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Comparative Analysis of State-of-the-Art Face Recognition Models: FaceNet, ArcFace, and OpenFace Using Image Classification Metrics
In recent years, facial recognition has emerged as a key technological advancement with numerous useful applications in numerous industries. FaceNet, ArcFace, and OpenFace are three widely used techniques for facial identification. In this study, we examined the accuracy, speed, and capacity to manage variations in face expression, illumination, and occlusion of these three approaches over a period of five years, from 2018 to 2023. According to our findings, FaceNet is more accurate than ArcFace and OpenFace, even under difficult circumstances like shifting lighting and facial occlusion. Also, during the previous five years, FaceNet has shown a significant improvement in performance. Even while ArcFace and OpenFace have made significant strides, they still lag behind FaceNet in terms of accuracy. Therefore, based on our findings, we conclude that FaceNet is the most effective method for facial recognition and is well-suited for use in high-stakes applications where accuracy is crucial. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
A Novel AI-Based Stock Market Prediction Using Machine Learning Algorithm
The time series forecasting system can be used for investments in a safe environment with minimized chances of loss. The Holt-Winters algorithm followed various procedures and observed the multiple factors applied to the neural network. The final module helps filter the system to predict the various factors and provides a rating for the system. This research work uses real-time dataset of fifteen stocks as input into the system and, based on the data, predicts or forecasts future stock prices of different companies belonging to different sectors. The dataset includes approximately fifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not; the forecasting will give an accurate result for the customer investments. 2022 Iyyappan. M et al. -
A Component Selection Framework of Cohesion and Coupling Metrics
Component-based software engineering is concerned with the development of software that can satisfy the customer prerequisites through reuse or independent development. Coupling and cohesion measurements are primarily used to analyse the better software design quality, increase the reliability and reduced system software complexity. The complexity measurement of cohesion and coupling component to analyze the relationship between the component module. In this paper, proposed the component selection framework of Hexa-oval optimization algorithm for selecting the suitable components from the repository. It measures the interface density modules of coupling and cohesion in a modular software system. This cohesion measurement has been taken into two parameters for analyzing the result of complexity, with the help of low cohesion and high cohesion. In coupling measures between the component of inside parameters and outside parameters. The final process of coupling and cohesion, the measured values were used for the average calculation of components parameter. This paper measures the complexity of direct and indirect interaction among the component as well as the proposed algorithm selecting the optimal component for the repository. The better result is observed for high cohesion and low coupling in component-based software engineering. 2022 CRL Publishing. All rights reserved. -
The fractional-order marriagedivorce mathematical model: numerical investigations and dynamical analysis
In this research, we present a fractional-order mathematical model that simulates the ongoing phenomenon of marriage and divorce, which significantly impacts human lives. The model is defined within the LiouvilleCaputo framework and incorporates different states and parameter values. Specifically, the model consists of four state variables that describe the statuses of married, divorced, or troubled couples, as well as the interactions between these states. We conduct a stability analysis for the proposed model and calculate the equilibrium points, revealing the conditions for a stable solution. Moreover, we establish the existence, uniqueness, and boundedness of the models solution using the Banach fixed point theorem. The global stability of the obtained equilibrium points is investigated using the Lyapunov function, revealing a globally stable solution with specific conditions. To obtain the results, we employ a multi-domain collocation technique that utilizes orthogonal generalized Romanovski polynomials. These polynomials offer the advantage of achieving higher-order accuracy without requiring a large number of basis functions. We assess the accuracy of the proposed technique by calculating the residual error. Furthermore, we conduct simulations with various parameter values and fractional orders to demonstrate the techniques effectiveness under different scenarios. The results confirm the techniques ability to provide accurate outcomes across multiple scenarios. To enhance result validation, a comparison with real data from the USA is introduced for both married and divorced individuals. This elucidates the significance of the proposed fractional model in providing a deeper understanding of the dynamics and behavior of the solution and its impact on the populations lives. By introducing and analyzing this model, this research contributes to a deeper understanding of the dynamics and implications of marriage and divorce phenomena. The Author(s), under exclusive licence to SocietItaliana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Mathematical and computational analysis of a fractional-order drug abuse model with nonlinear incidence and logistic growth
This paper presents a novel mathematical model for analyzing the dynamics of drug addiction using a fractional-order system based on the LiouvilleCaputo derivative. The proposed model incorporates a nonlinear saturated incidence rate, logistic growth in the addiction compartments, and seven interconnected subpopulations representing different stages of drug use and recovery, including relapse and awareness. We conduct a rigorous mathematical analysis to establish the existence, uniqueness, positivity, and boundedness of solutions, ensuring the epidemiological and physical validity of the model. The basic reproduction number R0 is derived, and the local and global stability of the equilibrium points is analyzed. A major contribution of this work is the application of a new domain decomposition spectral method based on second-kind Dickson polynomials, combined with the quasilinearization technique, to efficiently solve the complex nonlinear system. The convergence of the numerical method is theoretically validated. Numerical simulations are provided to illustrate the model's dynamics and to explore the impact of various parameters and intervention strategies. Compared to existing models, this study offers an improved framework for understanding memory-dependent behavior in addiction dynamics and introduces a computationally efficient approach to solve fractional-order systems with high accuracy. 2025 International Association for Mathematics and Computers in Simulation (IMACS) -
Effect of Waste Materials in Partial Replacement of Cement Fine Aggregate and Course Aggregate in Concrete
International Journal of Inventive Engineering and Sciences (IJIES), Vol.2, Issue 4, ISSN: 2319-9598 -
Flexible Teaching - Learning Systems in Indian Higher Education System through Technology - A Study on Demographic Impact
International Journal of Computer Application Vol. 5, Issue 2, pp 178-182, ISSN No. 2250-1797 -
Synergistic enhancement of bifunctionality in Ni-doped VO2 (B) nanostructures: A pathway for improved water electrocatalysis
The global energy demand led to the exploration of techniques to produce green hydrogen, such as water electrolysis, as a future fuel for energy management. However, the efficiency of hydrogen evolution (HER) during this electrocatalysis is usually regulated by the proficiency of the catalyst in generating a facile oxygen evolution reaction (OER). In this work, the robust bifunctionality of Ni-doped VO2(B) as an electrocatalyst for hydrogen and oxygen evolution reactions is investigated. The optical characterization uncovered the semiconducting nature of nanoflake-like VO2(B) nanostructures. Furthermore, optimizing Ni concentration resulted in significant reduction of overpotentials from 518 mV to 289 mV for HER and from 435 mV to 404 mV for OER under a current density of 10 mA/cm2. This excellent electrochemical efficiency of the Ni-doped VO2(B) nanostructures is further showcased by the low Tafel slope values of 129 mV/dec and 99 mV/dec for HER and OER, respectively. Consistent with these findings, the materials exhibited minimal charge transfer resistance of 57.1 ?, reinforcing the superior electrocatalytic activity. Additionally, the chronopotentiometric studies confirmed the long-term stability of the nanostructures. Altogether, Ni-doped VO2(B) nanostructures can be considered as a potential electrocatalyst for overall water electrolysis, laying the foundation for a sustainable energy future. 2025 Elsevier B.V. -
Natural template-assisted green synthesis of cobalt oxide and its surface functionalization using ?-alanine for biological applications
The incorporation of nanotechnology into material science has brought great advancements in diverse fields like medicine, electronics, energy, and the environment. Metal oxides gained notable attention from various nanomaterials due to their unique structure and properties. Cobalt oxide nanoparticles (Co3O4) stand out especially due to their diverse properties and applications. Synthesis of metal oxides through the traditional method faces many drawbacks, such as the use of toxic chemicals, a complex procedure, and environmental and health impacts. Whereas the green method of synthesis using natural resources, followed by surface modification with amino acids, acts as a better option for metal oxide synthesis. This paper focuses on developing a green, sustainable, and scalable method for synthesising Co3O4 nanoparticles, using a natural template, gum Arabic, followed by surface functionalization of ?-alanine. Various physico-chemical characterisation techniques such as DLS, TEM, FTIR and XRD were used to study nanoparticle composition and properties. Biocompatibility studies, cell viability assays and fibroblast cell lines from human skin by Alamar Blue assay, were carried out to test the effects of synthesised nanoparticles, and optimised protocols were also used to enhance performance for particular biomedical applications. Incorporating green synthesis and advanced techniques, ?-alanine functionalized Co3O4 nanoparticles, this research points toward developing more stable, biocompatible, and reactive nanoparticles under biological conditions. and multifunctional Co3O4 nanomaterials. Overall, the current study aims at sustainability with innovation towards transformative various biological applications in healthcare, biomedicine, diagnostics, MRI, biosensors, photo-sensing agents and energy technologies while addressing significant gaps in present methodologies. 2025 Elsevier B.V. -
A comprehensive review on bifunctional catalysts for efficient electrochemical hydrogen production from wastewater
The authors regret the wrong project number in the acknowledgement part, which has been renewed this year in the Korea's side. It is an important issue since the achievement of the funded project requires a correct project number during assessment. Therefore, the authors sincerely hope that the editor could kindly consider to change the content (project number) in the acknowledgement part as follow: Old number: New number: < This work was supported by the SEED money scheme, CHRIST University, Bangalore (CU-ORS-SM-24/32) and National Research Foundation of Korea (Project: RS-202400343361) The authors would like to deeply apologise for any inconvenience caused. 2025 Hydrogen Energy Publications LLC -
Multimodal learning for autonomous systems and robotics
The realm of autonomous systems and robotics is experiencing a paradigm shift driven by the integration of advanced artificial intelligence (AI) techniques and multimodal learning approaches. This abstract explores the latest advancements and research topics that are propelling the field toward more intelligent, efficient, and versatile autonomous systems. Multimodal learning leverages multiple sensory inputs to enhance the perception and decision-making capabilities of autonomous systems. This involves the integration of visual, auditory, tactile, and other sensory data to form a coherent understanding of the environment. Deep learning techniques, such as multimodal neural networks and crossmodal embeddings, play a pivotal role in this integration, enabling the system to learn joint representations and improve robustness in perception under varying conditions. Computer vision remains a cornerstone of autonomous systems, with advancements in techniques such as real-time object detection, tracking, and high-resolution image synthesis through generative adversarial networks. Vision-based reinforcement learning is also gaining traction, enabling systems to learn from visual inputs and improve their decision-making processes in dynamic environments. The integration of advanced sensors, including high-resolution light detection and ranging, radio detection and ranging, and event-based cameras, enhances the capability of autonomous systems to perceive their surroundings accurately. Multisensor data fusion, using methods like Kalman and particle filters, ensures robust perception even in adverse conditions, providing a comprehensive view of the environment. Innovations in actuation and control systems are fundamental for the development of responsive and adaptive robots. Soft robotics, inspired by biological systems, offers new possibilities in design, modeling, and control. Hybrid control systems facilitate the coordination of multimodal actuation, enhancing the robots versatility and performance. The deployment of high-performance embedded systems, incorporating heterogeneous computing architectures (CPU-GPU-FPGA integration), is vital for real-time data processing and decision-making. Neuromorphic computing and AI hardware accelerators provide low-power solutions that are crucial for the efficiency of autonomous systems. Techniques for uncertainty estimation, outlier detection, and anomaly detection are essential for maintaining system reliability. Advanced robotic perception and cognition, combined with cognitive architectures for autonomous reasoning, enable systems to operate safely in complex and dynamic environments. The interface between humans and robots is evolving, with a focus on multimodal human-robot interaction. Learning from human demonstrations and ensuring safety and trust in human-robot teams are critical areas of research, promoting effective collaboration between humans and robots. Advanced simulation techniques, including high-fidelity physics-based simulations and domain randomization, are employed to test and validate autonomous systems. Virtual reality and augmented reality provide immersive environments for training and testing. Real-time simulation and hardware-in-the-loop testing ensure the robustness and reliability of autonomous systems before deployment. Ethical AI and autonomous decision-making frameworks are being developed to address these issues. Privacy-preserving machine learning techniques and cybersecurity measures are essential for protecting sensitive data and ensuring the security of autonomous systems. This comprehensive overview underscores the rapid advancements and multifaceted nature of multimodal learning and autonomous systems, heralding a new era of intelligent and adaptive robotics capable of transforming numerous industries and improving the quality of human life. 2026 Elsevier Inc. All rights reserved. -
Generalized p-Transmuted Neutrosophic Distributions: Theory and its Applications
The study of neutrosophy offers a fresh approach for handling uncertain data with adaptability. This article explores the application of neutrosophic probability distribution in constructing a transmuted neutrosophic framework. Specifically, it introduces a generalized transmuted neutrosophic distribution. Building upon this generalization, quadratic and cubic transmuted distributions are developed and examined alongside certain lifetime distributions serving as foundational neutrosophic models. Additionally, an empirical investigation is conducted to assess the practicality and versatility of these distributions in real-world contexts. 2024, American Scientific Publishing Group (ASPG). All rights reserved. -
Forbidden Cravings: Exploring socio-cultural ramifications of food practices in Aamis
Food choices represent conscious affirmation and expression of personal, group, ethnic or national identity. Due to its multidimensional role, food that we rely on sustenance is often politicised and used as a tool to create conflict amongst and within diverse social groups. Assamese cuisine includes a rich platter of authentic food varieties, often limited to the north-eastern region. Although food consumption is a subjective experience, cultural taboos within a community might be acceptable practices in another culture, creating conflicting notions of food practices. The balance between the twin axis of culture and politics regarding food is disrupted when heterogeneous cultural patterns and opposing political notions are in discord. Similarly, the solidarity within a cultural group becomes hostile when the authority of the individual concerning food choices is not aligned with the authority of the social structure. This discord from a political and cultural standpoint is evident in the Assamese socio-cultural scenario. Taking Bhaskar Hazarika's Ravening/Aamis (2019) as a case study, this paper proposes to analyse the representational troupe of food, through a structuralist anthropological lens, with respect to food politics to understand socio-cultural ramifications of Assamese food patterns. 2022 Aesthetics Media Services. All rights reserved. -
Synthesis, characterization and antimicrobial studies of novel Schiff bases and their complexes
Novel Schiff bases, Furan-2-carboxylic acid pyridin-4-ylmethyleneamide, and Thiophene-2-carboxylic acid 1H-indol-2-ylmethyleneamide and their mononuclear Ni(II) and Cu(II) complexes have been synthesized and characterized by elemental analysis, molar conductance, UV-visible, FT-IR, 1H NMR and EPR spectroscopy. The complexes are non-electrolytes as evidenced from the molar conductance vaules. The ligands and their complexes have been tested for their antimicrobial activity against one gram positive bacteria, Bacillus subtilis, gram negative bacteria, Escherichia coli and fungi Candida albicans. It is found that metal complexes exhibited more activity than the free ligand. 2021 Scientific Publishers. All rights reserved. -
Automated Single Responsibility Principle Enforcement: A Step Toward Reusable and Maintainable Code
In this study, we delve into the sphere of automated code scrutiny, specifically concentrating on compliance with the single responsibility principle (SRP), a key principle in software architecture. The SRP proposes that a class should have a singular reason for modification, thereby enhancing code cohesion and facilitating its maintenance and reusability. The study presents a pioneering system that utilizes a holistic strategy to ascertain SRP compliance within code. This system rigorously inspects code interfaces, the interaction points among various software components. Through this process, we extract critical insights into the codes maintainability and reusability. An optimally designed interface can significantly improve code management and foster its reuse, leading to superior software design efficiency. Beyond interface inspection, our system also explores complexity metrics such as cyclomatic complexity and hassel volume. Cyclomatic complexity offers a numerical indicator of the count of linearly independent paths traversing a programs source code, serving as a measure of code complexity. Hassel volume is an additional metric that can quantify code complexity. Moreover, our system employs code smell detection methodologies to identify instances of high interdependence between classes, often a sign of SRP breaches. High interdependence, or tight coupling, complicates code modification and maintenance. The system integrates the conclusions from these varied analyses to determine SRP compliance. The outcomes of this investigation highlight a hopeful trajectory toward automated SRP detection. This could provide developers with tools that proactively foster the development of well-organized and maintainable code, thereby enhancing software design quality. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Emerging Nanomaterials as Versatile Nanozymes: A New Dimension in Biomedical Research
The enzyme-mimicking nature of versatile nanomaterials proposes a new class of materials categorized as nano-enzymes, ornanozymes. They are artificial enzymes fabricated by functionalizing nanomaterials to generate active sites that can mimic enzyme-like functions. Materials extend from metals and oxides to inorganic nanoparticles possessing intrinsic enzyme-like properties. High cost, low stability, difficulty in separation, reusability, and storage issues of natural enzymes can be well addressed by nanozymes. Since 2007, more than 100 nanozymes have been reported that mimic enzymes like peroxidase, oxidase, catalase, protease, nuclease, hydrolase, superoxide dismutase, etc. In addition, several nanozymes can also exhibit multi-enzyme properties. Vast applications have been reported by exploiting the chemical, optical, and physiochemical properties offered by nanozymes. This review focuses on the reported nanozymes fabricated from a variety of materials along with their enzyme-mimicking activity involving tuning of materials such as metal nanoparticles (NPs), metal-oxide NPs, metalorganic framework (MOF), covalent organic framework (COF), and carbon-based NPs. Furthermore, diverse applications of nanozymes in biomedical research are discussed in detail. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Breast cancer detection: A comparative review on passive and active thermography
Breast cancer is the main cause of death among women due to cancer. Early detection is crucial in controlling the disease. Thermography is a non-invasive imaging method that uses temperature differences on the breast surface to identify tumors. This paper focuses on the various aspects of thermography as a diagnostic tool for detecting breast cancer. It includes a review of the currently existing active thermography approaches used to energize the tumor cell to enhance the thermal contrast on the surface. The comparison of passive and active thermography showed that active thermography was more effective, increasing depth-dependent performance from 3 mm to 9 mm for 1.5 mm sized tumors and accuracy from 54% to 82% without a rise in false positive rates. The contrast between malignant and benign tissue also improved from 0.6 C to 0.9 C, indicating that active thermography increases the performance of passive thermography in various aspects. A comparative study of active thermography reveals that healthy tissues are likely to be damaged if the input parameters are not regulated properly. A comprehensive comparison of various tumor estimation algorithms in the paper concludes that the dynamic analysis using an active approach outperforms static analysis due to a significant decrease in error percentage. 2023 Elsevier B.V. -
Young consumers' green marketing orientation: role of customer citizenship behaviour in determining real estate purchase intention in India
Purpose: The purpose of this paper is to assess green marketing as an influential factor in the purchase of real estate. In this study, the consumer citizenship behaviour of young individuals will be studied with an assessment of real estate purchase intention, strategic green marketing orientation (GMO) and tactical GMO. Design/methodology/approach: The sample frame consists of young consumers from Indias metropolitan cities. The respondents were in the range of 18 to 35 years of age. These cities provide a high standard of living, more career options and better educational opportunities. Five separate sections of a standardised questionnaire were used, and a structural equation model was used to assess a total of 393 valid replies. Findings: Green marketing impacts the behaviour that influences the desire of young customer to buy green real estate. Research limitations/implications: As the focus of this study is primarily on major cities, future research may study similar behaviour in non-metropolitan cities. The study can also be conducted among consumers of other age groups. Originality/value: The originality, to the best of the authors knowledge, exists in examining how young consumers opinions about green marketing impact their intentions to purchase green houses and real estate in India. This study will be accessible to all parties involved in the housing and real estate industries. 2023, Emerald Publishing Limited. -
A Framework for Digital Forensics Using Blockchain to Secure Digital Data
Digital forensics (DF) requires evidence integrity and provenance across boundaries of jurisdiction, and blockchain technology is ideal for ensuring that. As part of this paper, we discussed a digital forensic framework designed to help prevent duplication of data and secure digital data. In order to accomplish such forensic capabilities, we provide a block-based forensics framework. Using it, examinations are validated, irreversible, traceable, robust, and demonstrate high levels of confidence among examiners and evidence entities. 2022 IEEE. -
Forecasting of Environmental Sustainability through Green Innovation of E-Vehicle Industry
E-mobility sustainability forecasting is getting more detailed with study, taking into account social cost in addition to technological, economic, or environmental factors. One solution for reducing greenhouse gas emissions is to implement green innovation in the transportation sector. The citizenrys view and acceptance of electric cars must be improved, more research into the social cost of these innovations is required. Consequently, the transportation industry might decarbonize more quickly. Another approach to do it is to advocate for more all-encompassing green innovations that can enhance sustainable development. Using Our Common Future, published in 1987 by the World Commission on Environment and Development [1], the commission emphasized the importance of sustainability while integrating social and economic development. Additionally, it recommended that governments take environmental factors into account while making decisions. The significance of sustainability was then increased and institutionalized, which meant that nations began passing laws that promoted sustainability. Consumer awareness of sustainability is rising largely from an economic and environmental standpoint. This also has an impact on the transportation industry and poses significant environmental, social, and economic difficulties. However, given that it generates close to 5% of the GDP and employs almost 11 million people, transportation is crucial from an economic standpoint. 2024 Sachi Nandan Mohanty, Preethi Nanjundan and Tejaswini Kar.