Browse Items (2150 total)
Sort by:
-
A Comparative Study of ML and DL Approaches for Twitter Sentiment Classification
This research made use of various machine learning (ML) and deep learning (DL) methods - such as support vector machines, random forests, logistic regression, naive Bayes, and XGBoost, convolutional neural networks (CNNs), and feedforward neural networks (FNNs) - for tweet analysis to investigate public sentiment towards Ola and Uber. The objective is to determine the most effective method for distinguishing between good and negative tweets. Feature engineering techniques improve the algorithms interpretation of tweet content. To balance out the disparity between positive and negative tweets. The project aims to uncover customer wants and concerns on Twitter to help Ola and Uber, in addition to improving Algorithms Accuracy. The study intends to help these ride-hailing businesses make educated modifications to boost customer happiness by closely examining tweets. Essentially, the study assesses how well various ML and DL algorithms comprehend user feedback on Uber and Ola. The overarching goal is to not only enhance computational methods but also contribute to the improvement of these ride-hailing services, ultimately fostering a more positive online environment for Ola and Uber enthusiasts. In summary, the study investigates sentiment analysis techniques on Twitter to optimize understanding of Ola and Uber-related tweets, aiming to facilitate positive changes for the ride-hailing services and their customers, promoting a friendlier Twitter community. 2024 IEEE. -
Innovative Method for Alzheimer Disease Prediction using GP-ELM-RNN
Brain illnesses are notoriously challenging because of their fragility, surgical complexity, and high treatment costs. Contrarily, it is not obligatory to carry out the operation, as the outcomes of the procedure may fall short of expectations. Adult-onset Alzheimer's disease, which causes memory loss and losing information to varied degrees, is one of the most common brain diseases. This will vary from person to person based on their current health situation. This highlights the need of using CT brain scans to classify the extent of memory loss and determine the patient's risk for Alzheimer's disease. The four main goals of Alzheimer's disease detection are preprocessing the data, extracting features, selecting features, and training the model with GP-ELM-RNN. The Replicator Neural Network has been utilized earlier for AD detection, however this study offers an improved version of the network, modified with ELM learning and the Garson algorithm. From this study, it is deduced that the proposed method is not only efficient, but also quite precise. In this research, GP-ELM-RNN network is built to four groups of images representing different stages of Alzheimer's disease: very mildly demented, mildly demented, averagely demented, and non-demented. The class of very mildly demented patients was found to have the highest accuracy (99.1%) and specificity (0.984%). As compared to the ELM and RNN models, this technique achieves superior accuracy (around 99.23%). 2023 IEEE. -
Effect of calcium sulfoaluminate additive on linear deformation at different humidity and strength of cement mortars
The effect of calcium Sulfoaluminate additives (CSA) on the compression and bending strength of mortar, as well as linear deformation of prism samples at different environmental humidity was studied. Test results indicate that bending strength of mortars with CSA and the referent at the age of 28 days are practically equal. Compressive strength of mortars with CSA reduced by 20... 23% for all dosages of CSA. Relative linear deformations depend on the humidity of the environment. At a humidity of 100%, the relative linear deformations are positive and the expansion increases with increasing dosage of the expanding additive. When hardening in dry air at a humidity of 55%, the greatest shrinkage deformations were observed for mortars with CSA. We can conclude that the expanding effect of CSA is fully manifested at high humidity, i.e. under construction conditions, this means very high-quality moisture care for concrete structures. The Authors 2020. -
Analytical Results of Heart Attack Prediction Using Data Mining Techniques
In the modern era of living a fast lifestyle, people are not more conscious of their food eating and lifestyle. Due to these reasons, the chances of having a cardiac-related disease have risen drastically. This paper has studied the various supervised and unsupervised machine learning algorithms in comparative methods with best accuracy. Models like classification algorithms, regression algorithms, and clustering algorithms have been used for this paper. This research paper majorly focuses on patients with certain medical attributes that indicate a higher risk of heart disease. The model almost gives a good accuracy for all the regression and classification models when compared to the clustering models. Among all the algorithms, random forest and decision tree gives better accuracy 2023 IEEE. -
Sentiment Analysis on Live Webscraped YouTube Comments Using VADER Sentiment Analyzer
After the covid disease came in the beginning of 2020s, the amount of people using social medias has increased dramatically. So as an effect of that, the viewers and engagement in one of the worlds largest platform by google called YouTube also increased. So many new content creators also born during these times. So this project is getting the sentiment from the audience or user to the content creators by which they can improve their content quality. This research holds promise in harnessing the power of sentiment analysis to enhance the overall YouTube experience and inform content creators and platform administrators in their decision-making processes. Understanding these trends is vital for content creators, as it can offer invaluable insights into viewer engagement and preferences. By gaining a deeper understanding of how viewers react to content, creators can refine their strategies, tailor their content to their audience, and enhance the overall quality of videos. By incorporating sentiment information into recommendations, the platform can suggest videos that resonate more effectively with users, thereby increasing engagement and satisfaction. The identification of negative sentiment and harmful comments enables YouTubes content moderation systems to proactively address issues such as hate speech, harassment, and toxicity. This, in turn, contributes to a safer and more welcoming space for users to share their thoughts and opinions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Power Efficient e-Bike with Terrain Adaptive Intelligence
Electric bicycles or e-bikes are gaining momentum in the market as they are offering a smooth, noiseless and pollution free option for individual transportation in cities as well as in countryside. E-bikes are usually with a battery powered electric motor drive with an additional option for pedaling. In this work a low cost e-bike was designed and developed with a brushless DC hub motor with controllers. For smart control, smartphone was used a console and the e-bike can be controlled using a mobile application which was connected to the e-bike through Bluetooth. The controller will pick the gradient of the terrain and will control the power of the motor, which results in energy saving. Predicted range of the e-bike, speed, acceleration and total distance covered were displayed in the console along with the geographical position on the map and throttle control options. The bike with the proposed control tested and the results were giving a reduction in current drawn from the battery. 2019 IEEE. -
The effect of non-thermal argon plasma treatment on material properties and photo-catalytic behavior of TiO2 nanoparticles
In this paper, a brief study on the effect of non-thermal plasma generated with argon carrier on material properties and photo-catalytic reduction behavior of TiO2 is presented. Commercially available TiO2 nanoparticles (20 nm size) were subjected to Ar cold plasma at different time durations. Then the plasma treated materials were explored for chemical reduction of carbon dioxide (CO2) into methane (CH4) using sunlight as photo-irradiation source. The results show that the non-thermal plasma affects the material properties of TiO2 such as UV-visible absorption, XRD patterns and Raman scattering significantly and also the enhancement of CH4 yields in CO2photo-chemical reduction. 2020 American Institute of Physics Inc.. All rights reserved. -
An Approach for Detecting Frauds in E-Commerce Transactions using Machine Learning Techniques
This paper is primarily focused on E-commerce fraud detection using machine learning techniques. There are many different ways to detect E-commerce fraud using machine learning approach. In this work, comparison study is conducted between various available machine learning algorithms to detect the online frauds. During the comparative study, focus is underlined on comparison of all the algorithms to identify the fraud transactions. When compared to other algorithms, such as support vector machine, Decision Tree, K-nearest neighbour and Random Forest, it has been observed that Logistic regression gives better result among all machine learning algorithms. 2021 IEEE. -
EVALUATING THE ELEMENTS IN THE RECREATIONAL SPACE OF AN INSTITUTION
The concept of 'Recreation' justifies the human need for satisfaction, leisure, and a state of pleasure. The elements involved in a recreational space impact the activities of the user in that space. Recreational spaces act as the in-between sojourns for formal pedagogy or andragogy. Spaces of recreation are essential, especially in educational institutions, where students spend most of their time. Public, semi-public, and private spaces are all included in the institutional design, with a large percentage used by students. Open public spaces, including recreational places, are measured in terms of their physical characteristics and connections to nature. The components of a recreational area influence the activities that users engage in there. This paper seeks to list and assess the many components that are present in a recreational space. This study will evaluate those elements and their types. Informal outdoor areas or other breakout areas promote interaction and provide the students with refreshments and leisure. The focus of this paper is to draw out the quality of leisure space synonymous with a productive environment for the student, where they feel rejuvenated. Five recreational spaces of CHRIST University were studied, and the elements that combine to form this place were also observed. A survey among the students who are frequent users of these spaces was conducted, and their responses were evaluated. The elements that majorly help students go to a place were assessed, and the element's significant role was concluded. The result of this study to design professionals is to understand the need to incorporate recreational spaces while designing an educational institution and design a student-oriented space. ZEMCH Network. -
Structural and morphological characterization of hydrothermally synthesized N-Carbon Dot @ Fe3O4 composites for heavy metal ion detection
Heavy Metal-ion contamination is one of the most serious issues facing day-to-day life. To address this issue, sensing and removal of heavy metal ions in contaminated water become indispensable. Carbon Dots are hydrophilic in nature with magnificent electron acceptor and electron donator and hence it has been used as fluorescent probes for sensing applications. The present study deals with the synthesis of N-Carbon Dot (N-CD) @ Fe3O4 composite which was successfully fabricated via the hydrothermal method. The surface structure and morphology of the synthesized composite were characterized using X-Ray Diffraction (XRD) and Scanning Electron Microscopy (SEM). The elemental analysis of a sample was characterized using Energy Dispersive Spectroscopy (EDS). Further, the phase occurrence and the molecular vibration were analysed using XRD and Fourier Transform Infra-Red Spectroscopy (FTIR). Finally, the optical studies were measured using Ultravioletvisible Spectroscopy (UV Vis) and Photoluminescence Spectroscopy (PL). The prepared composite exhibited noticeable fluorescence properties and has promising potential for the detection and removal of toxic heavy metal ions in water. 2022 -
Streamlined Deployment and Monitoring of Cloud-Native Applications on AWS with Kubernetes Prometheus Grafana
As organizations increasingly move their applications to the cloud, it becomes essential to have an efficient and cost-effective method for deploying and managing those applications. Manual deployment can be time-consuming, error-prone, and expensive. Additionally, managing logs and monitoring resources for each deployment can lead to even greater costs. To address these challenges, we propose implementing an automation strategy for deployment in the cloud. With automation, the deployment process can be streamlined and standardized across different cloud providers, reducing the potential for errors and saving time and resources. Furthermore, a central log system can be implemented to manage logs from different deployments in one location. This provides a unified view of all logs and allows for easier troubleshooting and analysis. Automation can also be used to set up monitoring resources, such as alerts and dashboards, across different deployments. Overall, implementing an automation strategy for deployment in the cloud can help organizations save time and resources while improving their ability to manage and monitor their applications. A centralized log management system can further enhance these benefits by providing a unified view of logs from all deployments 2023 IEEE. -
Media and Urban Governance: The Quest for Sustainable Cities and Communities
Connectivity becomes the hallmark of network society facilitated by digital technologies. Cities are fundamentally well-connected, fast-growing, communicative, and global in outlook. Cities are also known for media concentration, as the structures and people there extensively create and exchange messages - social, political, economic, and cultural. The urban communication landscape is very complex, and therefore, a robust media and communication infrastructure is required to form, reform, and transform urban communities from a sustainable development perspective. Media not only perform the responsibilities of information dissemination and community building but also facilitate urban governance and public discourses on policies. The policy-making process that consists of policy inputs, policy processes, and policy outputs - is heavily influenced by the public discourses triggered by the media. Media can establish a policy issue at the center of the public sphere, set the policy agenda, and create public opinion. It inevitably leads to the mediatization of public policy. Media can effectively place SDGs at the center of the policy discourse and serve as a tool for urban governance by enhancing citizens' participation and helping to solve complex urban problems. This research paper explores various aspects of the governance-media interface in an urban landscape to create sustainable cities and communities. The Electrochemical Society -
A Particle Swarm Optimization-Backpropagation (PSO-BP) Model for the Prediction of Earthquake in Japan
Japan is a country that suffers a lot of earthquakes and disasters because it lies across four major tectonic plates. Subduction zones at the Japanese island curves are geologically complex and create various earthquakes from various sources. Earthquake prediction helps in evacuating areas, which are suspected and could save the lives of people. Artificial neural network is a computing model inspired by biological neurons, which learn from examples and can be able to do predictions. In this paper, we present an artificial neural network with PSO-BP model for the prediction of an earthquake in Japan. In PSO-BP model, particle swarm optimization method is used to optimize the input parameters of backpropagation neural network. Information regarding all major, minor and aftershock earthquake is taken into account for the input of backpropagation neural network. These parameters are taken from Japan seismic catalogue provided by USGS (United States Geological Survey) such as latitude, longitude, magnitude, depth, etc., of earthquake. 2019, Springer Nature Singapore Pte Ltd. -
Real time conversion of sign language to speech and prediction of gestures using artificial neural network
Sign language is generally used by the people who are unable to speak, for communication. Most people will not be able to understand the Universal Sign Language (unless they have learnt it) and due to this lack of knowledge about the language, it is very difficult for them to communicate with mute people. A device that helps to bridge a gap between mute persons and other people forms the crux of this paper. This device makes use of an Arduino Uno board, a few flex sensors and an Android application to enable effective communication amongst the users. Using the flex sensors, gestures made by the wearer is detected and then according to various pre-defined conditions for the numerous values generated by the flex sensors, corresponding messages are sent using a Global System for Mobile(GSM) module to the wearer?s android device, which houses the application that has been designed to convert text messages into speech. The GSM module is also used to send the sensor inputs to a cloud server and these values are taken as input parameters into the neural network for a time series based prediction of gestures. The system is designed to be a continually learning device and improve reliability by monitoring every individual?s behaviour at all times. 2018 The Authors. Published by Elsevier B.V. -
Systematic Review on Humanizing Machine Intelligence and Artificial Intelligence
In this era, Machine Learning is transforming human lives in a very different way. The need to give machines the power to make decisions or giving the moral compass is a big dilemma when humanity is more divided than it has ever been. There are two main ways in which law and AI interact. AI may be subject to legal restrictions and be employed in courtroom procedures. The world around us is being significantly and swiftly changed by AI in all of its manifestations. Public law includes important facets such as nondiscrimination law and labor law. In a manner similar to this when artificial intelligence (AI) is applied to tangible technology like robots. In certain cases, artificial intelligence (AI) might be hardly noticeable to customers but evident to those who built and are using it. The behavior research offers suggestions for how to build enduring and beneficial interactions between intelligent robots and people. The human improvement is main obstacles in the development and implementation of artificial intelligence. Best practices in this area are not governed by any one strategy that is generally acknowledged. Machine learning is about to revolutionize society as it is know it. It is crucial to give intelligent computers a moral compass now more than ever before because of how divided mankind is. Although machine learning has limitless potential, inappropriate usage might have detrimental long-term implications. It will think about how, for instance, earlier cultures built trust and improved social interactions via creative answers to many of the ethical issues that machine learning is posing now. 2023 IEEE. -
AI Based Seamless Vehicle License Plate Recognition Using Raspberry Pi Technology
This research presents the implementation of an innovative Vehicle Management System designed specifically for the Christ University Project 'CampusWheels.' The system incorporates cutting-edge technologies, including YOLOv8 and Tesseract OCR, for robust license plate recognition. Addressing the unique challenges faced by Christ University in managing and securing vehicular movements within the campus, this project becomes crucial as the number of vehicles on campuses continues to grow. It not only provides an effective solution to these challenges but also introduces innovative methodologies, marking a significant departure from conventional campus management practices. The paramount importance of this project lies in its ability to enhance campus security through real-time vehicle monitoring and identification. The utilization of YOLOv8 for vehicle detection and Tesseract OCR for license plate recognition ensures a high level of accuracy in identifying and tracking vehicles entering and leaving the campus. This precision significantly contributes to the prevention of unauthorized vehicle access, a common security concern on educational campuses. Moreover, the system's ability to streamline traffic flow and improve efficiency in parking and access control addresses practical issues faced by campus administrators and security personnel. 2024 IEEE. -
A Neural Network Based Customer Churn Prediction Algorithm for Telecom Sector
For telecommunication service providers, a key method for decreasing costs and making revenue is to focus on retaining existing subscribers rather than obtaining new customers. To support this strategy, it is significant to understand customer concerns as early as possible to avoid churn. When customers switch to another competitive service provider, it results in the instant loss of business. This work focuses on building a classification model for predicting customer churn. Four different deep learning models are designed by applying different activation functions on different layers for classifying the customers into two different categories. A comparison of the performance of the different models is done by using various performance measures such as accuracy, precision, recall, and area under the curve (AUC) to determine the best activation function for the model among tanh, ReLU, ELU, and SELU. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Securing International Law Against Cyber Attacks through Blockchain Integration
Cyber-attacks have become a growing concern for governments, organizations, and individuals worldwide. In this paper, we explore the use of blockchain technology to secure international law against cyber-attacks. We discuss the advantages of blockchain technology in providing secure and transparent data storage and transmission, and how it can enhance the security of international law. We also review the current state of international law regarding cyber-attacks and the need for a robust and effective legal framework to address cyber threats. The study proposes a blockchain-based approach to secure international law against cyber-attacks. We examine the potential of blockchain technology in providing a decentralized and tamper-proof database that can record and track the implementation of international laws related to cyber-attacks. We also discuss how smart contracts can be utilized to automate compliance with international laws and regulations related to cybersecurity. The study also discusses the challenges and limitations of using blockchain technology to secure international law against cyber-attacks. These include the need for interoperability between different blockchain networks, the high energy consumption of blockchain technology, and the need for international cooperation in implementing and enforcing international laws related to cybersecurity. Overall, this study provides a comprehensive overview of the potential of blockchain technology in securing international law against cyber-attacks. It highlights the need for a robust legal framework to address cyber threats and emphasizes the importance of international cooperation in implementing and enforcing international laws related to cybersecurity. 2023 IEEE. -
An Innovative Method for Election Prediction using Hybrid A-BiCNN-RNN Approach
Sentiment, volumetric, and social network analyses, as well as other methods, are examined for their ability to predict key outcomes using data collected from social media. Different points of view are essential for making significant discoveries. Social media have been used by individuals all over the world to communicate and share ideas for decades. Sentiment analysis, often known as opinion mining, is a technique used to glean insights about how the public feels and thinks. By gauging how people feel about a candidate on social media, they can utilize sentiment analysis to predict who will win an upcoming election. There are three main steps in the proposed approach, and they are preprocessing, feature extraction, and model training. Negation handling often requires preprocessing. Natural Language Processing makes use of feature extraction. Following the feature selection process, the models are trained using BiCNN-RNN. The proposed method is superiorto the widely usedBiCNN and RNN methods. 2023 IEEE. -
Artificial Intelligence (AI) in CRM (Customer Relationship Management): A Sentiment Analysis Approach
The use of customer relationship management (CRM) in marketing is examined in this essay. It looks at how CRM makes it possible to use reviews, integrate AI, conduct marketing in real time, and conduct more regular marketing operations. CRM tactics are illustrated through case studies of businesses like Uber, T-Mobile, Amazon, Apple, and Apple. CRM offers centralized data, better marketing and sales, and better customer support. There is also a discussion of the ethical, private, security, adoption, and scalability challenges of AI in CRM. In general, CRM makes data-driven decisions and customer insights easier to achieve to increase growth, loyalty, and engagement. 2024 IEEE.