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A STUDY ON UPPER DEG-CENTRIC GRAPHS
The upper deg-centric graph of a simple, connected graph G, denoted by Gud, is a graph constructed from G such that V (Gud) = V (G) and E(Gud) = {vivj : dG(vi, vj) ? degG(vi)}. This paper introduces and discusses the concepts of upper deg-centric graphs and iterated upper deg-centrication of a graph. I??k University, Department of Mathematics, 2025; all rights reserved. -
A STUDY ON LOWER DEG-CENTRIC GRAPHS
The lower deg-centric graph of a simple, connected graph G, denoted by Gld, is a graph constructed from G such that V (Gld) = V (G) and E(Gld) = {vivj: dG(vi, vj) < degG(vi)}. For a connected graph G of order n, the lower deg-centric graph Gld ?=Kn if and only if degG(vi) > eG(vi), for all vi ? V (G). In this paper, the concepts of lower deg-centric graphs and iterated lower deg-centrication of a graph are introduced and discussed. Palestine Polytechnic University-PPU 2025. -
A STUDY ON DEG-CENTRIC GRAPHS OF SOME GRAPH FAMILIES
The deg-centric graph of a simple, connected graph G, denoted by Gd, is a graph constructed from G such that, V (Gd) = V (G) and E(Gd) = {vivj: dG(vi, vj) ? degG(vi)}. This paper presents the properties and structural characteristics of deg-centric graphs of some graph families; the deg-centrication of graph operations are also discussed. Palestine Polytechnic University-PPU 2025. -
Integrated Automated Attendance System with RFID, Wi-Fi, and Visual Recognition Technology for Enhanced Classroom Security and Precise Monitoring
The integrated automated smart attendance system utilizes RFID, Wi-Fi, and visual recognition technologies to elevate classroom security and ensure precise monitoring of attendance records. It consolidates cutting-edge components such as RFID tags, ESP8266 Wi-Fi modules, ESP-32 CAM modules, solenoid locks, servo motors, and PIR sensors to devise a strong remedy. RFID technology enables accurate attendance tracking by assigning tags to students and faculty members. The Wi-Fi and visual recognition components enhance the system's functionalities, facilitating wireless connectivity, instantaneous data transfer, and validation of identities. Solenoid locks and servo motors ensure controlled access, responding to validated attendance records. PIR sensors detect motion, contrasting between genuine presence and proximity. The paper's methodology delineates the necessary hardware and software requirements, procedures for system initialization, testing phases, establishment of server connectivity, implementation of access control mechanisms, and formulation of end-of-session protocols. It highlights the successful integration and validation of hardware components, backend connectivity, identity confirmation, attendance recording, data encryption, and session termination procedures. The research aims to modernize attendance tracking in educational settings, improving efficiency, accuracy, and security while appreciating the need for further adaptation to suit diverse educational environments for broader adoption and sustained advancement. 2024 IEEE. -
Real-Time Safety Monitoring for Construction Sites Using RFID and Visual Recognition Technologies
The integrated automated safety monitoring system for construction sites utilizes RFID, Wi-Fi, and vision-based recognition systems to enhance worker safety and ensure adherence to safety regulations. This system combines sophisticated components such as RFID tags and Raspberry Pi, solenoid locks, servo motors, and PIR sensors to provide an exhaustive solution. RFID technology is applied to assign unique tags to each worker, facilitating accurate tracking and identification The Wi-Fi and visual recognition components improve the system's functionalities, enabling wireless connectivity instantaneous data transmission, and verification of appropriate safety gear application. Solenoid locks and servo motors ensure regulated access to hazardous areas, responding to authenticated safety compliance records. PIR sensors sense motion, differentiating between authentic presence and mere nearness. The methodology outlines the necessary hardware and software criteria, procedures for system initialization, evaluation phases, server connectivity setup, access control enactment, and session closure protocols. It details the seamless integration and verification of hardware components, backend connectivity, identity and safety adherence verification, data encoding, and session termination processes. This research aims to upgrade safety surveillance in construction environments, boosting productivity, accuracy, and security. It also underscores the need for further adaptability to various construction settings to advance greater uptake and continuous improvement in workplace safety protocols. 2025 IEEE. -
The ontological causation
[No abstract available] -
An iconic turn in philosophy
[No abstract available] -
Identification of the Functional Limitation of Marine Loading or Unloading Arm; A Case Study
Marine loading or unloading arms are used to transfer product from tanker vessels that often carries products like petroleum or chemicals from or to the tankers. Cochin Port has dedicated Tanker jetties for handling petroleum with Marine Loading Arms installed for safe handling of cargo. However, my studies in Cochin Port Trust have shown that it has a potential threat to tackle while it is taken for the maintenance process. The case study aids in understanding of the working of marine unloading arm installed in the port and to identify the functional or safety limitations of the existing model installed. This case study also proves that a small change in the design can bring about a big change in the safety of the people working with the equipment. The identified parameters have been studied for providing the necessary alterations of the design which could be implemented on the upcoming project of constructing the marine unloading arm in Cochin Port Trust. To support faster and safety loading/unloading requirement these hydraulically operated marine loading arms are fitted with emergency release couplings and emergency release system. Marine Loading Arms are operated by using the hydraulic system. During maintenance procedure while checking the Emergency Release System (ERS) functionality, accidental release of Emergency release coupling can cause fatality. Hence a fool proof design is suggested with an extra locking arrangement. The studies conducted till now and the reviews conducted contributed in the analysis of the development and validation of the design. A design of a locking machanism for preventing the fatality is created and analysed for suggesting it to the industry so that it could be incorperated in the upcoming project of constructing the marine loading and unloading arm. 2023 American Institute of Physics Inc.. All rights reserved. -
Exploring the top ChatGPT libraries for powerful conversational AI
This research analyzes the need for top ChatGPT libraries and their usage today. It focuses on the features, benefits, and drawbacks of ChatGPT through in-depth interviews to uncover nuanced insights into library preferences and usage patterns. The findings reveal diverse perspectives among developers, highlighting preferences for libraries based on ease of integration and customization. Participants emphasize the significance of user-friendly interfaces and robust documentation in shaping positive experiences with ChatGPT libraries. Additionally, themes emerge around the need for continuous updates to address evolving AI challenges. Through in-depth insights from developers, the study elucidates the importance of user-friendly interfaces, adaptability, and efficient documentation, providing valuable guidance for developers seeking to optimize their conversational AI projects. This research contributes essential qualitative dimensions to the evaluation of ChatGPT libraries, offering actionable insights for the development community. 2024, IGI Global. All rights reserved. -
A Study on the Indian Small Car Market and Factors Influencing Customers' Decisions Towards Purchase of Small Cars
International Journal of Research in Computer Application & Management, Vol-2 (11), pp. 65-69. ISSN-2231-1009 -
EFMD-DCNN: Efficient Face Mask Detection Model in Street Camera Using Double CNN
The COVID-19 pandemic has necessitated the widespread use of masks, and in India, mask-wearing in public gatherings has become mandatory, with violators being fined. In densely populated nations like India, strict regulations must be established and enforced to mitigate the pandemics impact. Authorities and cameras conduct real-time monitoring of individuals leaving their homes, but 24/7 surveillance by humans is not feasible. A suggested approach to resolve this problem is to connect human intelligence and Artificial Intelligence (AI) by employing two Machine Learning (ML) models to recognize people who arent wearing masks in live-stream feeds from surveillance, street, and new IP mask recognition cameras. The effectiveness of this method has been demonstrated through its high accuracy compared to other algorithms. The first ML model uses the YOLO (You Only Look Once) model to recognize human faces in real-time video streams. The second ML model is a pre-trained classifier using 180,000 photos to categorize photos of humans into two groups: masked and unmasked. Double is a model that combines face recognition and mask classification into a single model. CNN provides a potential solution that may be utilized with image or video-capturing equipment such as CCTV cameras to monitor security breaches, encourage mask usage, and promote a secure workplace. This studys proposed mask detection technology utilized pre-trained datasets, face detection, and various classifiers to classify faces as having a proper mask, an improper mask, or no mask. The Double CNN-based model incorporated dual convolutional neural networks and a technology-based warning system to provide real-time facial identification detection. The ML model achieved high performance and accuracy of 98.15%, with the highest precision and recall, and can be used worldwide due to its cost-effectiveness. Overall, the proposed mask detection approach can potentially be a valuable instrument for preventing the spread of infectious diseases. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Analysis of Social Media Marketing Impact on Customer Behaviour using AI & Machine Learning
The study of client behaviour has been revolutionized by the combination of social media marketing with cutting-edge technology like Artificial Intelligence (AI) and Machine Learning (ML) in today's age of digital transformation. This study delves into the complex interplay between AI/ML, consumer involvement, and social media marketing methods. Our research exposes crucial insights via careful data collecting, sentiment analysis, and the construction of prediction models. By stressing the importance of catering content to individual interests, AI-driven customization emerges as a potent tool, increasing user engagement by 18%. Analysis of online sentiment shows how important it is to keep people feeling good about a business; postings with positive feelings get 30% more likes and comments on average. Accurate and time-saving insights from machine learning models provide up new avenues for optimizing marketing's use of available resources. As a result of the study's conclusions, companies will be able to better connect with their customers, use their resources more efficiently, and behave ethically moving forward. Promising new developments in the subject include the next steps, which include sophisticated AI models, temporal dynamics analysis, and investigation of long-term consequences, ethical issues, and multichannel techniques. This study helps companies, marketers, and policymakers better understand the convergence of technology and marketing in today's ever-changing digital world so that they may better serve their customers and build a successful brand over time. 2024 IEEE. -
Smart Affect Recognition System for Real-Time Biometric Surveillance Using Hybrid Features and Multilayered Binary Structured Support Vector Machine
Human affect recognition (HAR) using images of facial expression and electrocardiogram (ECG) signal plays an important role in predicting human intention. This system improves the performance of the system in applications like the security system, learning technologies and health care systems. The primary goal of our work is to recognize individual affect states automatically using the multilayered binary structured support vector machine (MBSVM), which efficiently classify the input into one of the four affect classes, relax, happy, sad and angry. The classification is performed efficiently by designing an efficient support vector machine (SVM) classifier in multilayer mode operation. The classifier is trained using the 8-fold cross-validation method, which improves the learning of the classifier, thus increasing its efficiency. The classification and recognition accuracy is enhanced and also overcomes the drawback of 'facial mimicry' by using hybrid features that are extracted from both facial images (visual elements) and physiological signal ECG (signal features). The reliability of the input database is improved by acquiring the face images and ECG signals experimentally and by inducing emotions through image stimuli. The performance of the affect recognition system is evaluated using the confusion matrix, obtaining the classification accuracy of 96.88%. 2020 The British Computer Society 2020. All rights reserved. -
Repercussions of global turbulence and market volatility in spot & futures market: India preparedness
This article examines the repercussions of global turbulence and market volatility in Indian Capital market for the period spanning from January 1, 2003 to August 31, 2013 with a total of 2654 observations and it is broken into pre-crisis and post-crisis respectively. The study employed Generalized Autoregressive Conditional Heteroskedasticity (1,1) model to measure the volatility persistence by employing dummy variables. Cointegrating Regression Augmented Dickey Fuller (CRADF) and Vector Error Correction Model (VECM) was employed to investigate the casual nexus between spot and futures market in both short and long run equilibrium. The squared residuals of VECM were applied to investigate the lead-lag relationship between the bivariate variables. Our findings indicate that there was a significant change in the post crisis period for spot and futures market volatility. Our result suggests that nothing can be learned and new regulation can only do more harm. Apart from this, nobody knows which financial instrument will be at the centre of the next crisis. Overall, the comprehensive financial sector reform like Credit Default Swap, Valuation Assumptions and Basel II Accord can create more problems and make the investors more complex to meet the global challenges environment. IJER Serials Publications. -
Partial slip and Joule heating on magnetohydrodynamic radiated flow of nanoliquid with dissipation and convective condition
Numerical investigation of three-dimensional flow of an electrically conducting nanofluid over a bidirectional stretching surface is proposed here. The slip flow over a convectively stretching sheet is considered. The flow is caused due to a non-linear stretching surface and Lorenz force. Water and copper nanoparticles are used to form nanoliquid. Suitable transformations are employed to reduce the conservation equations into nonlinear coupled, multidegree ordinary differential equations. Resultant nonlinear two-point boundary value problem is numerically integrated using Runge-Kutta-Fehlberg fourth-fifth order method. Computed results are verified with existing results under limiting cases. The influences of pertinent parameters on different flow fields are evaluated and presented via graphical and tabular form. It is found that the thermal radiation and convective heating at boundary stabilizes the thermal boundary layer growth. 2017 The Authors -
Collaborative security approaches for IoT ecosystems
The surge in Internet of Things (IoT) devices has transformed numerous industries by enabling unparalleled connectivity and data sharing. Yet, this rapid growth has also exposed critical security vulnerabilities. This chapter delves into collaborative security strategies aimed at improving the safety and reliability of IoT ecosystems. A major vulnerability is weak authentication and authorization, often stemming from poor password practices or insufficient authentication mechanisms. Such flaws can result in unauthorized access, data breaches, and serious cyber threats, including Distributed Denial of Service (DDoS) attacks and Man-in-the-Middle (MITM) attacks. DDoS attacks can overwhelm essential IoT systems, like those in smart cities or healthcare, while MITM attacks can jeopardize data integrity during communication between devices and cloud services. Given that IoT devices frequently handle sensitive information, including personal and health data, ensuring their security is vital to avoid detrimental outcomes for users. Physical security risks also present a challenge, as the physical compromise of IoT devices can disrupt systems or pose risks to individuals. To address these threats, this chapter recommends several cybersecurity measures, such as secure design and development practices, robust authentication methods, and advanced encryption techniques. Secure device design should include mechanisms for safe firmware updates and employ Trusted Platform Modules for secure key storage. Effective authentication can be enhanced with multifactor methods, role-based access controls, and digital certificates. Data protection should involve encrypting data both in transit and at rest, as well as employing techniques like data anonymization and differential privacy. 2026 Elsevier Inc. All rights reserved.. -
Application of Spray Drying process to convert Beneficial Compounds extracted from Plants into free-flowing powder
The use of herbal tablets has been rapidly growing and significant research work is being carried out worldwide with the goal to reap the benefits of the many useful plants that are available with medicinal values. Many of these plants go largely underutilized either due to lack of information on not only just the medicinal properties but simple and effective extraction methodologies as well, without sacrificing the properties of the extracts. Once extracted, the concentrates also must be converted into a suitable form that can be loaded in a capsule etc., ready to be consumed. While there many process methodologies being used worldwide to extract the useful resources from the plant, focus also must be on the process methodology that is being practiced to convert the extract (liquid or semi solid) into a solid free flowing powder form. Thus, in an herbal tablet, there many factors concerned with the manufacturing. They are (i) Identifying the most suitable plant for a particular immunity boosting purpose (ii) extraction of the useful contents, mostly in a liquid or slurry form (iii) transform the extract into a user-friendly product such as powder and finally (iv) encapsulation of the powder for ease of human consumption. This paper brings in a review of the several useful plants available around us across the world. In addition, the paper also highlights the suitable experimental results of the usefulness of spray-drying technology, which is a highly versatile process methodology to transform the extracts into free-flowing powder. Published under licence by IOP Publishing Ltd. -
Operational pattern forecast improvement with outlier detection in metro rail transport system
Transportation is an unavoidable part of every humans life. The mobility system handles the transport of humans from different places using various transport modes. According to a station in a populated area, the main problem is the presence of traffic in peak hours and wasting their valuable time on the road. The only medium which runs above the traffic is metro rails/subways. For these reasons, metro rails become a point of interest for each researchers prophecy and provide valuable recommendations for the smooth functioning of services. Even though, in many cases, the metro systems are affected by abnormal passenger flow. So, this study handles abnormal passenger flow detection and station clustering for the behavior study of a passenger flow system. The research compares outlier detection and anomaly identification for the behavioral analysis of the metro rail passenger flow. The study use data from Kochi Metro Rail Limited for the period 2017 to 2019. Outlier removal has used in passenger flow data before building a forecasting system. In pattern recognition algorithm those components which lie outside the patterns can be considered abnormal (anomaly).The outliers are the component falling apart from the region of interest. The effect of removing the outlier from the time-series pattern is studied against the outlier included pattern to show the improvement. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Leveraging Circular Economy Principles and Emerging Technologies for Future Trends in Sustainable Business Models: A Roadmap to Net-Zero Emissions
This study examines the influence of circular economy concepts and emerging technology on the development of sustainable business models aimed at achieving net- zero emissions. It underscores the significance of resource efficiency, waste reduction, and lifecycle consideration in promoting sustainability. We analyse critical technologies such as blockchain, artificial intelligence, and the Internet of Things for their potential to enhance transparency, simplify processes, and foster stakeholder collaboration. We suggest a detailed roadmap that guides organisations in incorporating these ideas and technologies to establish robust, environmentally sustainable business practices for a sustainable future. 2026 by IGI Global Scientific Publishing. -
Phyllanthus Emblica Extract Protects the Rat Liver Cells Against the Toxicity of Monosodium Glutamate: Experimental Evidence
Background: Monosodium glutamate (MSG), used widely in the food industry, is a threat to the public health. We investigated whether the MSG administration depletes non-enzymatic antioxidants, i.e., vitamins C and E in the liver of Wistar albino rats. We also examined the restorative effect of the ethanolic extract of Phyllanthus emblica (P. emblica). Methods: Wistar albino rats (n=42) were adapted and then randomly divided into seven groups of: 1) control, 2, 3, 4) MSG treatment, and 5, 6, 7) combined MSG and P. emblica extract treatment. All rat groups were treated daily for 120 days. They were orally administered either MSG alone or MSG plus the extract combined. The rats were then sacrificed and the liver was harvested from each group, and homogenized to examine the levels of vitamins C and E in the liver, using RP-HPLC method. Results: The vitamins C and E levels significantly declined (P<0.05) in the liver of MSG treated groups compared to those of the control rats. The combined treatment (extract + MSG) at low and moderate doses restored the vitamin C levels but it restored vitamin E only at the low dose (P<0.05). Conclusions: This study clearly demonstrated the deterioration of non-enzymatic antioxidants, i.e., vitamins C and E in the rats' liver after chronic exposure to MSG. The findings support the toxic effect and oxidative stress due to MSG exposure to the liver and the beneficial effect of the extract of P. emblica that inhibits the MSG's harmful effect on the liver. The Author(s), 2022.