bluuebunny/crossref_metadata_2025 huggingface dataset:
M-Tech Profit Program Supports in the Smart City of Madiun
Authors: Maidi, Parji, Sofjan Aripin, Bambang Supriyono, Akadun
Published: December 2023

In the digital revolution era, affordable and accessible internet services are crucial for all segments of society, especially with the rise of industrial revolution 4.0. However, limited internet access primarily affects the upper middle class. To bridge this gap, the Madiun City Government initiated the Profit M-Tech program, introducing the Free Wi-Fi Program for Madiun Hand-held Technology. This study aims to (1) clarify the program’s goals, (2) analyze its benefits in education, economy, and socio-culture, and (3) assess its impact on supporting smart city policies. Findings show that the program has significantly increased internet accessibility for all classes, aided sectors like education and economy during the pandemic, and effectively supported smart city initiatives.
Distributed Cipher Hashing Cryptographic Technique For Secure Data Storage In Cloud Computing Using XOR-MECC
Authors: A R Athira, Dr. P. Sasikala, Dr. R. Reka
Published: December 2023

The user is enabled by means of Cloud Computing (CC) to store and access the data as of anywhere. To secure the data amassed on the Cloud Server (CS), CC security encompasses a collection of policies and technologies. Most existing methods bring about a lower-Security Level (SL), the higher computation time for encryption together with decryption, and also Memory Usage (MU) issues, et cetera. Here, to resolve such issues in the existent methods, an effectual modified cryptographic is proposed in favor of the secured Data Storage (DS) in CC. Firstly, secure authentication is done via the Alder32 hash algorithm. The user is authenticated via registration, login, together with verification. Subsequently, the authenticated user commences uploading the file. Prior to uploading, the inputted file is bifurcated into sub-files counting on the size, and the features are extracted as of the split file and distributed CS. Next, the Brownian Motion-based BAT algorithm (BM-BAT) selects the appropriate distributed CS. Then, the Modified   Cryptographic (XOR-MECC) uploads the files securely into the CS. The experimentation's outcomes state that the proposed work attains better performance when weighed against the prevailing works.
Intrinsic Motivation – A Strategic Tool For Entrepreneurial Intention
Authors: Ms. Kajal J Mehta, Dr. C. Jothi Baskara Mohan
Published: December 2023

Entrepreneurial society evolved based on conditions like grabbing the opportunity, motivation and means to attain goals. To become an entrepreneur college students, need motivation either in form of willingness to become an entrepreneur or it has to be their need to become entrepreneurs this act will result in finding opportunities in the market (Angela Mota et.al, 2019). As motivation plays a vital role among students to become an entrepreneur, it becomes important to understand their motivation level (intrinsic motivation level), the current study tries to validate an intrinsic motivation inventory adopted by Mc Auley, Duncan, and Tammen (1987), so that this inventory can be used to understand the motivation level among students that can inspire them to become entrepreneurs. A study is conducted among 1037 participants from a higher education institution situated in Madurai. Jamovi open-source software 2.3.21 is used to validate the inventory. The validated scale shows good model fit. The procedure of validation is discussed in this study
A Study On Public Opinion About Businesses In Digital Era With Special Reference To Chennai
Authors: Mr. Daniel Felix Joseph Chalke, Ms. Juliet. N
Published: December 2023

Since 1600 the concept of digitalisation has been innovating. It all started back in 1679 when the binary system was found. Then after a series of technological advancements digitalisation saw the light of the world. Various fields depend on digitalisation and businesses are one of them. Even in the upcoming future, there is a lot of scope pertaining to digitalisation. The main objective of the paper is to analyse how effective are the businesses running in the new digitised era and also to examine the various advantages and disadvantages related to digitalisation of the business sector. The sample size of 210 respondents was chosen for the research where data was collected using questionnaires. A convenient sampling method was adopted where the samples were chosen based on convenience and ease of access. From the long analysis, it was found that people think that businesses are highly effective when backed with digitalisation and technology with a hope that it would be even more effective if there is a constant growth in digitalisation. It is finally concluded that digitalisation plays a very important roles in various sectors and one of them in the business sector and with many more developments and innovations in it, it is sure that India would reach all the heights of development in the business sector which would boost the economy and act like a boon for the upcoming future.
A Study On Job Satisfaction Of Nurses In Private Hospitals Chennai.
Authors: Mrs. A. Mary Louisa
Published: December 2023

Employee job satisfaction has been under academic and scholarly investigation by the researchers for more than a century now. The existence of such studies takes us back to 1911, when Taylor began to study employees and their job duties to develop better ways to train workers. Seven years later, the interest in job satisfaction had clearly arrived when Edward Thorndike examined the link between work and satisfaction in the Journal of Applied Psychology in 1918. The actual curiosity in exploring job satisfaction began with 'Hawthorne studies' by Elton Mayo (1933) and works by Hoppock (1935). Although, these two studies were carried out at the same time yet the Hawthorne studies (1927-33) had the most long lasting influence on job satisfaction research
Performance of Switched Inductor-qZSI on different Controls Methods for PV Application
Authors: Chandra Shekhar Singh Chandal, Saket Kumar Singh, Ajit Kumar
Published: December 2023

The performance of the Switched Inductor Quasi Impedance Source Inverter (SL-qZSI) for the Simple, Maximum, and Maximum Constant Boost Control Methods for PV Application is compared in this study.  Because a second DC-DC converter must be added to the inverter circuit to achieve the desired output due to the significant voltage differential between the AC output side and the DC input side, the suggested control technique intends to increase the efficiency of the DC/AC converter. This two-stage system i.e., DC-DC conversion and then DC to AC conversion not only reduces the efficiency but also dilutes the dynamic response. Researchers have recently suggested alternative ZSI to maximize the boost factor and enhance the functionality of the inverters (Input Side).  To satisfy both requirements, SL-qZSI is suggested. The SL-qZSI enhances input current and reliability compared to conventional ZSI. Additionally, compared to other ZSI, the proposed SL-qZSI has lower shoot-through (ST) current, current stress on inductors & diodes, and voltage stress on capacitors. Additionally, it prevents the starting inrush current, which may harm the equipment. The boost factor and gain of the maximum boost control are found to be high compared to others for the same value of the modulation index in the control component, but a large inductor is needed due to the significant ripple in the input current and shoot-through current. The inductor's high value results in an increase in both the inverter's size and price. Results from simulations have been run under the PV panel's Standard Test settings.
A Review and Comparison of FWD and Benkelman Beam in Evaluation of Pavement Structure Capacity
Authors: Shashi Ranjan, Utsav Mani,  Sumit Kumar
Published: December 2023

A new asphalt layer is typically added to the original pavement structure as part of major rehabilitation projects for India's road network, which is largely degraded and in need of repair. Understanding and analyzing the structural capacity of the pavement is crucial to creating a rehabilitation design that is both durable and cost-effective. Measuring a flexible pavement's structural capacity in relation to the deflections it causes when a load is applied. In numerous nations, the Falling Weight Deflect to meter (FWD) and the Benkelman beam are the most often employed methods for determining pavement deflections; the former operates under dynamic loading conditions, while the latter operates under static loading. However, since these devices are still commonly used in many nations, including India, it is vital to compare the deflections measured by the Benkelman beam and the falling weight deflect meter, even though numerous design approaches, like AASHTO, do not support their usage under static stress. 30 deflection observation stations along a 1.5 km flexible urban roadway segment were chosen for this investigation, and deflections were measured using BBD and FWD methodologies. On a point-scoring basis, both exams are administered simultaneously. Also, the information gathered during booth testing complies with IRC 81-1997 and IRC 115-2014.
Fabric Defect Analysis In Textile Manufacturing: Evaluating Methods For Generic And Jacquard Fabrics
Authors: Ms Shital K Dhamal, Dr Chandani Joshi, Dr D.S Chouhan
Published: December 2023

Regarding maintaining product quality and satisfying customer expectations, fabric defect analysis is vital in textile manufacturing. This research provides a comprehensive assessment of many techniques used for analyzing and pinpointing flaws in standard and Jacquard textiles. This study considers machine vision systems and AI-based algorithms as two examples of cutting-edge automation alongside more conventional human inspection procedures. Manual inspection techniques are extensively reviewed in the first section, with particular emphasis on these techniques' inherent subjectivity and resource inefficiency. More objective and effective defect detection techniques are highlighted as a solution to the problems caused by human bias. The research then looks into automated methods, examining how recent developments in image processing, computer vision, and pattern recognition have the potential to greatly improve the accuracy and speed with which defects may be detected. The major emphasis of this investigation is the use of AI, namely machine learning and deep learning models, for fabric defect identification. This demonstrates how AI may revolutionize textile manufacturing by automating flaw identification and categorization processes. Accuracy, efficiency, scalability, cost-effectiveness, and adaptation to varied fabric compositions, such as basic and complicated Jacquard textiles, are only some aspects considered throughout the evaluation. In addition, the research addresses the problems and opportunities in the field of fabric defect analysis right now. The paper presents prospective improvements, such as hybrid methods and real-time monitoring systems, to solve current constraints and pave the way for a more robust defect analysis framework. These innovations aim to contribute to sustainable practices and customer happiness in the textile manufacturing industry by fostering effective quality monitoring and production optimization. In conclusion, this study provides a thorough comprehension of fabric defect analysis procedures, which is helpful for professionals and academics in the field. The results fuel the never-ending development of quality assurance techniques, resulting in improvements that raise standards, shorten production times, and give the textile business a fighting chance in the market.
Investigation On Tensile Properties Of Aluminum 7075 - Silica Particles -Boron Fiber- Reinforced Hybrid Metal Matrix Composites
Authors: Vinayakumar R, Dr. H Ramakrishna
Published: December 2023

Hybrid metal matrix composites (MMCs), particularly those using an Aluminum 7075 (Al7075) base combined with silica particles and boron fibers, have emerged as a focal point in engineering for their superior strength-to-weight ratio. This study examines a composite of Al7075 reinforced with varying amounts of silica particles (0%, 2%, 4%, 6%) and boron fibers (1%, 3%, 5%, 7%). The research aims to discern the impact of these reinforcements on the composite's tensile properties. Employing the stir casting technique, these reinforcements  were melted into the Al7075 matrix. The composites underwent tensile testing to gauge their mechanical performance, with a tensometer measuring tensile strength. The findings indicate that the inclusion of silica and boron fibers enhances tensile strength, with the greatest increase of 43.5% rise in tensile stress observed in the sample with 6% silica and 7% boron fiber. Variance analysis reveals that silica contributes 85.51% to the strength, boron fiber contributes 12.70%, and there is an error margin of 1.49%. Furthermore, a comparison of experimental, regression, and Artificial Neural Network (ANN) predicted values confirms consistency within an error margin of 5.86%.
Cost-Efficient Deep Learning Model for Colorectal Cancer Diagnosis: Leveraging Unsupervised Learning Composite Network
Authors: Ayushi Lowanshi, Nitesh Rastogi
Published: December 2023

Despite this, the mortality rate associated with CRC is among the highest of all malignancies.  Even while prompt and accurate diagnosis can significantly improve patient outcomes, traditional diagnostic procedures are usually expensive, invasive, and time-consuming. This is despite the fact that rapid diagnosis can significantly improve patient outcomes. Deep learning has emerged as a potential avenue for the diagnosis of cancer, despite the prohibitive nature of the costs associated with developing robust models in terms of both computational and financial resources. In this article, the authors offer a deep learning model that is optimized for    the diagnosis of colorectal cancer at a low cost.  The model was trained using an unsupervised learning composite network (ULCN). Unsupervised learning is utilized by the ULCN in order to pre-process enormous amounts of unlabeled data in order to cut down on the necessary volume of labelled data as well as     the associated cost. Our model was able to capitalize on the underlying structures and patterns in the data, which allowed     it to perform far better than other methods available at the time on a test set of histopathological images. The unsupervised feature extraction phase significantly reduced the amount of time required for training, hence showcasing the efficiency of the model. The findings indicate that by combining unsupervised learning into deep learning systems, it may be possible to create   a diagnostic tool that is more easily accessible and scalable in clinical settings.
Deep Learning for Economical Colorectal Cancer Diagnosis: Harnessing the Power of Composite Networks with Unsupervised Learning
Authors: Ayushi Lowanshi, Nitesh Rastogi
Published: December 2023

As a major worldwide health issue, diagnosing colorectal cancer quickly and accurately is essential. The potential of deep learning to aid in colorectal cancer diagnosis has grown substantially in recent years. However, traditional deep learning models are frequently not widely adopted in clinical settings because to their high computational cost and resource intensiveness. To this end, we present "Deep Learning for Economical Colorectal Cancer Diagnosis: Harnessing the Power of Composite Networks with Unsupervised Learning," a novel method for analysing data in this area. The foundation of this study is the idea of using composite networks, which cleverly incorporates unsupervised learning to greatly lessen the training cost. Our algorithm effectively extracts relevant characteristics by exploiting vast volumes of unlabeled data and then fine-tuning on a smaller labelled dataset for targeted diagnostic tasks. The result is a cheap model that doesn't skimp on diagnostic precision. The empirical data we present here show that our composite network achieves high levels of accuracy while using a minimal amount of resources, making it a promising candidate for fast and cheap diagnosis of colorectal cancer. The results presented here highlight the potential of unsupervised learning to make deep learning more accessible and inexpensive for healthcare institutions around the world, and hence have far-reaching implications for the future of medical diagnosis.
Sustainable Pavement Construction: A Comprehensive Review on the Use of Recycled Materials and their Impact on Performance
Authors: Aditya Jakhar, Rahul Meena
Published: December 2023

The increasing demand for sustainable and environmentally friendly solutions in the construction industry has led to the exploration of using recycled materials in pavement construction. This study reviews the effectiveness of various recycled materials, including recycled concrete aggregates (RCA), coarse recycled aggregates (CRA), rubberized materials, fibers, recovered asphalt pavement (RAP), jarosite, fly ash, and ground granulated blast-furnace slag (GGBS), as potential substitutes for conventional materials. The mechanical properties, durability, and environmental benefits of these materials are assessed in the context of pavement construction. Although the appropriate percentage and treatment of recycled materials must be carefully considered, this review highlights the potential for recycled materials to contribute to sustainable pavement construction without compromising performance. By optimizing the mixtures and developing guidelines for their implementation, the construction industry can promote sustainability, reduce waste, and minimize the environmental impact of pavement construction
Flow and Heat transfer of Hybrid nanofluid past an Exponentially Stretched Porous surface
Authors: K. Fatima, T. Hymavathi
Published: December 2023

This research looks at the flow and heat conduction properties of a hybrid nanofluid formed by an exponentially stretched porous surface. Over the last decade, there has been a substantial increase in research on nanofluids. Despite several contradictions in the published results and a poor understanding of the heat transmission mechanism in nanofluids, this fluid has emerged as a viable heat transfer medium. It takes longer for the combination nanofluid to make heat than it does for ordinary nanofluid. It has also been shown through simulations that the mixed nanofluid has better temperature performance than the nanofluid. Using the similarity transformation approach, the governing equations of the issue are turned to similarity equations. To convert nonlinear partial differential equations (PDEs) to ordinary differential equations (ODEs), the Keller Box approach is used. This strategy for solving these ODEs has been shown to be quite successful. The effects of several targeted parameters on physical quantities are shown, and the comparison of findings for validation is also recorded. The study's findings are provided as graphs, demonstrating the strong influence of the targeted elements on both nanofluid and hybrid nanofluid. Because of the existence of hybrid nanofluid, the velocity and temperature profiles have improved. Variations in the amount of Copper (Cu) nanoparticles, in particular, cause dramatic variations in the velocity and temperature profiles of the hybrid nanofluid. Furthermore, a lower Prandtl number is shown to reduce temperature and thermal boundary layer thickness. The graphical representations also show how porosity affects temperature and velocity profiles.
Bevels And Flares for Different Restorations: A Narrative Review
Authors: Ekta Choudhary, Garima Joshi, Farheen Khan
Published: December 2023

The connection between the tooth and the restoration is the weakest link in any of the restoration. The success and longevity of a restoration greatly depend on the design and preparation of the marginal peripheries, which are the edges where the restoration meets the natural tooth structure.When the margins of the restoration closely adapt to the cavosurface finish line of the tooth preparation, it creates a favorable relationship between the restoration and the tooth structure. This close adaptation helps to improve the survival of the restoration in the oral environment.A well-fitting margin provides several benefits. It helps prevent microleakage, Microleakage can lead to recurrent decay, sensitivity, and even failure of the restoration. By closely adapting the margins, the risk of microleakage is minimized.A precise margin adaptation helps in creating a strong bond between the restoration material and the tooth structure. This bond enhances the structural integrity of the restoration and provides better retention, preventing its dislodgement or fracture.
Optimizing Electric Vehicle Battery Performance: Comprehensive Study of Battery Heating Challenges and Sustainable Battery Swapping System
Authors: Ramya P, Nagesh R, Murugesh Dodakundi
Published: December 2023

This paper conducts a comprehensive study on battery heating challenges in electric vehicles (EVs) and proposes a sustainable battery swapping system to optimize battery performance. With the increasing demand for EVs and the necessity for extended driving ranges, notable progress has been achieved in battery technologies. However, battery heating remains a crucial concern, particularly in cold weather conditions. This research thoroughly examines various factors contributing to battery heating, including battery size, chemistry, design, and ambient conditions. Diverse battery heating techniques, such as insulation and thermal management systems, are extensively analyzed. Underlining the significance of effective battery heating solutions, this study emphasizes the importance of maintaining an optimal battery temperature to enhance performance and prolong battery life. Additionally, the paper introduces a sustainable battery swapping system controlled by a Blynk app and Node MCU controller, facilitating seamless battery replacement and promoting the integration of renewable energy sources. This system ensures uninterrupted EV operation while providing users complete control and real-time monitoring capabilities via mobile devices, revolutionizing the EV charging paradigm.
Personal branding through Fashion blogging and its impact on Consumers
Authors: Dr. Gagan J. Bhatia
Published: December 2023

This research paper sets out to examine the influence of personal branding and fashion bloggers on our shopping habits and perceptions of fashion in the digital realm. It is noted that fashion bloggers act as influential online friends, imparting style tips and introducing us to new fashion trends and brands. The study underscores the importance of building a strong personal brand, with a focus on authenticity and expertise as key factors in gaining the trust of an audience. The paper also highlights the impact of fashion bloggers on consumer behaviour, demonstrating their ability to shape preferences and create a sense of trust that often surpasses traditional advertising methods. The study emphasises the interconnected relationship between personal branding, fashion blogging, and consumer decision-making, showcasing the profound impact they have on the digital fashion landscape.
Induction Programme as A Process of Employee Socialization
Authors: Dr. Premajyothi. D
Published: December 2023

Induction means the introduction of newly appointed employees and to the organisation by providing him relevant information so that he may not experience any difficulty in discharging his duties and responsibilities properly and effectively.

Induction is considered as an important part of a company’s human resource practices. Many aspects should be addressed when discussing the importance and objectives of an induction process. Induction can be defined as the process and support provided by the organization in order to help a new employee to learn to know the new organization and its members as well as the expectations and tasks of the job.

Employee retention and knowledge management is increasingly becoming the watch word of HR professionals. The process towards successful implementation of a knowledge management process and retention policy starts right from the moment the employee enters an organization. An induction program is one of the first and primary components of the process. (Foot and Hook (1999) presents induction as an important aspect of employee retention as it is the first process that the new employee faces and the way it is managed often reflects the values of a company. Paying special attention to the first impression made by the company will make the new employee feel valued and expected. With employee turnover being high, especially in the knowledge industry the importance of induction cannot be felt more now than ever. Ingersoll and Smith (2004) rightly put it as a support, guidance and orientation program for a new entrant. Many organizations though, consider it as just another formality and fail to understand the long term riches that it could yield. The effects of a good induction program needs to be analyzed not immediately but after a lapse of sufficient period.
Awareness And Utilization of Esi Benefits Among Employee
Authors: Dr. Premajyothi. D
Published: December 2023

The Employee State Insurance Act. ESI 1948. Is a piece of social welfare legislation enacted primarily with the object of providing certain benefits to employees in case of sickness, maternity and employment injury and also to make provision for certain others matters incidental thereto. The Act in fact tries to attain the goal of socio-economic justice enshrined in the Directive principles of state policy under part 4 of our constitution, in particular articles 41. 42 and 43 which enjoin the state to make effective provision for securing, the right to work, to education and public assistance in cases of unemployment, old age. Sickness and disablement. The act strives to materialize these avowed objects through only to a limited extent. This act becomes a wider spectrum then factory act. In the sense that while the factory act concerns with the health, safety, welfare, leave etc., of the workers employed in the factory premises only. But the benefits of extend to employees whether working inside the factory or establishment or they are directly employed by the principal employee or through an intermediate agency, if the employment is incidental or in connection with the factory or establishment.
A Review and Analysis on Fake News Detection Based on Artificial Intelligence and Data Science
Authors: Shoaib Shaik
Published: December 2023

Along with the development of the Internet, the emergence and widespread adoption of the social media concept have changed the way news is formed and published. News has become faster, less costly and easily accessible with social media. This change has come along with some disadvantages as well. In particular, beguiling content, such as fake news made by social media users, is becoming increasingly dangerous. The fake news problem, despite being introduced for the first time very recently, has become an important research topic due to the high content of social media. Writing fake comments and news on social media is easy for users. This study attempts to investigate advanced and state-ofthe-art fake news detection mechanisms pensively. We begin with highlighting the fake news consequences. The objective of this study was to synthesize the current possibilities of using AI to combat disinformation. Disinformation is currently a growing problem, and it is increasingly important to understand how we can utilize AI to help address this issue. Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) and Natural Language processing (NLP) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies this study made a review analysis by considering the authors their own perceptions in the previous years was presented in this paper.
Design And Analysis of a Pipe Inspection Caterpillar Robot Using Different Materials
Authors: A. Johnson, Dr. P. Rajendran, Dr. Sriram Venkatesh
Published: December 2023

The development of a pipe inspection robot employing caterpillar locomotion and constructed with PLA and ABS materials entails a comprehensive process. Beginning with the delineation of specific requirements, such as size and inspection capabilities, the conceptual design is conceived, outlining the robot's features. The subsequent phase involves meticulous 3D modelling using software like SolidWorks, followed by ANSYS analysis to simulate and optimize the robot's structural integrity, stress distribution, and overall performance. PLA and ABS are chosen as 3D printing materials, with PLA offering ease of printing and biodegradability, while ABS contributes durability and temperature resistance. The prototype is then constructed, incorporating motors, sensors, and the caterpillar locomotion system. ANSYS analysis is employed to assess the robot's behaviour under varying conditions, ensuring robustness and reliability. The iterative process of testing and refining the design leads to the optimization of the robot's performance. The final steps involve thorough documentation, regulatory compliance checks, and potential scalability for diverse applications, culminating in the deployment of the caterpillar robot for effective pipe inspection tasks.

This paper delves into the design and static structural analysis of a caterpillar robot model utilizing ANSYS, with a specific emphasis on assessing two distinct materials: Acrylonitrile Butadiene Styrene (ABS) and Polylactic acid (PLA). The study involves a comprehensive comparison of various parameters between the two materials, aiming to provide insights into their respective performance and suitability for the caterpillar robot's structural components.
Enhancing Food Resource Allocation in India: A Fuzzy Logic Approach Integrated with Julia and Python
Authors: N. Sindhuja1, K. Kalaiarasi
Published: December 2023

Exploring the intricate network of food resource distribution, especially in the Indian context, this research delves into the multifaceted challenges stemming from poverty and food insecurity. It offers a comprehensive analysis that includes historical perspectives, socio-economic conditions, government policies, and their impact on vulnerable communities. Using innovative methods such as fuzzy logic-based models, Lagrangian optimization, and the Graded Mean Integration (GMI) representation technique, the study provides a systematic approach to allocate resources effectively in food distribution inventory management. By employing the Julia programming language to define and illustrate data for three distinct regions, the research sheds light on critical factors like food poverty levels, budget constraints, demand, supply, inventory, and resource allocation. Additionally, the use of Python libraries for visual representation enhances our understanding of these crucial parameters. Ultimately, this research contributes to the ongoing effort to establish a more equitable and sustainable food distribution system, ensuring access to nutritious food for all individuals, thereby fostering both individual well-being and national prosperity.
An Integrated Approach of Ant Colony Optimization (ACO), Machine Learning (ML), and Fuzzy Logic for Revolutionizing Inventory Management in Modern Supply Chains
Authors: S. Swathi, K. Kalaiarasi
Published: December 2023

This article presents a groundbreaking approach aimed at addressing the limitations of conventional inventory management practices in contemporary supply chains. The principal objective is to revolutionize inventory management by harnessing the synergistic potential of Ant Colony Optimization (ACO), Machine Learning (ML), and Fuzzy Logic. This integrated framework seeks to elevate demand forecasting, optimize ordering strategies, and enhance inventory control processes. Methods: The methodology encompasses the amalgamation of three potent techniques: ACO for the optimization of reorder points and quantities, ML for precise demand forecasting through the analysis of historical data and external variables, and Fuzzy Logic for managing imprecise and linguistic factors to facilitate adaptable decision-making. This fusion minimizes overall inventory costs while refining inventory-related choices. Findings: The fusion of ACO, ML, and Fuzzy Logic represents a pragmatic solution for contemporary inventory management. Businesses that embrace this approach can attain adaptability, data-driven precision, and flexibility, resulting in improved demand forecasting, optimized ordering strategies, and more efficient inventory management processes. An illustrative real-world case demonstrates that this integrated approach leads to cost-effective and responsive solutions, with the potential to revolutionize inventory management, translating into cost savings, heightened customer satisfaction, and enhanced operational efficiency. Novelty: The novelty of this integrated approach lies in its distinctive amalgamation of ACO, ML, and Fuzzy Logic within the inventory management context. While these techniques are well-established in their own right, their integration signifies an innovative response to an enduring challenge. This approach enables adaptability to shifting conditions, precise demand forecasting, and flexible decision-making, which were arduous to achieve using traditional methodologies.
Noise Removal Using Adaptive Damping Based Affinity Propagation Clustering Algorithm in Coronary Artery Disease
Authors: Ramasamy MaheshKumar,  Sundaram Veni
Published: December 2023

The health business generates enormous amounts of data, and by using this massive quantity of data, many illnesses may be recognized, predicted, and even treated at early stage. Humans face significant danger from coronary artery disease, cancer, and tumor illness. Predicting Coronary Artery Disease (CAD) is a difficult and time-consuming process in the medical sector. Early prediction is a virtuoso skill in the medical area, particularly in the cardiovascular sector. Prior research on developing early prediction model provided a grasp of modern strategies for detecting variance in medical imaging. Cardiovascular disease prevention may be accomplished with a diet plan established by the concerned physician after early diagnosis. This study aims to forecast CAD utilizing the suggested approach by creating noise reduction in CAD using the Adaptive Damping based Affinity Propagation (ADAP) clustering algorithm. This kind of knowledge-based identification is critical for accurate prediction. Despite the lack of supporting evidence, this substantial strategy positively influences determining variance in medical disciplines. Additionally, this publication has minimized the use of noisy data to aid in illness identification. This article discusses novel adaptive image-based clustering algorithms and compares them to established classification methods for predicting CAD early and with better accuracy.
Connected Health: Revolutionizing Patient Care Through Artificial Intelligence Innovations
Authors: Dr. Rahul Pulimamidi, Prabu Ravichandran
Published: December 2023

The pharmaceutical sector is not immune to the radical changes brought about by the widespread adoption of AI. Automation, optimisation, and individualization are just some of the ways that artificial intelligence is being applied in the pharmaceutical business, from drug research to dispensing. This article examines how AI is already being used and where it has the potential to go in the pharmaceutical sector. We'll take a look at how AI is being used in the pharmaceutical industry, from R&D to personalised medicine to quality assurance to supply chain management to patient counselling. Data privacy, ethical concerns, and legal impediments are just a few of the difficulties we'll cover in relation to the use of AI in the pharmaceutical industry. This article will highlight studies that demonstrate how AI has the potential to drastically revolutionise the pharmaceutical industry through accelerating medicine development, improving patient outcomes, lowering costs, and increasing efficiency and accuracy across the board. This has led many to predict that AI will revolutionise every aspect of healthcare, from diagnosis to treatment to follow-up care. In most people's minds, AI techniques will supplement and improve upon existing human efforts rather than supplant them. Artificial intelligence is prepared to assist the healthcare business with administrative work, clinical documentation, patient outreach, and specialised aid in areas such as image analysis, medical device automation, and patient monitoring.
Investors Perception Towards Commodity Market With Reference To Coimbatore District
Authors: Dr. G. Bhoopathy, R. Sakthivel
Published: December 2023

In Modern days, the field of investment is becoming more dynamic than olden days. International and National factors are growing rapidly that change the values of specific Investments. The aim of the study is to examine the investors awareness towards commodity market and to identify various factors influencing commodity trading and to understand the most preferred commodities by the investors in commodity trading. The research found out four factors that is trading frequency habits, goal of an investors, literacy awareness level and emotional risk tolerance. The study adopted descriptive research design, by collecting data from 720 respondents by using questionnaire. Collected data were analyzed with the help of percentage analysis and statistical package for social science (SPSS) and the hypothesis were tested using anova. Further it is found from the study that majority of the respondents strongly prefer to invest in the commodity Gold, Crude Oil, Silver, Natural Gas, Aluminum and Least Preferred Commodities are Zinc, Copper, Lead Investments in commodity trading are increasing nowadays in metro and urban area.
Failure Analysis of Hybrid FREP Composites Laminated Bonded Tubular Lap Joints
Authors: K. Susheela, Prof P. Ramesh Babu
Published: December 2023

Fibre-reinforced polymer composites have found extensive applications in the oil, gas industries. The use of interlayer Hybrid composite pipes are used in several engineering applications is increasing due to their superior properties such as high specific strength, high chemical resistance and resistance to UV rays .Hybrid composite adherends lap joints need to be explored to develop a cost-effective combination of materials to address the certain complex  behavior due to hybridization . It is essential to understand the mechanical behavior of Hybrid Tubular Lap Joints (HTLJ) to provide design guidelines for safe performance of joint. In the present work Failure analysis of the adhesively bonded HTLJ is performed. Stress distributions within the joint region are estimated under tensile load. Failure indices at critical interfaces are evaluated by using Quadratic Failure Criterion (QFC).The failure index values of HTLJ were obtained for different overlap lengths. Effective overlap length for suitable performance of the joint is determined based on the Tsai-Wu failure criterion.
E- Commerce Practices with Special Reference on Amazon Product
Authors: Dr. E. Saravanan, Dr. A. Kavitha, M. Sathya
Published: December 2023

Online shopping is nowadays used everywhere or in every corner of the world and it is happening only because of customers directly make an order something they need through online shopping in Amazon. The meaning of online shopping is the process of buying goods and service from merchant over the internet. Amazon is one the site that customer used for purchasing many products. Online shopping makes easier for customer to choose different variety of product which they want, that the reason online shopping is popularly used nowadays. This paper made an research in online shopping through Amazon. Online shopping is a form of electronic commerce which allows consumers to directly buy goods and services from a seller over the internet using a web browser or a mobile app. Customer find a product of interest by visiting the website of the retailer directly or by searching among alternative vendors using a shopping search engine which display the same products availability and pricing at different e- retailer. A typical online store enables the customer to browse the firm’s range of product and services view photos or images of the product, along with information about the product specifications, features and prices. Online stores typically enable shoppers to use “search “features to find specific model, brand or items. It aims to find that the online shopping is easy to use and identify the offers and replacement of product factors.
Manifestation of Aesthetical Values in Traditional Design for JKKN ‘Makyung’ Costumes in Kelantan Based on its Sociocultural System
Authors: Muhammad Faisal Ahmad Zolkipli Abdullah,
Published: December 2023

In Kelantan, the traditional pattern that existed in the designing the costumes for ‘Makyung’ exhibits the combination of organic and geometric styles. This balances and reinforces every produced design. The use of aesthetical values in designing patterns with underlying decorations is directly created to mirror the activities within its social community through resources that reflect their customs and culture. Based on the displayed designs, we can learn one branch in JKKN’s ‘Makyung’ performances in Kelantan. The research on the conservation of aesthetical values in designing traditional pattern for ‘Makyung’ costumes is a qualitative research, carried out descriptively through observing and analysing the performances, as well as interviewing the costume designer which entails the analysis of aesthetical and sociocultural values to explain the connection and role of the traditional designs in the costumes. The result of this study on the conservation of aesthetical values will be able to help the society to understand the development of ideas, creativity, aesthetical values, and meaning in every performance as well as the factors that support the process of designing traditional patterns for JKKN’s ‘Makyung’ costumes in Kelantan based on its sociocultural system.
Systematic Literature Review: Stress among School Counsellors
Authors: Engku Mardiah Engku Kamarudin Nor Hafizah Mohammad Hanafi, Muhammad Asyraf Che Amat,
Published: December 2023

School counselor (GBK) is a career that carries out its duties as a teacher in providing guidance and counseling services in schools. This career is one of the important areas of work in the process of educating and providing auxiliary services in schools, but it is also not spared from many challenges and stresses. It is important that we examine the stress among GBK as it can affect their personal well-being, productivity and quality of service. Therefore, this systematic literature review (SLR) wasconducted with the aim of identifying stress factors among GBK. The study was conducted by obtaining data and findings from reading materials through search engines such as Scopus, Sage and Eric. The results of the analysis found that there are several factors that trigger stress among GBK and they have been grouped into two main factors, namely personal and profesional factors. Although there are studies done on the stress factor of GBK, it is still not conducted in depth, especially in the country. Therefore, it is hoped that the relevant studies in this field can be further enhanced in the future.
Fan Tokens Analysis: Reshaping Sports Industry with Non-Fungible Tokens
Authors: Pooja Singhal, Ritu Gupta, Shashi Bhushan, Avishek Choudhuri
Published: December 2023

Purpose: This analysis examines the trend of using non-fungible tokens (NFTs) to represent digital collectibles, specifically in the context of the sports industry. The use of NFTs, or NFT-FAN tokens, allows for the creation of unique and verifiable digital collectibles that can be bought, sold, and traded on Blockchain marketplaces. This trend has the potential to reshape the sports industry by providing new revenue streams for teams and players, as well as giving fans a new way to engage with and show support for their favourite teams and players. The analysis will explore the current state of the NFT-FAN token market, its potential impact on the sports industry, and the challenges and opportunities it presents.

Design/Methodology/Approach: This research attempts to identify the market cap for NFT Tokens with UFA Champions Football League case study analysis in three stages. The first stage is Data preprocessing, followed by market analysis and finally observed market cap ups and downs along with types of NFT Tokens.

Findings: The finding shows 1) Most and Least Traded Token in Last 24 Hours 2) Lowest and Maximum priced FAN Token

Originality//Value: This research brings the importance of FAN Token in the field of sports industry and trend for NFTs Token Market Cap in sports industry.
Visual Inspection Checklist For Concrete Structures (As A Part Of Structural Health Assessment)
Authors: Sangeeta Pandey, Ajay Kumar Sinha
Published: December 2023

Visual inspection of deteriorating reinforced concrete structures is an integral aspect of routine assessment practices. Data collected through visual inspection is primarily qualitative and subjective because it rarely involves. This article provides structural inspectors and facility engineers the methodology for visually inspecting any civil structure in a systematic way. The subject article is not prescribing any repair, retrofitting and rehabilitation methodologies. With help of basic design, technical and geographical information, inspectors can add value to the evaluation of deteriorated condition of structures. It is primarily for evaluating general civil structures, like buildings, retaining walls, transmission line towers, this study includes all the civil concrete structures, steel structures and pre-fabricated structures.
Application Of Rd-Fuzzy Topology In Selection Of Roads To Travel Based On Traffic
Authors: K. Kamala
Published: December 2023

Road traffic is the main concern of every person who is travelling along the road.  This traffic directly related with the hurdles persisting among the roads. These hurdles measured as a value between 0 to 1, which  in turn satisfy the topological axioms called rd-fuzzy topology. The best routes for the drivers  discovered by treating the overall road network of the city as graph and using the cyclic path covering number of graphs and compactness property of rd-fuzzy topology. Finally, some of the basic topological properties proved in this rd-fuzzy topology.