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Self compacting concrete for slip form paving
International Journal of Research in Engineering and Technology, Vol-4(7), ISSN-2319-1163 -
Self Compacting Concrete for Slip from Paving
Volume:04, Issue:07, July -
Rating-Based Cyberbullying Detection with Text, Emojis on Social Media
In the dynamic landscape of online interactions, cyberbullying has become pervasive, profoundly impacting user's digital well-being. Public figures, especially celebrities and influencers, face heightened vulnerability to online harassment, exacerbated by the post-pandemic surge in social media usage. To address this challenge, our research adopts a holistic approach to detect cyberbullying in text, considering both textual content and the nuanced expressions conveyed through emojis on social media platforms. We employed a diverse set of machine learning and deep learning models, including Support Vector Classifier, Logistic Regression, Random Forest, XGBoost, LSTM, Bi-LSTM, GRU, and Bi-GRU, to accurately classify non cyberbullying or cyberbullying text. Beyond classification, our study introduces an offensive rating system, assigning severity ratings on a 1-5 scale to identify cyberbullying instances. A critical aspect is the establishment of a threshold value which depends on user security and safety ethics of different social media platforms; texts surpassing this trigger an automatic recommendation to block the user, ensuring a proactive response to minimize harm. This recent contribution not only comprehensively addresses cyberbullying but also empowers society. 2024 IEEE. -
Koppa Archaeological Research Project (KARP): Exclusive iron age landscape in the Western Ghats, India /
Archaeological Research In Asia, Vol.17, pp.173-180, ISSN No: 2352-2267. -
Koppa Archaeological Research Project (KARP): Exclusive Iron Age landscapes in the Western Ghats, India
Koppa Archaeological Research Project (KARP) conducted systematic transect surveys and excavations in the Coorg plateau at the upper reaches of river Kaveri in the Western Ghats, Karnataka. The archaeological record of the study area is exclusively an Iron Age landscape, as we did not find any other prehistoric or early historic sites. We focused on studying thirty-nine Iron Age (1200 BCE300 BCE) sites of two categories, habitation and megaliths, and we present here our preliminary observations addressing to site findings, their landscapes and settlement patterns. We then consider the geographical and climatic implications for understanding the formation of their economy and politics. We argue that sites in the Western Ghats did not differ significantly from the temporal and cultural material typologies of contemporary Iron Age sites in the semi-arid or savannah conditions of Deccan plateau. Yet the adaption of Iron Age communities to Western Ghats climate which is characterized by high rainfall, lush tropical forests, fallow lands and diverse faunal ecology should have had distinct implications for the development of their economy and politics. 2019 Elsevier Ltd -
Advancements in Computational Modeling for Enhancing Financial Risk Management: Applications, Challenges, and Future Directions
The advancement in risk management with deeper insights and more accurate predictions amidst complex data landscapes is attributed to computational modeling. It offers sophisticated tools to analyze, forecast, and mitigate risks in the dynamic financial market. This research article discusses integrating machine learning, network analysis, and other techniques to enhance risk identification, scenario analysis, and decision support in financial institutions. This article also addresses the importance of data quality, model validation, and transparency in ensuring the reliability and effectiveness of computational models. The application of machine learning techniques in credit risk assessment, market risk analysis, stress testing, scenario analysis, sensitivity analysis, portfolio management, and optimization is discussed. The study has demonstrated the conceptual model where identifying the type of risks is the first step, followed by sourcing the data internally and externally, considering the accuracyand reflection of current market conditions. Choosing the right computational techniques occupies an important stage due to the availability of both traditional and modern techniques. Traditional techniques are equally important to modern techniques, but this comes with challenges. Further risk management processes can be initiated to address the identified risks proactively and reduce potential financial losses. Finally, the study outlines future trends and technological advancements that promise to shape the future of computational modeling in financial risk management. 2025, Bentham Books imprint. -
Serverless Data Processing System and its Design Space Consideration
Serverless computing is becoming increasingly important in data-processing applications in science and business. The scheduler is at the centre of serverless data-processing systems, allowing for dynamic decisions on job and data placement. The complex design space, which is influenced by various user, cluster, and workload variables, presents problems for developing high-performance and cost-effective scheduling structures and processes. To make this exploration easier, we present Sched-Probe, a framework that includes a conceptual model and simulator for systematic design space exploration. Using the Sched-Probe framework, we evaluate the performance of three scheduling systems and two techniques using real-world workloads. Our open-source software is now available on ExDe, allowing system designers to collaborate on delving into the complexity of serverless scheduling, paving the way for optimised and efficient data-processing systems. 2024, Iquz Galaxy Publisher. All rights reserved. -
Optical Character Recognition system with Projection Profile based segmentation and Deep Learning Techniques
Optical character recognition is the solution to convert text from printed or scanned documents into editable data. This project is aimed at building a Optical character recognition system that recognizes digital text. A document is first detected using contour-based detection technique without altering the angle of the image and is segmented into lines, once the lines are segmented the words embedded in them are extracted. This segmentation is done using projection profiling method. Characters are then segmented words with vertical projection profiling from the extracted words. These characters are fed into an image recognition model for recognition. The recognition model is CNN based deep learning model. Modified VGG16 architecture is used here to extract maximum features from the images and then classify them. To train the model a dataset is created from a repository of digital character dataset. The dataset consists of images of 153 font variants. 2022 IEEE. -
The role of audiobooks in developing English listening proficiency: A study of undergraduate learner in Tamil Nadu
Listening is one of the inevitable yet often overlooked skills in the process of learning a second language, especially for Indian undergraduate students learning English as a Second Language (ESL). Even after years of studying English in school, many students still struggle to understand spoken English. This occurs because students do not get sufficient exposure to real English as spoken in daily life, and they often feel nervous or uncomfortable in classroom settings. Various apps and tools are used to facilitate language learning, but there remains limited understanding of how audiobooks specifically contribute to enhancing listening skills. This study examines the effectiveness of audiobooks in developing listening skills through a twelve-week program involving 66 undergraduate students from Tamil Nadu. Students participated in a pre-test before the intervention and a post-test after completing the twelve-week audiobook listening program, which involved daily sessions of one hour. They listened to audiobooks with teacher support initially and then independently. The results indicated a significant improvement in listening scores, with an average increase of 25%. Additionally, students reported feeling less anxious about listening, better retention of new vocabulary, and improved ability to follow spoken English. The findings suggest that audiobooks are a vital component of language learning, providing autonomous, low-pressure, and long-term benefits in listening development. Consequently, audiobooks enhance confidence and foster consistent listening skills among ESL learners. The study recommends integrating audiobooks into regular classroom activities to support language acquisition. Contribution/Originality: This study contributes to the existing literature by validating audiobooks as effective ESL tools. It uses a new estimation methodology through SPSS analysis, originates a formula linking Krashens Affective Filter with audiobook learning, and is one of the few studies on Tamil Nadu undergraduates. The paper contributes quantitative and emotional factors, documents progress, and finds that audiobooks enhance proficiency. 2025 AESS Publications. All Rights Reserved. -
An Analysis of Levenshtein Distance Using Dynamic Programming Method
An edit distance (or Levenshtein distance) amongst dual verses refers to the slightest amount of replacements, additions and omissions of signs essential to turn one name addicted to the additional is referred to as the edit distance (or Levenshtein distance) amongst dual verses. The challenge of calculating the edit distance of a consistent verbal, that is the set of verses recognised by a fixed mechanism, is addressed in this research. The Levenshtein distance is a straightforward metric for calculating the distance amongst dual words using a string approximation. After witnessing its efficiency, this approach was refined by combining certain comparable letters and minimising the biased modification between associates of the similar set. The findings displayed a considerable enhancement over the old Levenshtein distance method. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Application of data analytics principles in healthcare
Information technology has transformed the healthcare field worldwide. In many areas of the healthcare industry, implementations of data analytics tools are commonly used recently. Applying data analytics principles in medical sciences appropriately transforms the mere storage of medical records in to discovery of drugs. Data science and analytics are essential tools because they can help make better decisions when it comes to spending and reducing inefficiencies in healthcare. The proposed model of healthcare data analytics provides a framework to accelerate the adoption and implementation of predictive analytics in healthcare. Healthcare data analytics can be applied to prove formulated hypotheses, test those using standard analytics models and predict patient health conditions. It can be used to classify patients at risk of developing diseases such as diabetes, asthma, and other life-long illnesses. In spite of the challenges faced while applying data science predictive analytics in the healthcare environment, there is an enormous opportunity for its usage in providing quality healthcare for patients. BEIESP. -
Service request scheduling based on quantification principle using conjoint analysis and Z-score in cloud
Service request scheduling has a major impact on the performance of the service processing design in a large-scale distributed computing environment like cloud systems. It is desirable to have a service request scheduling principle that evenly distributes the workload among the servers, according to their capacities. The capacities of the servers are termed high or low relative to one another. Therefore, there is a need to quantify the server capacity to overcome this subjective assessment. Subsequently, a method to split and distribute the service requests based on this quantified server capacity is also needed. The novelty of this research paper is to address these requirements by devising a service request scheduling principle for a heterogeneous distributed system using appropriate statistical methods, namely Conjoint analysis and Z-score. Suitable experiments were conducted and the experimental results show considerable improvement in the performance of the designed service request scheduling principle compared to a few other existing principles. Areas of further improvement have also been identified and presented. Copyright 2018 Institute of Advanced Engineering and Science. All rights reserved. -
Capacity Aware Active Monitoring Load Balancing Principle for Private Cloud
Virtual machines (VMs) are the basic compute elements in cloud computing. There are load balancing principles associated with a job scheduler assigns the requests to these computing elements. Deploying an effective load balancing principle enhances better performance that ultimately achieves users satisfaction at the high level. Assigning an equal requests load appropriate to the capacity of the VMs will be a fair principle that can be the objective of any load balancing principle. Active monitoring load balancing principle assigns the requests to a server based on the pre-computed threshold limit. This paper presents a technique for assessing the capacity of the VMs based on a common attribute. This work measures each VMs processing ability as a percentage using the statistical method called Z-score. A threshold is quantified and the requests are proportioned based on this threshold value. Each server is then assigned with the proportioned requests. Suitable experiments were conducted Requests Assignment Simulator (RAS), a customized cloud simulator. The results prove that the performance of the proposed principle is comparatively better than a few load balancing principles. Areas of future extension of this work were also identified. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Evaluation and applying feature extraction techniques for face detection and recognition
Detecting the image and identifying the face has become important in the field of computer vision in recognizing and analyzing, reconstructing into 3D, and labelling the image. Feature extraction is usually the first stage in detection and recognition of the image processing and computer vision. It supports the conversion of the image into a quantitative data. Later, this converted data can be used for labelling, classifying and recognizing a model. In this paper, performance of such feature extraction techniques viz. Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG) and Convolutional Neural Network (CNN) technique are applied to detect and recognize the face. The experiments conducted with a data set addressing the issues like pose variation, facial expression and intensity of light. The efficiency of the algorithms was evaluated based on the computational time and accuracy rate. 2018 Institute of Advanced Engineering and Science. All rights reserved. -
Celebrity endorsements the interplay between intellectual property law and the consumer protection act, 2019
The ambit of Copyright law has expanded over time, leading to development of newer concepts such as, Personality Rights. These rights are vested in individuals who have acquired an identifiable persona in the eyes of the public. There are two important facets to personality rights-Right to Publicity &Right to Privacy. When such identifiable identities use their acquired celebrity status to promote goods and services of a company to attract more consumers, it can be understood as Celebrity Endorsements. This is the most common source of marketing used by major companies to increase sales and garner goodwill and reputation. However, this source of communicating necessary information to the public becomes dangerous when celebrities promote false or misleading advertisements. To counter such issues, the Consumer Protection Act, 2019 introduced provisions tohold celebrities endorsing such products or services to be liable for injury suffered by consumers. The Law mandates that in order to ensure that such misleading advertisements arent promoted, the celebrities must conduct due diligence of the products before endorsing them. However, the question remains that to what extent can celebrities, who are not directly involved in production or manufacturing, be held liable for exploiting their personality rights? This paper aims at addressing the newly created legal interlink between personality rights via celebrity endorsements and protection of consumer interests. 2020, National Institute of Science Communication and Information Resources. All rights reserved. -
Data Reduction Techniques in Wireless Sensor Networks with AI
Due to their numerous uses in practically every part of life and their related problems, such as energy saving, a longer life cycle, and better resource usage, the research of wireless sensor networks is ongoing. Its extensive use successfully saves and processes a considerable volume of sensor data. Since the sensor nodes are frequently placed in challenging locations where less expensive resources are required for data collection and processing, this presents a new difficulty. One method for minimizing the quantity of sensor data is data reduction. A review of data reduction methods has been provided in this publication. The different data reduction approaches that have been put forth over the years have been examined, along with their advantages and disadvantages, ways in which they can be helpful, and whether or not using them in contexts with limited resources is worthwhile. 2022 IEEE. -
Corrosion behaviour in friction stir processed and welded materials
This chapter presents a comprehensive study on the influence of friction stir processing/welding (FSW/FSP) on corrosion behaviour. It briefly discusses the different aspects of corrosion including corrosion types, measurement techniques and data analysis. The corrosion behaviour of a wide range of friction stir processed materials, including light weight metals such as magnesium and aluminum alloys, as well as high strength metals such as steel, has been discussed in detail. The influence of FSP parameters on the microstructural evolution, comprising grain-size and precipitate refinement along with its correlation with the corrosion properties, has been described for different materials. 2014 The authors and contributors. All rights reserved. -
Visible light active bismuth chromate/curcuma longa heterostructure for enhancing photocatalytic activity
Bismuth chromate nanostructures were fabricated via hydrolysis technique using curcuma longa for enhancing the photocatalytic activity. The analytes have been labelled as Bi2CrO6-C, when prepared without using curcuma longa and Bi2CrO6-G, prepared using curcuma longa extract (Bi2CrO6/Curcuma longa). The as-fabricated catalysts have been confirmed via characterization techniques including X-ray diffraction, Transmission electron microscopy (TEM), and Field emission scanning electron microscopy (FESEM), UVVis. DRS. The as-synthesised analytes have been evaluated their photocatalytic efficiency via photodegradation of an organic pollutant, Methyl Orange (MO). The current research findings imposed the effect of inculcation of a green extract curcuma longa reduces particle size and increases surface area of the material and moreover makes heterostructure with Bismuth chromate and inhibits recombination of photogenerated charges for efficient degradation of the organic pollutant. Bi2CrO6-G demonstrates here enhanced photocatalytic activity as compared to Bi2CrO6-C. Akadiai Kiad Budapest, Hungary 2024. -
Diabetes mellitus prediction using machine learning within the scope of a generic framework
Artificial intelligence (AI) based automated disease prediction has recently taken a significant place in the field of health informatics. However, due to unavailability of real time large scale medical data, the dynamic learning of prediction models remains principally subsided. This paper, therefore proposes a dynamic predictive modelling framework for chronic diseases prediction in real-time. The framework premise suggests creation of a centralized patient-indexed medical database to dynamically train machine learning (ML) models and predict risk levels of chronic diseases in real time. In this study, comprehensive empirical evaluations to train seven state-of-the-art ML models for diabetes risk prediction are performed in context of phase 2 of the suggested framework. The selected optimal model can then be dynamically applied to predict diabetes in phase 3 of the framework. Various metrics such as accuracy, precision, Recall, F1-score and receiver operating characteristic (ROC) curve are employed for evaluating performances of the trained models. Parameter tunings using different type of kernels, different number of neighbors and estimators are rigorously performed in order to create a suggestive literature for healthcare prediction ecosystem. Comparative analysis indicates high prediction accuracies on diabetes test data records for neural network and support vector machine (SVM) models as compared to other applied models. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
An enhanced predictive modelling framework for highly accurate non-alcoholic fatty liver disease forecasting
Non-alcoholic fatty liver disease (NAFLD) is a chronic medical ailment characterized by accumulation of excessive fat in the liver of non-alcoholic patients. In absence of any early visible indications, application of machine learning based predictive techniques for early prediction of NAFLD are quite beneficial. The objective of this paper is to present a complete framework for guided development of varied predictive machine learning models and predict NAFLD disease with high accuracy. The framework employs stepby-step data quality enhancement to medical data such as cleaning, normalization, data upscaling using SMOTE (for handling class imbalances) and correlation analysis-based feature selection to predict NAFLD with high accuracy using only clinically recorded identifiers. Comprehensive comparative analysis of prediction results of seven machine learning predictive models is done using unprocessed as well as quality enhanced data. As per the observed results, XGBoost, random forest and neural network machine learning models reported significantly higher accuracies with improved AUC and ROC values using preprocessed data in contrast to unprocessed data. The prediction results are also assessed on various quality metrics such as accuracy, f1-score, precision, and recall significantly support the need for presented methodologies for qualitative NAFLD prediction modelling. 2025 Institute of Advanced Engineering and Science. All rights reserved.