Browse Items (11808 total)
Sort by:
-
Impact of data centers on power consumption, climate change, and sustainability
The data-driven economy is transforming with data centers becoming a crucial business infrastructure. However, the increasing reliance on data centers is posing a threat to the environment. Climate change activists are focusing on reducing emissions from sectors like automotive, aviation, and energy. Data centers consume more electricity than the UK, accounting for 3% of global electricity supply and 2% of total greenhouse gas emissions. By 2040, digital data storage is projected to contribute to 14% of the world's emissions. The number of data centers worldwide has surged from 500,000 in 2012 to over 8 million, with energy consumption doubling every four years. The rise in internet penetration rates and the introduction of 5G technologies and IoT devices will further exacerbate the issue, increasing the demand for data processing. 2024, IGI Global. -
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. -
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. -
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. -
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 -
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. -
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. -
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. -
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. -
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 -
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. -
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. -
An iconic turn in philosophy
[No abstract available] -
The ontological causation
[No abstract available] -
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. -
A STUDY ON COARSE DEG-CENTRIC GRAPHS
The coarse deg-centric graph of a simple, connected graph G, denoted by Gcd, is a graph constructed from G such that V (Gcd) = V (G) and E(Gcd) = {vi vj: dG (vi, vj) > degG (vi)}. This paper introduces and discusses the concepts of coarse deg-centric graphs and iterated coarse deg-centrication of a graph. It also presents the properties and structural characteristics of coarse deg-centric graphs of some graph families. 2024, Canadian University of Dubai. All rights reserved. -
A study on deg-centric graphs
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 introduces and discusses the concepts of deg-centric graphs and iterated deg-centrication of a graph. (2024), (Universidad Catolica del Norte). All rights reserved. -
Evidence-Based Interventions for Improved Psychosocial Outcomes in Harmful Alcohol Use: A Scoping Review
Background. Harmful alcohol use is defined as a drinking pattern that lasts at least one month or has occurred often during the preceding 12 months and that negatively impacts multiple facets of life. It has a high recurrence rate and a poor prognosis, despite the availability of cognitive-behavioral and psychosocial therapy. Emerging neuromodulation techniques for treating harmful alcohol use are gaining traction in the field of psychotherapy, but knowing their efficacy in terms of psychosocial outcomes necessitates an adjuvant approach. This scoping review aims to investigate the existing evidence on the effectiveness of various psychosocial interventions that improve quality of life (QoL) dimensions in conjunction with neurotherapies for individuals with harmful alcohol use. Methods. The review utilized a five-stage technique to search for research papers from 2000 to 2022. After screening and reviewing 41 full-text papers, 29 were found to meet the inclusion criteria. Conclusion. The articles highlighted the advantages of integrated therapeutic interventions such as motivation enhancement therapy, cognitive behavior therapy, neurotherapy, multimodal therapy, supportive therapy, and 12-step facilitation programs. However, limited studies have explored the effectiveness of combining neurotherapy with psychosocial interventions. Implications. Future research should focus on the efficacy of combining neurofeedback with psychosocial therapies to improve QoL for individuals with harmful alcohol use. 2024. Thakuria and Bennett. -
Enhancing Digital Citizenship Through Secure Identification Technologies in the Global Unified Digital Passport
Passports play a vital role in enabling international movement and security, as well as confirming one's identity. However, the existing passport system has many problems and limitations, such as identity fraud, passport falsification, human smuggling, terrorism, and border control. Despite the fast growth and adoption of digital technologies in various fields, the passport system has not been able to adapt to the changing demands and expectations of the global community. Therefore, there is a pressing need to investigate and develop a digital passport and verification system that can address the shortcomings of the conventional passport system and provide a more safe, convenient, and effective way of managing and verifying the identity and travel history of individuals across the world. This paper presents the solution and requirement for the development of a digital passport system that can be applied globally and universally. The paper proposes a conceptual framework and a technical architecture for the digital passport system, based on the principles of blockchain, biometrics, and cryptography. The paper also discusses the possible benefits, challenges, and implications of the digital passport system for various stakeholders, such as travelers, governments, airlines, and immigration authorities. The paper aims to contribute to the research and innovation of digital identity and citizenship, as well as to the progress of the sustainable development goals (SDGs) related to peace, justice, and strong institutions. 2024 IEEE. -
Design and Implementation of an Optimized Mask RCNN Model for Liver Tumour Prediction and Segmentation
Segmentation of liver tumour is a tedious job due to their large variation in location and closeness to nearby organs. In this research, a novel Mask RCNN prototype is developed which uses ResNet-50 model. The architecture utilizes the masked location of convolution neural network to precisely detect liver tumours by recognizing liver sites to deal with changes in liver and CT snaps with distinct metrics. The preprocessed CT scans are subjected to ResNet-50 model. The data samples used here comprises 130 instances recorded from several clinical sites that are publicly available on the LiTS weblink. The designed model upon deployment generates a promising outcome thereby obtaining a DSC of 0.97%. Thus, we can conclude that the developed model is capable enough to accurately assess liver tumours and thus help patients in early diagnosis. 2023 IEEE.