bluuebunny/crossref_metadata_2025 huggingface dataset:
Smoker Detection Using Machine Learning
Authors: Irtiqa Showkat, Er. Vivek Gupta
Published: December 2023

Smoking remains a persistent global public health challenge, posing severe health risks and socioeconomic burdens. Early detection of smokers is crucial for implementing targeted interventions, public health campaigns, and personalized support to mitigate smoking-related illnesses. The dataset used in this research comprises extensive information on individual health, encompassing age, height, weight, waist circumference, visual acuity in both eyes, hearing capability in both ears, systolic and diastolic blood pressure, cholesterol levels (HDL, LDL, and triglycerides), haemoglobin levels, urine protein content, liver enzymes (AST, ALT, and GTP), presence of dental caries, and fasting blood sugar. Four For the smoking identification job, cutting-edge machine learning methods including logic regression, Gaussian Naive Bayes, Random Forest Classifier, and XGBoost Classifier are used. Each algorithm is tested using a variety of efficiency indicators after being trained on the data set used for training., including accuracy, ROC-AUC scores, and confusion matrices.The outcomes show the efficiency of the Random Forest Classifier, demonstrating an excellent precision of around 79.4% in correctly identifying smokers. This model outperforms the other algorithms and proves to be a robust approach for smoker detection based on the provided health parameters. Furthermore, the study delves into the interpretability of the models, analyzing the significance of different health parameters in predicting smoking behaviour. Insights gained from the feature importance analysis offer valuable guidance for public health practitioners and policymakers in designing targeted interventions.
Detection Of Stress And Its Levels Through Sleep Using AI
Authors: Maleeha Jeelani, Er. Yuvika
Published: December 2023

In this work, stress level detection during sleep using AI and physiological signals is explored. Stress and sleep, as well as their interplay in maintaining physical and mental health, are investigated. The potential of AI models in sleep monitoring and stress level detection is examined. Wearable technology is used to gather physiological signals such as breathing, heart rate, body temp, and others. To assure data quality, careful data preparation is carried out. and extraction of relevant features necessary for accurate stress level prediction. Two AI models, namely K-Nearest Neighbors (KNN) and Random Forest classifiers, are utilized for stress level prediction on a balanced dataset. To pinpoint the most important factors in stress level estimation, a feature significance analysis is also carried out. Remarkable accuracy is achieved by the AI models, with stress level prediction accuracy reaching approximately 99%. The potential of AI and physiological signals in revolutionizing stress assessment methodologies is highlighted. Broader implications of the findings, particularly the relationship between sleep and stress, are discussed, and ethical considerations, including data privacy and responsible AI implementation, are addressed. The integration of AI-based sleep analysis into wearable devices is envisioned, empowering individuals to manage stress and optimize their well-being. Valuable insights into stress level detection during sleep are contributed by this thesis, propelling forward the field of AI-driven healthcare applications. The groundwork for future advancements in stress management strategies and personalized healthcare solutions is laid by this research.
The Intersectional Exploitation And The Situational Irony Of Disabled Lumpenproletariat In The Novel Motherless Brooklyn
Authors: Sakthi Prasath, Mohammed Nazar
Published: December 2023

This paper explores marginalization effected by the intersectionality of psychological disability and class-based subalternity in the novel ‘Motherless Brooklyn’. Through analyzing characters afflicted by psychological disabilities and how their class situations lead them to involve themselves in exploitatory endeavors, this paper focuses on the structure of exploitation made possible by the intersectionaliity of both disability and class-based marginalization.
A Study on Factors Influencing the Perception and Preference of Investor’s Behaviour towards Stock Broking Services
Authors: Dr. R. Sucharitha
Published: December 2023

Investor behavior towards stockbroking services is shaped by a combination of perception and preference, influenced by various factors. Investors often form perceptions of stockbrokers based on their reputation, reliability, and the quality of services offered. These perceptions, in turn, impact their preferences when choosing a stockbroking service provider. Perception plays a significant role in investor decision-making. Investors tend to favor stockbrokers with a strong track record of providing accurate market information, timely execution of trades, and transparent fee structures. Positive word-of-mouth recommendations from other investors also contribute to a favorable perception of a stockbroker. Preference is closely tied to investors' individual goals and risk tolerance. Some investors prefer full-service brokerage firms that offer personalized advice, research reports, and a range of financial products and services. These investors value the expertise and guidance provided by their brokers. On the other hand, cost-conscious investors may prefer discount brokerage platforms that offer lower commission fees and more control over their trades. Technological advancements have had a profound impact on investor preferences. Many investors today favor online and mobile trading platforms that provide easy access to real-time market data, research tools, and the ability to execute trades from anywhere. These platforms have become increasingly popular, especially among younger, tech-savvy investors. Regulatory compliance and security are also critical factors influencing investor behavior. Investors prefer stockbroking services that adhere to stringent regulatory standards and prioritize the security of their investments and personal information. In summary, investors' perceptions and preferences regarding stockbroking services are shaped by factors such as reputation, reliability, cost, technological offerings, and regulatory compliance. As the investment landscape continues to evolve, stockbrokers must adapt to meet the changing expectations and preferences of investors to remain competitive in the market.
Growth, Structural Change and Plantation Crops: A Brief History of Rubber Cultivation in Travancore
Authors: Dr. Lekharani. ML
Published: December 2023

Plantation crops are commodities worthy of serious attention because of the central and longstanding place it has held in the world economy and in the lives of millions of people. The introduction of it marked a distinct departure from the peasant agriculture of Travancore. Large scale mono-culture of these crops for the export market introduced certain new features of structure and pattern. In this chapter a product-oriented approach has been chosen and attention has been focused on a limited number of crops (Coffee, Tea, Rubber) which account for a major share in the plantation economy .One of the few holdovers from the era of the spice trade and colonialism, these crops had been a major traded commodity since the second half of the nineteenth century. Colonial plantations in Travancore had three phases. The first period started in the 1850s, and the first plantation crop was coffee. During the first thirty years after the planting of coffee, the principal form of ownership was proprietary. From the eighties, the ownership structure began to change and partnership firms became common. The main plantation crop of this period was Tea. The rise of rubber cultivation in 1900 introduced changes in the structure of ownership. Before going to the details it would be apt to discuss about the cultivation of these particular crops.
Analyzing the Availability of Raw Materials for Sustainable Fisheries Processing in Micro-Small Enterprises in Kendari, Indonesia
Authors: Roslindah Daeng Siang, Mimit Primyastanto, Pudji Purwanti
Published: December 2023

This study aims to determine the status of raw material availability to support production activities to become a sustainable business. The analytical methods used were location quotients, standard deviation, and relative scale. Several micro and small-scale fish processing businesses in Kendari City such as smoked fish, dried fish, fish balls, somay, shredded fish, spicy anchovy sauce, seaweed chips, crab shell powder, and crab shell chips. All these products have the potential to be developed locally considering that Kendari City is the centre of the fishing industry in Southeast Sulawesi. The fishery products both capture and cultivation in Kendari can fulfill and support product development except for seaweed and crab shell-based products due to the availability of raw materials. Both raw materials must be imported from other regions such as South Konawe and Bombana districts. This study found that the types of fish most widely used in fish processing businesses are kite, skipjack, and tuna. Three statuses of sustainable availability were identified, namely fulfilled, moderately fulfilled, and less fulfilled. There are three categories of sustainable availability status depending on the fishery product business developed: first, fulfilled mainly for smoked fish, fish balls-somay, and shredded fish; second, less fulfilled for dried fish and spicy anchovy sambal; and finally, less fulfilled mainly for seaweed chips and crab shell powder and chips.
Guideline for Chinese-Foreign Cooperation Project Management in Higher Education in China
Authors: Sarayuth Sethakhajorn Liang Shujie, Patchara Dechhome,  Niran Sutheeniran,
Published: December 2023

The objectives of this research were 1) to study the current situation of Chinese-foreign cooperation project management in higher education in China, 2) to develop the guidelines for Chinese-foreign cooperation project management in higher education in China, and 3) to evaluate the adaptability and feasibility of the guidelines for Chinese-foreign cooperation project management in higher education in China. The sample were 226 lecturers and administrators in higher education in China. They were selected by systematic random sampling and sample random sampling. The interview group was the 1 administrator from each university, totaling 10 people. The experts for evaluated the adaptability and feasibility of the guidelines for Chinese-foreign cooperation project management consisted of high-level administrators from each university, totaling 9 people. Research instruments include: 1) questionnaire, 2) structured interview, and 3) evaluation form. data analysis by using percentage, mean, standard deviation and content analysis.
Architectural Expression and Identity- Case Studies in Iconic Building Designs
Authors: Abbas Ali Hamza Mouthana Mahmood Al-Hoshary ,
Published: December 2023

The practice of expressing cultural identity through architecture is not limited to Kuwait but is widespread. It focuses on architects and the methods they use to reflect cultural identity in their built environments. The main goal is to learn more about their viewpoints and recent work in this area. The latter half of the 20th century saw significant architectural changes in Iraq as a result of a variety of reasons, including economic, regional, and global shifts. A study was done with 18 Iraqi architects to examine their opinions and methods for expressing their cultural identity. Focused interviews and a standard questionnaire were employed in this survey to get respondents' opinions. Their architectural works were also examined to provide specific examples of their viewpoints and thought processes.

The study's key conclusion was that Iraqi architects employ a variety of strategies to convey Iraqi cultural identity in their architectural endeavors. To visually represent the spectrum of these strategies and their relationship with architecture in Iraq, a three-dimensional matrix was developed. This matrix serves as a valuable tool for practitioners and architectural critics interested in gaining a deeper understanding of the prevailing trends in achieving cultural identity within Iraqi architecture.

Overall, the study underscores that the pursuit of cultural identity in architecture is a global phenomenon, with architects across different regions grappling with similar issues and employing diverse strategies to express their cultural heritage through their work. This ongoing exploration of identity in architecture contributes to the rich tapestry of architectural expression worldwide.
A Novel Hybrid Approach for Similarity-Based Link Prediction in Complex Networks
Authors: Nirmaljit Singh, Dr. Harmeet Singh
Published: December 2023

Link prediction in complex networks is the challenging task of predicting missing or future connections between nodes. Complex networks, such as social networks, biological networks, and online recommendation systems, are often incomplete, with unknown or unestablished relationships between nodes. Link prediction algorithms fill in these missing links by leveraging the existing network structure and properties. By analyzing network patterns, connectivity, and characteristics, these algorithms can predict the likelihood of future connections, enabling applications such as recommender systems, network analysis, and understanding the dynamics of complex systems. Link prediction is a crucial task for providing relevant recommendations in e-commerce recommender systems. However, it is challenging due to the sparsity of user-item interaction data and the dynamic nature of user preferences. In this paper, we propose a novel hybrid link prediction algorithm that combines the Jaccard Coefficient Similarity Index, Adamic Adar Index, and MapSim Similarity based Index methods. Our algorithm leverages the complementary strengths of each individual method to improve the overall prediction accuracy. The Jaccard Coefficient Similarity Index measures the similarity between two users or items based on the number of shared items or users. The Adamic Adar Index considers the common neighbors between two users or items to predict the link probability. The MapSim Similarity based Index method incorporates the geographic location of users and items to predict the link probability. We evaluate our proposed hybrid algorithm on two real-world e-commerce datasets, and the results show that it outperforms several state-of-the-art link prediction algorithms in terms of accuracy and precision.
Thermal Analysis of Single Slope Solar Still with Sensible Storage Material (Sand)
Authors: Abhilash Kumar Dahayat, Aneesh Somwanshi
Published: December 2023

In this work authors utilized easily available and cheap sensible storage material (sand) in single basin solar still. The sensible storage material helps to enhance the distillate output from solar still during no sunshine hours. The mathematical model of the proposed solar still has been developed and experimentally validated. The numerical computation has been performed to analyse the effect of mass od sand placed beneath the basin plate. It is seen that with increase in mass of sand the overall daily distillate output decreases. When mass of sand increases from 15kg to 60 kg the daily distillate output decrease by 11.8%. The total daily distillate output produced by proposed still for a day in summer for Raipur is 5.32l/m2 which is about 24.4% more than conventional single slope solar still. The total nocturnal production of pure water from the propped still for a day in summer in Raipur climate is 1.37l/m2. The proposed solar still with sand storage is a good available cheap option to increase the overall productivity of conventional single slope solar still.
The Effectiveness of Online Learning Activities Using Google Classroom on Arabic Writing and Reading Skills for Non-Native Speakers in Bahrain
Authors: Siti Mastura Baharudin, Manal Amin AlJarrah
Published: December 2023

Implementing online learning activities through a learning management system as a supplementary tool for motivating and empowering independent learning and Innovative teaching is becoming a crucial part of 21st-century education. Google Classroom is one example of a social learning site that teachers and students use in many educational institutions in Bahrain. In particular, the study aims to determine the effectiveness of online learning activities using Google Classroom on writing and reading skills among year six students. Data were collected with a mixed-methods research paradigm. Altogether, 80 students from Bahrain government primary schools were involved in an intervention program intended to analyze students’ Arabic writing and reading skills as the primary research inquiries of the study. From the said population of respondents, 80 students participated in the experimental group activities and an interview dealing with their experience using Google Classroom. Overall, the findings affirmed that most participants considered Google Classroom an effective supplementary tool for their learning. This study proved that experienced students using Google Classroom influence their fluency in learning Arabic courses, so they found it easier to apply Google Classroom, serving as an advantage.
Moo-ving Forward: How Access to the Internet is Transforming Dairy Farming in Bangladesh
Authors: Farzana Nazera Mohamed Kaisarul Haq, Kazi Tanvir,
Published: December 2023

This research paper examines the impact of internet access on dairy farming in Bangladesh, focusing on the relationship between access to the internet, social media, YouTube, artificial intelligence, and dairy farm performance. The study finds that there is a positive and interconnected relationship between social media, YouTube, and artificial intelligence when combined with internet access, which enhances dairy farm performance. However, the study also reveals a negative association between YouTube and dairy farm performance. The research sheds light on the dynamic landscape of dairy farming in Bangladesh and provides valuable insights into the multifaceted ways in which technology and internet access are transforming the sector. These findings have important implications for the future of the dairy industry in Bangladesh and beyond. It is crucial for dairy farmers to be aware of the potential impact of social media on their business and to take steps to mitigate any negative effects. Overall, this study highlights the transformative role of Internet access in dairy farming and underscores the need for continued research in this area.
E-Learning Websites Selection and Ranking Using Multi-Criteria Decision-Making Methodology
Authors: Parveen Sehgal, Aakash Gupta Divya,
Published: December 2023

The present research places significant importance on the evaluation and prioritization of E-learning platforms based on a predetermined set of selection criteria. The primary aim of this research project is to select and rank E-learning websites. The issue pertaining to the selection and ranking of e-learning platforms is addressed through the utilization of a methodology known as multiple-criteria decision-making (MCDM). The task of choosing E-learning websites is addressed through the application of the multicriteria decision-making (MCDM) technique known as Weighted-Combinative Distance-based Assessment (W-CODAS), which is specifically designed to tackle such issues. In order to assess the suitability and effectiveness of the proposed method, a comparison is made between the results obtained from methodology which is proposed and those obtained from established approaches such as Visekriterijumsko Kompromisno Rangiranje (VIKOR) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The methodology under consideration has not yet been employed to assess, choose, and prioritize the diverse E-learning platforms
Developing an algorithm for Fingerprint Recognition of Newborns and Toddlers based on Deep Neural Networks and Transfer Learning
Authors: Mohammed Shabil, Dr. H.S. Fadewar
Published: December 2023

In the late 19th century, Sir Galton started the use of fingerprint recognition as a method for baby identification. However, even after the extent two centuries, fingerprint identification for newborns and toddlers has not advanced to the same level as that for adults. According to research from the International Centre for Missing and Exploited Children, there is a growing request for fingerprint identification of babies because more than a million of them go missing each year. So, we want to ensure that newborns and toddlers have access to their rights, including vaccinations, healthcare, and nutritional supplements until they reach school age. This paper develops an algorithm that has been used in previous research to recognize newborns and toddlers to produce remarkable and promising results by Pre-trained model deep neural networks and transfer learning to extract the deep features and to get the identification score, aiming to enhance accuracy and validate. The proposed model is validated on the fingerprint dataset of babies named as NITG dataset. The fingerprint recognition model proposed in the research showcases excellent results, where achieved 100% accuracy in training and 90.35% in validation.
Development And Characterization of Magnesium Ion Conducting Biopolymer Electrolyte Using Agar and Magnesium Nitrate Hexa Hydrate
Authors: R. Sujatha ,C. Shanmugapriya
Published: December 2023

The aim of the study was to develop an agar-based biopolymer electrolyte with varying concentrations of Mg(NO3)2.6H2O in double-distilled water as the solvent. To better understand the impact each concentration had on the biopolymer, a range of characterisation techniques were employed - XRD, FTIR, DSC, Ac impedance analysis and transference number measurement being amongst them. XRD was used to determine whether a crystalline or amorphous polymer resulted from the process. Agar and magnesium nitrate may form complexes, according to FTIR. Through the process of differential scanning calorimetery, researchers have pinpointed the glass transition temperatures for a biopolymer electrolyte with superior conductivity. In order to study its performance, a Mg2+ ion primary battery was built using a biopolymer membrane with a highest ionic conducting of 1.74 x 10-4 S cm-1. This electrolyte was comprised of 40% agar and 60% magnesium nitrate in water, and its ionic transference number of Mg2+  had been determined at 0.30 via Evan's methodology.
Impact of social media in enhancing vocabulary and English Language Acquisition – A Study
Authors: Ainum Suhail
Published: December 2023

Social media is a commonly employed method of communication that is pervasive among individuals across many age cohorts. Educational institutions have included social media platforms into their instructional practices to not only facilitate communication, but also to distribute new knowledge and promote active interaction with students. The growing utilization of social media in recent times has prompted a controversial discourse regarding its effectiveness as a pedagogical instrument. The objective of this study is to examine the influence of social media on the acquisition of English vocabulary among student populations. The main aim of this research is to examine the influence of social media on individuals' inclination towards developing competency in language acquisition. Furthermore, this study investigates the influence of these platforms on the process of language acquisition, encompassing both positive and negative consequences. The data and replies for this study were obtained by the presentation of a questionnaire. The questionnaire data was subjected to quantitative analysis. The findings of the research suggest that social media platforms have a significant role in stimulating interest in the acquisition of language among persons engaged in the study of English.
Metacognition-Driven Approach to Enhance Computer Programming Skills: A Multi-Criteria Decision Making Analysis
Authors: C P Pavan Kumar Hota,  V. Asanambigai, D. Lakshmi
Published: December 2023

Computer program writing is thought to be a challenging skill to master by both experts and students. A key component of developing into a successful student [1] of computer programming is metacognition. Instructive brain science defines metacognition  as the monitoring and management of one's cognitive processes. 1145 engineering students were assessed using the personality questionnaires, Brain Dominance, Multiple Intelligences, Learning Style, and Metacognition tests. The two key elements of this study from Multiple Criteria Decision Making (MCDM) methods are the AHP and TOPSIS algorithms, which produce optimal decisions based on priorities. The multiple criteria decision-making process is compatible with both min and max functions. The study's psychology dataset includes four attributes for thinking style, eight for multiple intelligences, five for the personality scale, four for learning style, and eight for the metacognitive awareness inventory. The max function is also used for the learning style and the four attributes for the personality scale. The MCDM's outcome ranking order can be applied to the adoption of instructional strategies, individualized and customized learning directions, activity oriented learning, multiple assessments and attainments, student tracking strategies and techniques, and learning through adaptive tutoring systems. Additionally, it recommends combining a TPACK framework model with metacognition to help learners achieve their goals by navigating the talent, intelligent, and technological spaces while maintaining a high level of self-awareness.
Synthesis, Characterization and In-Vitroanti-Tumor Activity of Some Novel Pyrazole Derivatives
Authors: Kalaivanan.R , S. Senthilkumar , V. Sughanya
Published: December 2023

Pyrazole and its derivatives are considered a pharmacologically important active scaffold that possesses almost all types of pharmacological activities. The presence of this nucleus in pharmacological agents of diverse therapeutic categories such as  a potent anti-inflammatory, antipsychotic , anti-obesity drug, an analgesic, and  anti-depressant agents have proved the pharmacological potential of the pyrazole moiety. Owing to this diversity in the biological field, this nucleus has attracted the attention of many researchers to study its skeleton chemically and biologically. This heterocyclic can be traced in a number of well-established drugs belonging to different categories with diverse therapeutic activities. Aim of the present study is to synthesize and characterize various 4-[1-(2-hydroxy-4,4-dimethyl-6-oxocyclohex-1-en-1-yl)3,4-(phenyl]-5-methyl-2-phenyl-2,4-dihydro-3H-pyrazol-3-one derivatives using an efficient Scheme. The synthesized compounds (1-10) were evaluated for their in vitro anti-cancer activity.In order to develop potent anti-cancer agents, a novel series of pyrazole derivatives were synthesized and the structures of all compounds were confirmed by spectral studies. MTT assay has been employed to study anti-proliferative activity of these compounds with four human cancer cell lineDLD1 (Human Colorectal Adenocarcinoma) at various concentration (6.25-100µg/mL). The results revealed that compounds VEPD and 4MEPD shows goodanti-tumoractivity and the other compounds shows moderate activity against theDLD1cell line.
Secure And Adaptive Data Stream Mining For New Generation Big Data
Authors: A Ravi Kishore, Dr Gururaj Murtugudde
Published: December 2023

As the era of new-generation big data applications unfolds, the need for secure and adaptive data stream mining has become increasingly paramount. Evolving databases, characterized by ever-changing data streams and dynamic data distributions, present unique challenges and opportunities. This paper addresses the crucial intersection of security and adaptability in the context of data stream mining for new-generation big data. First, we delve into the evolving landscape of big data, where real-time data streams from diverse sources drive decision-making processes. Ensuring the privacy and security of sensitive information within these data streams is a fundamental concern. We explore cryptographic techniques, anonymization methods, and access control mechanisms that safeguard data while allowing for meaningful analysis. We present novel adaptive algorithms and model update strategies that can continuously learn and adjust to changing data distributions. These approaches enable data stream mining to remain effective and accurate over time. This paper offers insights into the fusion of security and adaptability in data stream mining, providing a foundation for the development of robust and privacy-conscious solutions for the evolving landscape of new-generation big data applications.
Stock Market Data Using Data Mining For Feature Extraction
Authors: P. Dinesh kumar, Dr. B. Subramani
Published: December 2023

This paper presents a robust approach for feature extraction from stock market data by combining Principal Component Analysis (IPCA) and Moving Averages (MA). IPCA reduces dimensionality, capturing underlying patterns, while MAs identify trends and cyclic behaviors. The synergistic integration of these techniques enhances the extraction of essential features for stock market analysis. Research method effectively uncovers relevant information, offering valuable insights for trading and investment decisions. It addresses dimensionality challenges and identifies meaningful patterns, promoting a deeper understanding of market dynamics.
Crop Yield Prediction Using With The Simulated Annealing Convolutional Neural Networks (SACNNS) Optimization Algorithm
Authors: N. HARSHINI, Dr. M. RATHAMANI
Published: December 2023

This paper introduces an innovative approach for precise crop yield prediction in Tamil Nadu, India, a region with diverse agricultural conditions. The Simulated Annealing Convolutional Neural Networks (SACNNs) optimization algorithm is employed to enhance the performance of Convolutional Neural Networks (CNNs). By leveraging a comprehensive agricultural dataset that includes weather information, soil characteristics, and historical crop yields, SACNNs fine-tunes CNN architecture and parameters for improved prediction accuracy. The model excels at predicting crop yields at a local level, accommodating the unique agricultural conditions of different districts in Tamil Nadu. Experimental results highlight SACNNs' superiority in prediction accuracy and robustness compared to conventional CNNs and other optimization methods. These findings offer valuable insights for informed decision-making in agricultural planning and resource allocation, benefiting farmers, policymakers, and stakeholders in the region.
Heart Disease Feature Extraction Using Machine Learning
Authors: R. Karthikeyan, B. Selvanandhini
Published: December 2023

In the realm of healthcare, accurate and efficient heart disease diagnosis and prediction are crucial tasks. This abstract introduces a study concentrating on feature extraction from heart disease data using an Enhanced Multi-Layer Perceptron (EMLP) neural network. The method combines deep learning with specialized enhancements such as dropout, batch normalization, and gradient clipping. These enhancements enhance accuracy and robustness in feature extraction. The trained enhanced MLP adeptly captures intricate patterns in heart disease data, generating informative and distinguishing features. These features contribute to precise disease diagnosis and prediction. Experimental results underscore the method's effectiveness, advancing heart disease detection and prognostication within medical data analysis.
Torus Ring Signcrypted Kademlia Hash Storage-Based Cryptography For Secured Data Storage In Fog Computing
Authors: V. Suresh Kumar, Dr. P. E. Elango
Published: December 2023

Fog computing is decentralized computing infrastructure which used to present various services. Generally, fog network includes numerous data, computation, storage and application processes among data in the server. The fog node toward large-scale end-users offers a variety of interacted IoT services. Security is main concern for preserving data from unauthorized user access as carry outing secure data storage from fog user as well as server. Thus, secure data storage in fog is a demanding and rising concern for Fog-IoT security paradigms. Several research works have developed for secured fog data storage. But, data security level was minimum because performance of signcryption was not effectual. To overcome such problems, Torus Ring Signcrypted Kademlia Hash Storage (TRSKHS) method is proposed in fog computing. The designed TRSKHS method contains key generation, encryption as well as decryption for enhancing fog data storage by lesser  computational cost. At first, registration process is carried to register fog users detail to the fog server. After registering, fog server generates pair of keys namely  public key and public key for every registered user using Torus Ring Signcryption. Then, the fog user carry outs encryption as well as signature generation process. Initially, fog user data is encrypted in ciphertext as well as signature is generated for each data to provide secured data storage. Before storing encrypted data in server, hash value of each data is generated by using Kademlia Hash function. Then, encrypted user data through their hash value is stored in database. Finally, the decryption and signature verification is performed by the user to decrypt data. After that authorized users attain original data and enhance secure data storage. Through data accessing process, fog data is decrypted by their generated private key. This assists for TRSKHS method to enhance secure data storage by lesser  amount of encryption time. Experimental assessment of TRSKHS method and conventional methods are performed with number of fog users as well as data. Proposed TRSKHS method carries the experimental evaluation using encryption time, storage complexity, data confidentiality rate as well as data integrity rate.
User’s Learning Capability Aware E-Content Recommendation System For Enhanced Learning Experience
Authors: P. Vijaya kumar, G. Jagatheesh kumar
Published: December 2023

E-learning is inevitable during these pandemic days and most of the learners find it comfortable to learn online. However, the main challenge is to locate the appropriate data in line with the learner’s requirement. Considering the necessity of this issue, this article presents an e-content recommendation system that considers the user’s learning capability. This work categorizes the documents into three categories such as basic, intermediate and advanced levels. Based on the users’ learning capability, corresponding documents are recommended and this idea enhances the overall learning experience. This work is based on three phases such as data pre-processing, feature extraction and classification. The collected documents are pre-processed for preparing the documents suitable for further processes. Features such as Parts-OF-Speech (POS) tagging, Term Frequency - Inverse Document Frequency (TF-IDF) and semantic similarity based on WordNet are extracted and the multiclass Support Vector Machine (SVM) is employed for distinguishing between the classes. The performance of the work is tested and the results prove the efficacy of the work with 98% accuracy rates, in contrast to the comparative techniques.
Finite element solutions for bending analysis of simply supported two layered un-symmetric cross-ply (0/90) square plate under uniformly distributed transverse load
Authors: Mahaboobali Nadaf, Dr. R. J. Fernandes
Published: December 2023

Laminated composite plates are extensively used in aerospace, military, civil infrastructure, and automotive industries due to their benefits, including superior fatigue resistance, high strength, and lightweight properties. Customizable fiber orientation, materials, and stacking patterns are additional advantages. Therefore, conducting rigorous analyses to evaluate deflections and stress levels in these plates is essential for maintaining structural integrity and optimal performance across various applications. The solutions presented in this study rely on the utilization of a higher-order shear deformation theory (HSDT), which offers a notably precise depiction of the plate's behavior. The theoretical model employed in this research accounts for deformations and the variation of stresses along the thickness direction. Finite element formulations are employed to derive the solutions. In order to ascertain the accuracy and reliability of the current formulations and solution methodologies, the obtained results are systematically compared with those documented in the existing literature. This rigorous comparison serves as a robust validation of the approach applied in this study. Once the accuracy of the solutions is established, numerical results for two layered cross-ply composites with real material properties are presented. These results are in good agreement with the findings reported in the literature. By incorporating Higher Order Shear Deformation Theory (HSDT), the accuracy of predictions is significantly enhanced. This improvement ensures a more dependable foundation for the design and optimization of composite structures.
ResNet50: Automated Fabric Defect Detection and Classification based on a Deep Learning Approach
Authors: K. Sharath Kumar, Dr. M. Rama Bai
Published: December 2023

"The demand for an intelligent computer-based system for visual inspection is on the rise in the textile business, especially among those who place a premium on textile quality. In this research, we present a fully automated AI-driven algorithm for defect detection in fabric, one that makes use of deep neural network models that have already been pre-trained. In order to train the networks, the fabric images go through a series of pre-processing steps where typical image processing techniques are applied. In order to train and categorize various fabric flaws, we use Deep Convolutional Neural Networks (DCNNs) and the pre-trained ResNeT network. The system obtains a best classification accuracy of 96.40% in simulations utilizing preexisting textile datasets. This model's detection and classification system can help human operators spot flaws in the fabric manufacturing process with this degree of precision."
Effect Of Working Capital Management On The Profitability Of Selected Manufacturing Companies In India
Authors: Siddalingeshwara S
Published: December 2023

Throughout the long period there has been a major discussion or conversation on the impact of working capital on the profitability of the companies. Working Capital is one of the major important part in each companies or businesses and also in decision of financial system or management. The major role for any companies and its department of corporate divisions is its WCM. The basic concept of WCM is net working capital balance  alongside the overheads of different divisions of profitability. It is essential for the day to day costs of the companies. Examining the relationship between working capital management's effect on Indian co’s profitability is the primary goal of this article. The sample size of this research are selected 20 manufacturing companies of India  from different sectors such as, IT, FMCG, Cement and steel  sectors and selected five companies from each sectors based on market size for a period of five years from 2018-19 to 2022-23 and analysis is evaluated based on different factors. or financial ratios which are given under GP, net profit, inventory turnover ratio, capital employed returns and working capital ratio. Information was gathered from the official yearly reports and analysis were applied with determined that Pearson's coefficient of correlation knows the outcomes and performance b/n different variables used and applying the SPSS statistical software tool, with a 5% significance level. However, this study discovered and demonstrated a favorable connection of the influence of working capital and other parameters, as well as the considerable interaction between the variables.
Assessing the Performance of a Hybrid Geolocation Algorithm Integrating FP and TOA Techniques across Diverse Environmental Conditions
Authors: Saidur Rahman Humayun Kabir, Alomgir Kabir, Rubab Ahmmed,
Published: December 2023

This paper presents a validation study of indoor geolocation accuracies using a hybrid approach that combines fingerprinting (FP) and time of arrival (TOA) techniques. The investigation focuses on three dense environments, examining the influence of furniture density and multipath components on geolocation accuracy, particularly in non-line-of-sight (NLOS) scenarios. The results indicate that geolocation performance improves in denser environments with higher furniture density due to increased multipath components. Additionally, optimizing the hybrid method with a polygon size of 50 cm and sampling rate of 80 GHz leads to further accuracy enhancements. These findings underscore the significance of furniture density and demonstrate the effectiveness of the hybrid method in addressing NLOS challenges. The research contributes to the advancement of indoor geolocation techniques and provides valuable insights for designing precise indoor positioning systems across various applications.
Investigating and Optimizing Factors Affecting Thinning in Sheet Deep Drawing through Simulation and Taguchi Experimental Planning
Authors: Huy Trieu Quy
Published: December 2023

This research endeavors to explore the intricate dynamics of factors influencing the thinning process in flat sheet deep-drawing process. It delves into the optimization of crucial parameters that shape the outcome of the deep-drawing process, including punch radius (Rp), die radius (Rd), and workpiece clamp force (F). Employing advanced CAE (Computer-Aided Engineering) software, this study harnesses finite element analysis to simulate the deformation experienced during the stamping process. By doing so, it not only provides a comprehensive understanding of the thinning process but also predicts the material's formability. Furthermore, it aims to determine the optimal parameters for this stamping process. In addition to simulation, this research employs the Taguchi orthogonal array experimental planning method and utilizes analysis of variance (ANOVA) to evaluate the influence of these parameters, quantifying their impact in terms of percentages. The findings revealed that die radius exerts the most significant influence among the three factors, accounting for 90.02% of the variability, followed by punch radius at 9.56%. In contrast, the workpiece blocking force falls within an insignificant range, making up only 0.42% of the overall impact.
Result on The Weak Non-Split Independent Domination Number of Some Special Graphs
Authors: Dr. B. Uma Devi, S. M. Ambika, , R. K. Shanmugha Priya
Published: December 2023

A graph   has a vertex set  and edge set  and  .  A non - empty subset D is an independent dominating set if every vertex in     is adjacent a vertex in  and   is a non – adjacent vertices. The domination number is the minimum cardinality of an independent dominating set. If, in addition let  and  be the element of .  Then  weakly dominates  if  (i)  and (ii) . A set S is a weak non-split dominating set of  if every vertex is   is weakly dominated by at least one vertex in  and the induced subgraph  is connected. The minimum cardinality of a weak non-split independent dominating set is the weak non-split independent domination number of  G .The  main purpose of this paper is to introduce the concept of weak non-split independent domination number.  For that  we have chosen Soifer graph, Chvatal graph, Fritsch graph, Herschel graph, Moser graph, Franklin graph to find the weak non-split domination number.
Evaluation of Life Skills-Based Open High School Program in Bogor Regency, West Java
Authors: Ivan Hanafi, Elianasari Ade Noor Syamsudin,
Published: December 2023

Equitable access and low quality of learning are the main problems of education in Indonesia. Open schools are one of the solutions to the problems of education in Indonesia, especially at the secondary school level. The purpose of this study is to evaluate the implementation of the life skills-based open high school program in Bogor Regency, West Java and produce an implementation model of the life skills-based open high school program  at the high school level. This evaluation research uses qualitative methods with the CIPP model. The data in this study were obtained through observation, in-depth interviews and documentation studies at the SMA organizer of SMA Terbuka in Bogor Regency: SMAS PuspaMekarCiteureup
The Constructivist Approach to Student's Metacognitive Formation in Elementary School
Authors: Tri MarhaeniPudji Astuti Herpratiwi, Dwi Yulianti, Handoko ,
Published: December 2023

Success in education and learning is determined by the willingness and ability of students in designing and planning learning. The ability to plan, design, implement, and evaluate learning is included in metacognition. The urgency in the research is that not all teachers are able to design a learning approach for the development of students' metacognitive abilities. Some of the learning approaches implemented by the teacher are still not applicable. If this is allowed, it will have an impact on students' low metacognition. The type of research used is pre-experimental One Group Pre-test – Post Test Design.The research target schools were 186 Bandar Lampung State Elementary Schools. The sample was taken by stratified random sampling, namely SD with an Accreditation so that 40 State Elementary Schools were obtained. The research subjects were students in grades 1, 2 and 3 in each sample school. The results showed that the constructivist approach can improve students' metacognitive abilities. Students who have high metacognitive abilities can develop knowledge, find ideas, conclude lessons, develop problem solving strategies, estimate time estimates, have high creativity and have independence in learning.
Learning to Write Explanatory and Narrative Textsin the Development of Student Character Values and its Implementation at Tt-Jbi High School
Authors: Wiwik Pudjaningsih Herman Budiyono, Priyanto,
Published: December 2023

The purpose of this study is to describe the implementation of the learning process of writing explanatory texts and narratives in the development of character values of TT-JBI HIGH SCHOOL students. This research has a descriptive-qualitative approach. The learning process of writing explanatory texts and narratives in the development of student character values is analyzed and described. The steps of data analysis of this research are as follows: data reduction, data presentation, and drawing conclusions. The results showed that the learning process of writing explanatory texts and narratives in the development of character values of TT-JBI High School students was carried out in stages, namely (1) context development; (2) model review; and (3) learning conclusions. The character values developed in the learning are character values: religious, discipline, curiosity, hard work, honesty, love to read, creative, friendly / communicative, democratic, tolerance, independence, and responsibility.
Exploration of College Students’ Psychological Anxiety towards Modular Distance Learning During a Pandemic.
Authors: Camarista Genesis Gabion
Published: December 2023

The study aimed to investigate the anxiety levels among college students during the COVID-19 pandemic towards modular distance learning (MDL). The survey involved 336 students from Southern Negros Occidental, Philippines, enrolled in eight degree programs. Results showed high anxiety levels, with students with good internet connections having moderate anxiety levels. Those with two gadgets showed significant increases in anxiety. Teachers' motivation through printed modules for practice was an essential factor.