical energy storage systems. The Author(s) 2022. -
Factors influencing purchase decision and brand switching in the passenger car segment in Bengaluru
This study identifies and analyses the Product Attributes of Passenger Cars and the demographic factors that influence consumer Purchase Decision and Brand Switching in the Indian context, specific to the city of Bengaluru. It discusses the existing knowledge pertaining to Passenger Cars and a conceptual framework is developed based on the review of literature. The research identifies what drives the Purchase Decision and Brand Switching for the Indian consumers and analyses how it differs based on demographic variables such as age, gender and income. Based on the model thus created, the research seeks to segment the Indian Passenger Car consumers according to the significant demographic variables thus identified. A questionnaire was administered to 200 respondents of different age, income and gender groups within the city of Bangalore. The data was then analyzed using Factor Analysis, One-way ANOVA and frequency analysis in SPSS.It was found that Quality, Aftersales Service, Safety and Price are the major value factors effecting purchase decision of Indian Passenger Car consumer. Age and income also has a significant influence of Purchase Decision and Brand Switching. It was also found that purchase intention varies between different age and income groups. The research was conducted within the city of Bangalore alone which may not be generalized to the entire country. 2020 SERSC. -
Evaluating Generalization and Robustness of U-Net Based Image Steganography
This paper investigates the effectiveness and generalization ability of U-Net based image steganography models across multiple datasets, with comparisons to the classical Least Significant Bit (LSB) substitution method. Models were trained on STL-10, CIFAR-10, and Stanford Cars datasets and evaluated both in-distribution and on out-of-distribution internet images. Results show that the STL-10 model consistently achieved the best trade-off between imperceptibility and recovery quality, while the CIFAR-10 model failed to generalize due to its low resolution and limited diversity. Baseline experiments confirmed that LSB achieves extremely high PSNR and SSIM at low payloads, but suffers sharp increases in Bit Error Rate (BER) under higher payloads or even mild distortions such as JPEG compression and Gaussian noise. By contrast, the U-Net model provided more stable recovery and greater robustness, highlighting the advantages of learned feature embeddings over handcrafted substitution. These findings underscore the importance of dataset diversity and robustness testing in developing practical steganographic systems for real-world deployment. 2025 IEEE. -
The Development of Structured Tele Based Medicine Concept Using Programmable System
In the medical field, clinics and hospitals frequently use dispersed applications like telediagnosis. These apps must nevertheless provide information security in order to properly transit security measures like firewalls and proxies. The User Datagram Protocol (UDP) is often recommended for videoconferencing applications because of its low latency; nevertheless, security problems occur when UDP tries to pass through firewalls and proxies without a specified set of fixed ports. In order to overcome these obstacles, this study presents a revolutionary platform that uses Transmission Control Protocol (TCP) rather of UDP: VAGABOND, which stands for 'Video Adaptation framework, across security gateways, based on transcription,' Adaptation Proxies (APs) that are designed to accommodate user preferences, device variations, and dynamic changes in network capacity comprise VAGABOND. This platform's versatility at the user and network levels guarantees seamless operation in a range of scenarios. VAGABOND uses a binomial probability distribution to start making adaptation decisions. This distribution is formed from the retention of video packets inside a certain time period. VAGABOND gets beyond firewall and proxy constraints by using ordinary TCP ports (like 80 or 443) to provide videoconferencing data via TCP. But even though TCP is a dependable transport protocol, it can occasionally have latency and socket timeout problems. VAGABOND has clever adaptation techniques to deal with these problems and ensure smooth data transfer. 2024 IEEE. -
Front-End Security Analysis forCloud-Based Data Backup Application Using Cybersecurity Tools
In this challenging, demanding, daunting, and competitive business world, the rise, and growth of cybercrimes are very high. With the proliferation of Cloud Computing techniques, usually in industrial arenas, business information and important clients data are stored and managed using cloud platforms. Application programs are developed to handle such valuable information assets of the organizations. Cloud backups are provided for these client data where security is the most concerning aspect. There are many vulnerabilities in the current scenario where intruders can cause havoc. Destruction of the product can happen by exploiting vulnerabilities that can put the company and the product in jeopardy. It may create a bad impression about the organization among the customers, competitors, and the public world. This paper shows the work done by a cyber security team whose main objective is to run vulnerability analysis and mitigate threats on an application that backs up the clients data to the cloud. Cyber Security is an important aspect in all types of businesses because it protects all categories of data such as fragile data, private information, intellectual property data, and other data including governmental and industrial information systems from theft and damage which concludes in huge financial loss and loss of client data. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Damaged Relay Station: EEG Neurofeedback Training in Isolated Bilateral Paramedian Thalamic Infarct
Stroke is a major public health concern and leads to significant disability. Bilateral thalamic infarcts are rare and can result in severe and chronic cognitive and behavioral disturbances - apathy, personality change, executive dysfunctions, and anterograde amnesia. There is a paucity of literature on neuropsychological rehabilitation in patients with bilateral thalamic infarcts. Mr. M., a 51 years old, married male, a mechanical engineer, working as a supervisor was referred for neuropsychological assessment and rehabilitation with the diagnosis of bilateral paramedian thalamic infarct after seven months of stroke. A pre-post comprehensive neuropsychological assessment of his cognition, mood, and behavior was carried out. The patient received 40 sessions of EEG-Neurofeedback Training. The results showed significant improvement in sleep, motivation, and executive functions, however, there was no significant improvement in memory. The case represents the challenges in the memory rehabilitation of patients with bilateral thalamic lesions. 2024 Neurology India, Neurological Society of India. -
Leveraging FinTech for the Advancement of Circular Economy
During the past six decades, there has been a lot of emphasis on increasing production and fulfilling the demands of the fast-growing population. As a result, there has been unprecedented utilization and depletion of natural resources and harm to the environment. It was rightly realized by government and policymakers that there is an indispensable need to align economic development with the environment. In other words, the world needs to pursue environmentally friendly economic development. In order to achieve sustainable development, the thought leaders devised a new approach called circular economy. The circular economy focuses on reusing and recycling materials to reduce the consumption of natural resources and minimize waste creation. In recent years, financial technology commonly known as FinTech has become a significant part of commercial activities across many industries. FinTech has benefited organizations and users in terms of cost and time saving with a high degree of reliability. This article outlines the ways in which FinTech supports the cause of a circular economy. It also explores the impediments in this path. 2024 Scrivener Publishing LLC. -
Understanding green economy
Resource efficiency, environmentally friendly consumption and production, and the green economy's contribution to sustainable development resource efficiency refer to the ways in which resources are used to deliver value to society and aims to reduce the amount of resources needed, as well as emissions and waste generated, per unit of product or service. Sustainable consumption and production aim to improve production processes and consumption practices to reduce resource consumption, waste generation, and emissions across the full life cycle of processes and products. A macroeconomic strategy for achieving sustainable economic growth is offered by the green economy, with a primary emphasis on investments, jobs, and skills. 2024, IGI Global. All rights reserved. -
Role of social media influencers in fashion and clothing
This study provides an overview of the influence of social media influencers in the fashion and clothing industry. With the increasing presence of social media in people 's lives, individuals are easily influenced by what they see online, leading to the adoption of trends promoted by influencers. Influencers, who have established their personal brand and gained a substantial following, play a key role in shaping consumer preferences and driving sales. Social media platforms allow fashion brands to connect with their audience, democratizing fashion shows and enabling direct interaction. The rise of fast fashion and the influence of fashion influencers have contributed to the growth of the clothing industry. Businesses are now utilizing AI-powered analytics and dedicated platforms to enhance their influencer marketing strategies. In summary, social media influencers have a significant impact on consumer behavior, driv- ing sales and shaping trends in the fashion and clothing industry. 2023, IGI Global. All rights reserved. -
Food wastage and consumerism in circular economy: a review and research directions
Purpose: Considering food waste as a global problem resulting from the wastage of valuable resources that could fulfil the requirements of malnourished people, the current research focusses on understanding consumerisms impact on this phenomenon. Additionally, the circular economy (CE) approach can be critical in reducing food waste and promoting sustainability. Design/methodology/approach: A systematic literature review was conducted using bibliometrics and network analysis. The study reviewed 326 articles within 10 years, from 2013 to 2023. Findings: The findings reveal four prominent factors behavioural, environmental, socioeconomic and technological in managing food waste (FW). Reducing FW at a holistic level can benefit individuals and the environment in several ways. Research limitations/implications: Consumers are encouraged to be more responsible for their food consumption by reducing food waste, as it affects societies and businesses both economically and environmentally. This can help promote a responsible consumption culture that values quality over quantity and encourages people to make more informed choices about what they eat and how they dispose of it post-consumption. All stakeholders, including firms, the government and consumers, must examine the motives behind inculcating pro-environmental behaviour. Originality/value: Addressing consumerism and the ability to decrease FW behaviour are complex issues that require a multidimensional approach. This study seeks to fill the gap in understanding consumerism and the capacity to reduce FW using the CE approach and understand the research gaps and future research trends. 2024, Emerald Publishing Limited. -
Siamese-Based Architecture for Cross-Lingual Plagiarism Detection in English-Hindi Language Pairs
The cross-lingual plagiarism detection (CLPD) is a challenging problem in natural language processing. Cross-lingual plagiarism is when a text is translated from any other language and used as it is without proper acknowledgment. Most of the existing methods provide good results for monolingual plagiarism detection, whereas the performances of existing methods for the CLPD are very limited. The reason for this is that it is difficult to represent the text from two different languages in a common semantic space. In this article, a novel Siamese architecture-based model is proposed to detect the cross-lingual plagiarism in English-Hindi language pairs. The proposed model combines the convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM) network to learn the semantic similarity among the cross-lingual sentences for the English-Hindi language pairs. In the proposed model, the CNN model learns the local context of words, whereas the Bi-LSTM model learns the global context of sentences in forward and backward directions. The performances of the proposed models are evaluated on the benchmark data set, that is, Microsoft paraphrase corpus, which is converted in the English-Hindi language pairs. The proposed model outperforms other models giving 67%, 72%, and 67% weighted average precision, recall, and F1-measure scores. The experimental results show the effectiveness of the proposed models over the baseline models because the proposed model is very efficient in representing the cross-lingual text very efficiently. Copyright 2023, Mary Ann Liebert, Inc., publishers 2023.