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Deep Convolutional Neural Networks Network with Transfer Learning for Image-Based Malware Analysis
The complexity of classifying malware is high since it may take many forms and is constantly changing. With the help of transfer learning and easy access to massive data, neural networks may be able to easily manage this problem. This exploratory work aspires to swiftly and precisely classify malware shown as grayscale images into their various families. The VGG-16 model, which had already been trained, was used together with a learning algorithm, and the resulting accuracy was 88.40%. Additionally, the Inception-V3 algorithm for classifying malicious images into family members did significantly improve their unique approach when compared with the ResNet-50. The proposed model developed using a convolution neural network outperformed the others and correctly identified malware classification 94.7% of the time. Obtaining an F1-score of 0.93, our model outperformed the industry-standard VGG-16, ResNet-50, and Inception-V3. When VGG-16 was tuned incorrectly, however, it lost many of its parameters and performed poorly. Overall, the malware classification problem is eased by the approach of converting it to images and then classifying the generated images. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Edge, IIoT with AI: Transforming industrial engineering and minimising security threat
[No abstract available] -
Sexual health and safety of adolescents with intellectual disability: Challenges and concerns among special educators in India
Sexual health education among adolescents with intellectual disabilities is an area of concern among parents and educators. Special educators play a vital role in teaching sexual health and safety to their students with disabilities. The present study explores special educators' concerns in teaching sexual health among adolescents with intellectual disabilities. The participants included 35 special educators working with adolescents with intellectual disabilities in India. Summative content analysis of the data collected using a semi-structured interview schedule highlights the neglect of the issues related to sexual health among adolescents with disabilities. Special educators reported the challenges in providing sexual health education. Teachers lacked skills in imparting sexual health education. Developmentally and culturally appropriate sexual health education can help adolescents with a disability learn healthy life skills. The paper emphasizes the need for professional support and training among special educators on sexual health education. The Author(s) 2022. -
Digital platforms for business applications
[No abstract available] -
Internet of Things-Based Smart Agriculture Advisory System
The Internet era provides a lot of automation tools for data analysis, and it is the need of the hour to develop new analytical tools to manage the big data. For task automation, machine learning and expert systems are of primary importance to study the behavior of computer thinking to involve computers in sensible work, known as computational intelligence. The data involves varied formats such as structured, unstructured, as well as semi-structured, and it is an automation tool that uses computational intelligence to extract valid and potential information from the sources. The specific purpose of this proposed work is to meet out computing demands which highly rely on computational intelligence. Computational intelligenceinvolves the design and deployment of an analytical tool for multidimensional data analytics. The proposed integrated framework focuses on multidimensional data analytics, for crop and plant data, especially plants that contain medicinal values and components. This research works main aim is to create a secured data tool for agriculture crop data management through big data (crops and plants) analytics. The data security is enhanced through applied cryptography, and the final phase prediction on crops is done by various machine and deep learning algorithms. The specific objective of this research work is to help farmers in making informed decisions for the enhancement of cultivation and information. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
A Comprehensive Comparison of MobileNet, ResNet50 and InceptionV3 for Efficient Plant Pathology Detection
Plant diseases have a strong impact on agricultural productivity due to economic factors and a reduction in crop quality. This work focuses on the classification of apple leaves into four classes: healthy, rust-infected, scab-infected, and infected by both diseases, using the Plant Pathology FGVC7-2020 dataset that contains 3,642 images in total. The work involves the analysis of three sophisticated deep learning architectures: ResNet50, MobileNet and InceptionV3.It turned out that MobileN et showed the highest performance, reaching a 92% accuracy rate; it was followed by ResNet50 with 75% accuracy and InceptionV3 at 73%, hence underlining its better generalization capability and efficiency in classifying. We discuss the proposed methodology, which includes data preprocessing techniques, experimental results and final conclusions, is discussed in detail. These results underline the fundamental importance of determining an appropriate neural network architecture for the recognition of plant diseases, which is of prime importance to improve agricultural productivity. 2025 IEEE. -
Role of Perceived Social Support and Sense of Coherence Towards Quality of Life Among People Seeking Treatment for Substance Use
A significant portion of individuals diagnosed with substance use disorder confront societal neglect and escalating stigma. This heightened pressure contributes to increased stress and the emergence of comorbid psychiatric conditions alongside their existing affliction. Considering it to be a significant untouched area, the current empirical research was undertaken to explore the role of psychological factors towards quality of life among people seeking treatment for substance use. To accomplish the proposed objectives a correlation design had been adopted and standardized psychological measures were administered on a sample of 100 participants who were registered under residential rehabilitation programs. The data was analyzed using SPSS statistical software and the findings revealed a significant positive correlation of perceived social support and a sense of coherence with quality of life, i.e., r = 0.72 (p <.001) and r = 0.68 (p <.001), respectively. Further, perceived social support and sense of coherence also emerged as predictors (R2 = 55%) of quality of life among people seeking treatment for substance use. Findings provide strong advocacy that people seeking treatment for substance use should be provided with social support which in turn helps them to hold a sense of coherence and ultimately both contributes to their overall quality of life. 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. -
Role of Perceived Social Support and Sense of Coherence Towards Quality of Life Among People Seeking Treatment for Substance Use
A significant portion of individuals diagnosed with substance use disorder confront societal neglect and escalating stigma. This heightened pressure contributes to increased stress and the emergence of comorbid psychiatric conditions alongside their existing affliction. Considering it to be a significant untouched area, the current empirical research was undertaken to explore the role of psychological factors towards quality of life among people seeking treatment for substance use. To accomplish the proposed objectives a correlation design had been adopted and standardized psychological measures were administered on a sample of 100 participants who were registered under residential rehabilitation programs. The data was analyzed using SPSS statistical software and the findings revealed a significant positive correlation of perceived social support and a sense of coherence with quality of life, i.e., r = 0.72 (p <.001) and r = 0.68 (p <.001), respectively. Further, perceived social support and sense of coherence also emerged as predictors (R2 = 55%) of quality of life among people seeking treatment for substance use. Findings provide strong advocacy that people seeking treatment for substance use should be provided with social support which in turn helps them to hold a sense of coherence and ultimately both contributes to their overall quality of life. 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. -
Silicon photonic modulators for high-speed applications-a review
[No abstract available] -
Radiative heat transport and unsteady flow in an irregular channel with aggregation kinematics of nanofluid
In this study, an unsteady free convective heat transfer and the laminar flowof incompressible nanoliquid in a wavy channel subjected to the nanoparticles aggregation effects were studied. For the investigation, ethylene glycol-based nanofluid with titania nanoparticles was used. Here, the role of the nanoparticle aggregation, thermal radiation, applied magnetic field, and internal heat absorption is examined. A semi-analytical solution of the complicated partial differential equation is obtained by the method of regular perturbation. The effect of several parameters on velocity and temperature profile has been studied. In addition, Nusselt number (Nu) and skin friction (Formula presented.) are also examined and analyzed with the help of graphs. It has been observed that the velocity profile enhances with aggregation effect than without aggregation effect. The aggregation effects are minimal for smaller volume fraction of nanoparticles. A reverse trend near the wavy wall is visible for all parameters. The magnitude of velocity decreased as an effect of the applied magnetic field, whilethe magnitude of velocity increased due to radiative heat flux. Furthermore, the heat sink mechanism reduces the magnitude of the nanofluid temperature. 2021 Wiley Periodicals LLC -
Exploring various nanomaterials in enhancing the performance of chiral nematic liquid crystal for blue phase display
This study aims to develop composite liquid crystal (LC) materials for energy-efficient blue phase (BP) display applications with enhanced luminescent and dielectric properties. Chiral nematic liquid crystal (CNLC) was systematically doped with nanomaterials, including nickel zinc ferrite (NZFO), single-walled carbon nanotubes (SWCNT), gold nanoparticles (GNPs), and strontium titanate (SrTiO3). Optimal doping concentrations (0.05 wt% for NZFO and SWCNT; 0.1 wt% for GNPs) enhanced photoluminescence, while SrTiO3 served as a luminescence quencher. Dielectric studies revealed a substantial reduction in the Freedericksz transition threshold voltage, particularly with NZFO (0.05 wt%), which halved the voltage. Optical texture and structural analysis confirmed that the CNLC structure remain intact while maintaining the BP temperature window (12 C). The reduced splay elastic constant in all doped CNLC revealed that the optimum quantity of nanomaterials is occupied in the disclination site of BP, resulting in a reduction of volume and associated free energy around the disclinations to reduce threshold voltage. These findings highlight the potential of nanomaterial-doped CNLCs, especially magnetic NZFO NPs, in enabling high-performance, low-power BP-based LC displays for advanced applications. 2025 Elsevier B.V. -
Exploring various nanomaterials in enhancing the performance of chiral nematic liquid crystal for blue phase display
This study aims to develop composite liquid crystal (LC) materials for energy-efficient blue phase (BP) display applications with enhanced luminescent and dielectric properties. Chiral nematic liquid crystal (CNLC) was systematically doped with nanomaterials, including nickel zinc ferrite (NZFO), single-walled carbon nanotubes (SWCNT), gold nanoparticles (GNPs), and strontium titanate (SrTiO3). Optimal doping concentrations (0.05 wt% for NZFO and SWCNT; 0.1 wt% for GNPs) enhanced photoluminescence, while SrTiO3 served as a luminescence quencher. Dielectric studies revealed a substantial reduction in the Freedericksz transition threshold voltage, particularly with NZFO (0.05 wt%), which halved the voltage. Optical texture and structural analysis confirmed that the CNLC structure remain intact while maintaining the BP temperature window (12 C). The reduced splay elastic constant in all doped CNLC revealed that the optimum quantity of nanomaterials is occupied in the disclination site of BP, resulting in a reduction of volume and associated free energy around the disclinations to reduce threshold voltage. These findings highlight the potential of nanomaterial-doped CNLCs, especially magnetic NZFO NPs, in enabling high-performance, low-power BP-based LC displays for advanced applications. 2025 Elsevier B.V. -
Corrosion mitigation performance of disodium EDTA functionalized chitosan biomacromolecule - Experimental and theoretical approach
Disodium ethylenediaminetetraacetate salt is known for its excellent coordinating properties with the metal ions. The present study deals with the investigation of the prepared Disodium EDTA functionalized chitosan in corrosion inhibition for mild steel in 1 M HCl. The modified chitosan was characterized by spectral studies, thermal analysis, and Zeta potential studies. The corrosion inhibition efficiency (%) was evaluated using the gravimetric method and electrochemical studies. The electrochemical studies included potentiodynamic polarization, linear polarization resistance, and electrochemical impedance methods. The modified chitosan polymer showed an inhibition efficiency of 96.63% for 500 ppm at 303 K. Adsorption process obeyed Langmuir isotherm. Experimental results and theoretical calculations endorsed initial physisorption followed by a chemisorption process. Surface characterization studies supported the formation of a protective film that enabled the inhibition process. Density functional theory, Monte Carlo studies, and molecular dynamics simulation studies show a good agreement with the experimental results. Two-way Analysis of Variance was performed to test the influence of immersion period and inhibitor concentration on the corrosion rate using the statistical software IBM SPSS 20.0. A quartic model was generated as the best fit with the highest R2 value of 0.973. Design Expert software was employed for statistical modeling fit. 2021 -
Experimental and Theoretical Approach of Evaluating Chitosan Ferulic Acid Amide as an Effective Corrosion Inhibitor
Phenolic acid grafted chitosan has widespread drug delivery applications, as bio adsorbent, packing material, etc., due to its excellent antioxidant and antimicrobial properties. However, for the first time, the anticorrosive efficiency of ferulic acid modified chitosan has been investigated. The prepared chitosan derivative is characterized using spectral methods, thermal analytical methods, surface charge, and particle size analysis. The evaluation of corrosion inhibition potential showed a highest value of 95.96% at 303K. Thermodynamic activation and adsorption parameters endorse a mixed adsorption process involving an initial electrostatic interaction followed by chemisorption. Electrochemical studies gave results which agreed well with the gravimetric studies. Surface morphological studies were performed using contact angle measurements, FESEM, EDAX, AFM, optical profilometric and UV spectral techniques. Computational studies involving quantum chemical calculations, Monte Carlo and molecular dynamic simulation studies, and radial distribution function analysis are further done to validate the experimental results. Graphical Abstract: [Figure not available: see fulltext.] 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG. -
Sunova spirulina Powder as an Effective Environmentally Friendly Corrosion Inhibitor for Mild Steel in Acid Medium
Abstract: Spirulina, blue green algae is a rich source of proteins and vitamins with excellent antioxidant properties. Sunova spirulina powder an effective, green corrosion inhibitor was used to evaluate its inhibition efficiency towards mild steel in 1M HCl medium. Weight loss studies of mild steel showed an inhibition efficiency of 96% for 600ppm concentration of inhibitor solution and 12h of immersion period at 303K. The percentage of inhibition efficiency increased with a step up of 10K raise in temperature from 303 to 333K and thereafter decreased. The results obtained were further validated by inductively coupled plasma optical emission spectrometric (ICP-OES) measurements and electrochemical techniques that included Tafel polarisation, linear polarisation and AC impedance studies. Potentiodynamic polarisation study marked the inhibitor to be a mixed type inhibiting both cathodic and anodic reactions. The adsorption studies proved that the adsorption process was spontaneous and followed Langmuir adsorption isotherm. The thermodynamic activation and adsorption parameters calculated showed that the mechanism of inhibition involved a physisorption process initially and then it slightly shifted towards chemisorption process at higher temperature. The protective layer formed on the metal surface was studied using FTIR and SEM. The complex formation between the Fe2+ and the active constituents of the spirulina extract was verified using UV visible spectra and fluorescence spectra. The effect of inhibitor concentration and temperature on corrosion rate was tested statistically using two-way analysis of variance (ANOVA) technique. Graphic Abstract: [Figure not available: see fulltext.]. 2020, Springer Nature Switzerland AG. -
Preparation, characterization, and evaluation of corrosion inhibition efficiency of sodium lauryl sulfate modified chitosan for mild steel in the acid pickling process
The polar head and a hydrophobic long alkyl chain end of surfactants show effective adsorption on the metal surfaces and metal/solution interfaces. The present study deals with the investigation of corrosion inhibition efficiency of chitosan modified with an anionic surfactant, namely sodium lauryl sulfate. The modified chitosan was characterized using spectral techniques such as ATR- FTIR and NMR, thermal analytical methods that include TGA and DSC. The surface charge and particle size distribution were analyzed using Zeta potential analyzer. The corrosion inhibition efficiency of the water-soluble modified chitosan was evaluated using gravimetric and electrochemical methods. A maximum corrosion inhibition efficiency of 96.44% for 6 h of immersion period at 303 K was obtained. The adsorption process obeyed Langmuir isotherm. The adsorption mechanism involved both physisorption and chemisorption. Tafel and impedance studies showed results in agreement with the gravimetric method. Tafel plot indicates the inhibitor controlled both cathodic hydrogen evolution and anodic metal dissolution reactions. AC impedance study supports the increase in surface coverage of the metal surface by the inhibitor, forming a protective film. Further evidence comes from the surface characterization of the inhibited metal surface by contact angle measurement, SEM, EDAX spectra, and atomic force microscopic studies. DFT and Monte Carlo simulation studies showed a proper alignment with the experiment results. 2020 Elsevier B.V. -
Mediating role of resilience on the relationship between meta emotions and emotional regulation among neglected adolescents
Background: Adolescence is a pivotal stage in human development, marked by significant biological, cognitive, and emotional transformations that shape an individual's character and future behaviour. The emotional strategies and behavioural patterns inculcated during this phase become integral aspects of one's personality, influencing how one navigates through various life challenges. For vulnerable adolescents, such as neglected children in institutional care or orphans, negative life experiences can heighten the risk of developing psychological concerns. This heightened vulnerability is often exacerbated by impaired emotional regulation and low resilience, which may contribute to the emergence of internalising disorders. Methods: The present study was conducted to examine the relationship between meta emotions and difficulties in emotional regulation among neglected adolescents and to analyse the mediating effect of resilience on this relationship. The participants of the study were 667 neglected adolescents: 335 males (50.2%) and 332 females (49.8%) who belong to the age group 13 to 17years (mean age = 14.60, S.D = 1.16) years from various institutional homes across Bangalore. Results: The results of this study confirm that in the presence of increased levels of negative meta emotions in an individual, despite the mediating effects of resilience, difficulties in emotional regulation will be further worsened. This was substantiated by the positive correlation between negative meta emotion and difficulties in emotional regulation. Contrastingly, elevated positive meta emotions in an individual, along with the mediating effects of resilience, reduce the difficulties in emotional regulation. This was also reinforced by the strong relationship between positive meta emotions and difficulties in emotion regulation. Conclusion: The findings of the study highlight the prevalence of increased negative meta emotions and poor emotional regulation among neglected adolescents, which is of utmost importance from a psychological, social and policy-making perspective. This calls for the need for tailored and individual-focused interventional strategies to improve the psychological health of these vulnerable children. Moreover, the critical period of adolescence is a crucial time for implementing effective policies in order to shape the behavioural and cognitive aspects of personality with much-needed care, support, and professional guidance. The Author(s) 2025. -
A Pipeline for Speech-to-Text Summarization and Question Identification for Enhanced Chatbot Interactions
The rapid advancements in natural language processing provide strong support for the new potential application of integrating Google Speech Recognition API, BART, and BERT to create a full pipeline for speech recognition, text summarization and question answering without breaking human interaction. The research aims to develop such a holistic pipeline involves integrating the Google Speech Recognition API to perform speech-to-text, BART for text summarization, and finally BERT for question answering based on both the summary and original transcript. The system was tested under various criteria such as testing accuracy, real-time processing performance, and stress tests for scalability where the findings include an average of 60% text compression with BART, an 88% accuracy in BERT-based question answering, and scores indicating high user satisfaction (4.3/5). Real-time processing latency can be able to cater to interaction within 2-3 seconds and the capacity of the system has proven without performance loss during simultaneous users. The research done can practically find applications in areas like intelligent virtual assistants, customer service automation and e-learning applications that improve accessibility and user experience. 2025 IEEE. -
Multimodal Emotion Recognition Using Deep Learning Techniques
Humans have the ability to perceive and depict a wide range of emotions. There are various models that can recognize seven primary emotions from facial expressions (joyful, gloomy, annoyed, dreadful, wonder, antipathy, and impartial). This can be accomplished by observing various activities such as facial muscle movements, speech, hand gestures, and so forth. Automatic emotion recognition is a significant issue that has been a hotly debated research topic in recent years. At the moment, several research people have taken a component in inheriting or extra multimodal for higher understanding. This paper indicates a method for emotion recognition that makes use of 3 modalities: facial images, audio indicators, and text detection from FER and CK+, RAVDESS, and Twitter tweets datasets, respectively. The CNN model achieved 66.67 percent on the FER-2013 dataset of labeled headshots while on the CK+ dataset, 98.4 percent accuracy was obtained. Finally, diverse fusion strategies had been approached, and each of those fusion techniques gave distinctive results. This project is a step towards the sense of interaction between human emotional aspects and the growing technology that is the future of development in today's world. 2022 IEEE.