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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. -
Hybrid GNN-Driven Framework for Intelligent Malware Detection and Cryptojacking Prevention in Heterogeneous Cloud Environments
Cloud environments are increasingly targeted by cryptojackers who use the computers processing capabilities for mining cryptocurrency without authorization. This research aims to enhance the security features that protect against cyber attackers by implementing deep learning techniques that help to detect anomalous behaviors in the cloud through analysis of data from typical system transactions. The hybrid HGCN-SIEM Fusion architecture for cryptojacking prevention and malware detection incorporates four types of Graph Neural Network (GNN) approaches: GCN, GAT, GIN, and GraphSAGE. The proposed technique achieves superior malware detection accuracy compared to all baseline models. After experiments on the standard SoK cryptojacking malware dataset, GAT and GraphSAGE demonstrated an accuracy average of 97.5%, GCN and GIN achieved similar accuracy, with an average score of 95.5%. The HGCN-SIEM model outperforms with an optimum accuracy of 98.8%, ensures low latency, and provides a well-balanced mix of rapid attack detection and the best utilization of the network bandwidth. SHA-256 is used to hash all process, instance, and event identifiers to protect privacy and ensure distinct, impenetrable node representations. Graph sampling, edge pruning, and adaptive batching are used to manage computational scalability in heterogeneous cloud networks, which reduces latency, increases throughput, and optimizes resource utilization during inference. This research work points out those GNN architectures that combine different node types that are extremely useful for security monitoring and malware detection in various network settings, demonstrating reliability and practicality in cybersecurity contexts. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
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. -
From Producer to Consumer: AI-Blockchain Integration for Sustainable Supply Chain Tracking and Optimization
The blockchain is transforming the way supply chains operate. It is a decentralized peer-to-peer system that ensures safe and transparent data interchange. Because there is no central authority, blockchain technology can't be hacked; the data are dispersed throughout numerous nodes. Therefore, more openness, safety, and resistance to tampering are assured as opposed to conventional centralized database systems that depend on a single authority to manage all the data. This would ensure the immutability of the transaction log, which accurately traces the products from origin to destination. The supply chain refers to the transportation and distribution of goods when transactions are verified in real time and integrated into a secure, cryptographic ledger. This decentralized framework provides better tracking of various manufacturers, reduced chances of fraud, and greater trust among the participants. The early understanding based on tracking will definitely helps the stakeholders to improve the blockchain activities, reliability, it will minimize risks, and enhances efficiency. This will parallelly help those who are involved within supply chain management giving high accountability and seamlessness in the movement of details. 2025 IEEE. -
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. -
Dynamic Financial Portfolio Optimization Using Temporal Convolutional Networks and Real-Time Data Analysis
This paper presents an integrated framework for AI-driven portfolio optimization combining temporal convolutional networks (TCNs) with conditional value-at-risk (CVaR) minimization. Our system processes real-time market data through an automated pipeline implementing volatility-adjusted feature engineering and walk-forward validation. The architecture employs dilated causal convolutions for temporal pattern extraction combined with Ledoit-Wolf shrinkage covariance estimation for robust portfolio optimization. Experimental results demonstrate an 18.7% annualized return with 22.3% volatility, outperforming traditional mean-variance optimization by 14.2% in risk-adjusted returns. The implementation addresses key challenges in numerical stability and computational efficiency through eigenvalue clamping and gradient checkpointing. 2025 IEEE. -
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. -
Impact of ESG Index on the Stock Return: Empirical Evidence from CRIP Sector
In modern times, investment decisions are significantly influenced by a range of metrics. One widely embraced investment strategy in both developed and developing economies is investment through analysing Environmental, Social Responsibility, and Governance (ESG) factors. Investors rely on ESG scores as a valuable resource to pinpoint companies that are more likely to maintain their growth trajectory while reducing the possibility of encountering negative occurrences such as legal complications, controversies, and unfavourable public attention. This, in turn, facilitates more effective risk management and enhances returns on investment. However, the influence of ESG factors on stock returns within the Construction, Real Estate, Infrastructure, and Project (CRIP) sector is relatively limited. Consequently, the main aim of this study is to assess how ESG aspects influence the returns of stocks in companies operating in the CRIP sector. To conduct this analysis, we employed the Crisil ESG database, which provides comprehensive data on ESG metrics and stock returns. A sample containing 35 companies from the CRIP industry was meticulously chosen for investigation. To quantify the influence of ESG aspects on stock returns within the CRIP sector, a Fixed Effect Panel Regression Model was applied. The study results suggest a favourable and considerable relationship of ESG ratings on the closing stock price. Furthermore, the analysis demonstrates a large and beneficial influence of ESG ratings on stock returns. These results contain substantial implications for investors and stakeholders having a vested interest in making well-informed investment choices within the CRIP industry. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
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. -
Real-Time Fabric Defect Detection Using a Lightweight YOLOv8 Model on Edge Devices
The detection of defects in fabric is a critical process for maintaining quality standards and reducing economic losses in the textile industry. Traditional inspection methods, which rely on human operators, are often slow, inconsistent, and susceptible to error. This research introduces an innovative solution that harnesses Edge AI and deep learning to facilitate real-time, on-site defect detection. We developed a highly efficient and lightweight model based on the YOLOv8 architecture, specifically tailored for deployment on resource-constrained edge devices like NVIDIA Jetson Nano or Raspberry Pi. Through a process of comprehensive literature analysis and domain expertise, a compact, high-precision model was trained on diverse fabric defect datasets. To ensure optimal performance on edge hardware, we employed advanced optimization techniques like quantization and pruning. The primary offering of the work are threefold: the making of a streamlined YOLOv8-based model for fabric defect detection, a comparative analysis of various edge inference strategies, and a proposed system architecture for real-time embedded deployment. This study effectively demonstrates the practical application of advanced AI to solve longstanding challenges in textile quality control. Future efforts will be directed towards extensive real-world operational testing and exploring localized Model Training with Federated Learning enhancement. 2025 IEEE. -
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. -
Inverted LPDA for Broadband Radio Astronomy Observation between 150 and 800 Mhz
Radio transients are celestial objects that vary their brightness in time. The brightness can vary from a few milliseconds to a few hours and exhibit emissions across Radio waves to X-rays and even in Gamma rays. Sophisticated search techniques such as single pulse search, clustering, advanced AI, and digital signal processing are used to detect the radio signals emitted from these transient sources. A study of the signals from the transient sources helps to understand their origin and nature. This paper describes the details of a new antenna designed to detect radio transients at low frequencies between 150 MHz and 800 MHz at RRI Gauribidanur Observatory. 2025 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. -
Exploring Social Cues and Engagement in Humanoid Robots: A Robosen K1 Case Study
With the increase adoption of humanoid robots in today's world, the need to understand the ways through which these robots communicate social cues has become indispensable for effective human-robot interaction (HRI) in everyday life. The focus of this study is on the examination of the influence of nonverbal behaviour of Robosen K1 (a humanoid robots) on human perceptions and emotional responses. K1 was programmed to perform expressive full-body movements, due its lack of facial expressions, such as dancing, push-ups, and standing on its head. The research design was a mixed-method approach, which combined behavioural observations from live interactions with data from an online survey. Findings from the study revealed positive emotional reactions from participants, most of which described the robot as 'impressive', 'curious', and 'amusing'. Also, results indicated that 89.8% of participants were favourably disposed to engaging with similar robots in the future. Finally, it was found that the robot's gestures, being highly expressive, contributed to perceived personality traits such as 'playful' and 'friendly'. The study, therefore, concluded that a well-designed non-verbal cues would play critical role in enhancing emotional connection, engagement, and trust in humanoid robots, hence, their importance for successful HRI design. 2025 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.