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Performance Analysis of Several CNN Based Models for Brain MRI in Tumor Classification
Classification is one of the primary tasks in data mining and machine learning which is used for categorizing data into classes. In this paper, brain MRI images are used for classification of tumors into three categories namely, Meningioma, Glioma, and Pituitary Tumor. These methodologies used are spatial based, depth based, feature map based and depth based CNN showcasing the power of deep learning in automating the tumor detection process. To evaluate the performance of several deep learning models, data is divided into training and testing data where a generalization method is used for comparison. The experimental results demonstrate promising accuracy, showing that a few techniques are valuable tools for radiologists and physicians, along with further analysis. The best accuracy obtained is 96% using MobileNet and ResNet50 in comparison to other CNN methodologies used in this paper. 2024 IEEE. -
Sustainability In The Built Environment: Are We Doing Enough?
Sustainability is one of the key requirements for any business; however, there exist gaps in recognizing and aligning these sustainable practices with everyday operational activities. With this in mind, this study aims to explore the current awareness level in business leaders and stakeholders about corporate responsibility towards sustainability and reflects on the obstacles encountered by them in diverse built environments, laying the groundwork for addressing these hurdles and contributing towards the overall sustainable development. The study uses a thematic approach to analyzing data with the help of NVIVO12 software. The major findings include Energy Auditing process - not carried out frequently; both sustainability and profitability go hand in hand; absence of technology such as AI and Sensor technology has contributed to the built environment's energy performance gap; there is a knowledge gap that exists among business leaders in understanding the concept of sustainable development. The Electrochemical Society -
Traffic Management in Forest and Ecosystem Conservation. A Study on NH 766 Through Bandipore National Park and Proposing a Traffic Management Plan with Alternate Route Consideration
Transportation network is inevitable in the developing world. In India where we have a rich forest cover, many of the roads are passing through eco-sensitive areas such as national parks and wildlife sanctuaries. There are issues being reported due to these roads passing through the eco-sensitive areas such as animal deaths due to road accidents, loss of habitats, fragmentation of ecosystems, and loss of forest cover. The CalicutKollegal national highway, NH766 is passing through Bandipore national park on the stretch which connects Sultan Bathery and Gundelpette. Recently, a conflict had risen between environmental activists and the public for imposing a complete traffic ban along the NH766 passing through the Bandipore NP. A baseline study had conducted on the NH766, and the impact of the same on the ecosystem existing is analyzed through the data collected. A network analysis is performed on the alternate route available for bypassing the traffic. Traffic management plan and policies are derived out of the analysis on the baseline data collected and the inferences drawn from the network analysis performed on the alternate routes. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
FIN2SUM: Advancing AI-Driven Financial Text Summarization with LLMs
In the modern financial sector, the rapid digitalization of financial reports necessitates efficient and reliable text summarization tools. This research introduces FIN2SUM, a novel framework designed for summarizing the managerial analysis and discussion sections of 10-K reports from top NASDAQ-listed companies. The study aims to evaluate Large Language Models (LLMs) in financial text summarization, highlighting LLAMA-2's adeptness in processing complex financial information, thus making FIN2SUM a vital tool for analysts and decision-makers. The methodology includes a thorough evaluation of three state-of-the-art LLMs - LLAMA-2, FLAN, and Claude 2 - using BERT and ROUGE scores. The research concludes that FIN2SUM, enhanced by LLAMA-2, significantly advances AI-driven financial text summarization. 2024 IEEE. -
Interactions between emotional and spiritual intelligence and their effects on employee performance
The association among worker behavior, spiritual intelligence, emotional intelligence, and system effectiveness is explained by this study. Understanding how others communicate and being aware of how one's own emotions affect others around you are all characteristics of emotional intelligence. Spiritual intelligence, which is a higher level of intelligence, reveals one's actual attributes and abilities. As company's most asset, the effectiveness of employee behavior has a significant impact on the company's ability to survive and thrive. In contrast to other facets of human conduct, employee conduct is distinguished by more formal behavior. This study aims to determine whether those with emotional and spiritual intelligence perform well at work. This research also aims to comprehend the behavior of emotionally intelligent and spiritually inclined people at work. Attempts are made in this study to ascertain whether higher levels of spiritual and emotional intelligence might boost the efficacy of these abilities. In this study, productivity at work is the dependent variable, whereas emotional intelligence and spiritual intelligence are independent variables. The parameters that can assess the variables were established using a literature review and a few common surveys. An organized survey that considers the variables is developed to gather information from the working class. To determine the link between the variables chosen for this study, the gathered data was analyzed using statistical approaches such as partial correlation and correlation. 2024 Author(s). -
Behavioral Time Management Analysis: Clustering Productivity Patterns using K-Means
This paper focuses on investigating the efficiency profile through the three-time management behaviors using the K-Means clustering method. In the case of the study, the data gathered from digital time management tools for 100 participants for one month was preprocessed to distil features surrounding productivity, including daily working hours, focus time, break duration and frequency, and task completion ratios. The four groups that were agreed upon through K-Means clustering differed in terms of time management behaviours and productivity. Insert table 6 IT cluster 1 worked long hours with high productivity owing to the fact that they are IT professionals but had a tendency of multitasking. Employment Cluster 2 (marketing and sales professionals) achieved both personal and work-related self-care but identified the need for more concentrated time per task. As for the differences in the breaks, it can be noted that cluster 3 (management and administration personnel) had significantly higher task completion times and focus times, but their break intervals needed to be optimized. Hypothesis 2 stated that there will be many hours of leisure for Cluster 4 (students and interns) imply that their work hours should be adjusted to several small tasks a day, and their rates of task completion should be increased. From the study, it is possible to stress that time management should be considered as an individual activity that requires specific approaches to the given subject area and to the learner in particular. Specifically, demographic profiling identified the roles that age and occupational status may play in averting or exacerbating productivity deficiencies: insights that could be actionable in specific scenarios. The implications of this research offer practical insights into individual and organizational time management, as the usability aspects of machine learning techniques were considered and their applicability established, which further extends the scope of time management by revealing patterns and improving time management plans and practices. 2024 IEEE. -
Optimal allocation algorithm of marketing resources based on improved random forest
Random Forest algorithm is an ensemble learning algorithm that classifies data by combining multiple decision trees. It has a wide range of applications and is not easy to overfit. It has a wide range of applications in medicine, bioinformatics, management and other fields. By studying the Cobb-Douglas sales function, it is found that it can only analyze the static allocation of marketing resources, but cannot describe the dynamic changes. Enterprise marketing resource management runs through the enterprise management from beginning to end. The research on marketing resource management is helpful for enterprises to grasp and control the whole process of marketing resource management from the overall and overall level, and has important theoretical value and reality for enterprise marketing management activities. significance. In the vast majority of enterprises in our country, the size of advertising promotion expenses and the number of salesmen is often determined based on the experience and subjective assumptions of decision makers, so it is difficult to say that they are optimized. This paper starts with determining the optimal advertising budget and the number of salespeople, and conducts applied research on the optimal allocation of marketing resources. 2023 IEEE. -
Machine Learning Integration for Enhanced Solar Power Generation Forecasting
This paper reviews the advancements in machine learning techniques for enhanced solar power generation forecasting. Solar energy, a potent alternative to traditional energy sources, is inherently intermittent due to its weather-dependent nature. Accurate forecasting of photovoltaic power generation (PVPG) is paramount for the stability and reliability of power systems. The review delves into a deep learning framework that leverages the long short-term memory (LSTM) network for precise PVPG forecasting. A novel approach, the physics-constrained LSTM (PCLSTM), is introduced, addressing the limitations of conventional machine learning algorithms that rely heavily on vast data. The PC-LSTM model showcases superior forecasting capabilities, especially with sparse data, outperforming standard LSTM and other traditional methods. Furthermore, the paper examines a comprehensive study from Morocco, comparing six machine learning algorithms for solar energy production forecasting. The study underscores the Artificial Neural Network (ANN) as the most effective predictive model, offering optimal parameters for real-world applications. Such advancements not only bolster the accuracy of solar energy forecasting but also pave the way for sustainable energy solutions, emphasizing the integration of these findings in practical applications like predictive maintenance of PV power plants. The Authors, published by EDP Sciences, 2024. -
Pre and Post Operative Brain Tumor Segmentation and Classification for Prolonged Survival
The aim of this research was to provide a detailed overview of the techniques in detecting and segmenting meningioma brain tumor in pre- and post-operative MRI images and classify for presence of meningioma thereby giving an early diagnosis to decrease the death rate. This study examines trending techniques for brain tumour segmentation and classification in Magnetic Resonance (MR) images of pre and post-surgery. For the segmentation and anomalies in the brain categorization, several approaches such as regular machine learning techniques (K-mean bunching, Fuzzy C mean grouping etc.), Deep Learning-based approaches (CNN, ResNET, Dense Net, VGG etc.), classical algorithms (Snake contour, watershed method etc.), and hybridization approaches were applied, according to the analysis. Information base, for example, BRATS, Fig-Share, EPISURG or TCIA can be taken to gather clinical pictures which principally contains of 2 classifications, pre and post pictures of Brain tumor. The multiple processes of brain tumour segmentation methodologies, such as preprocessing, feature extraction, segmentation, and classification, are also explained in this work. The task of segmenting residual and recurrent tumors differs greatly from that of segmenting tumors on baseline scans before surgery. This study shows that each approach has its own set of pros and limitations, as well as notable findings in terms of precision, sensitivity, and specificity, according to the comparison research. The use of segmentation approaches to determine success and reliability has been discovered. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Predictive Modelling of Heart Disease: Exploring Machine Learning Classification Algorithms
In addressing the critical challenge of early and accurate heart failure diagnosis, this study explores the application of five machine learning models, including XGBoost, Decision Tree, Random Forest, Logistic Regression, and Gaussian Naive Bayes. Employing cross-validation and grid search techniques to enhance generalization, the comparative analysis reveals XGBoost as the standout performer, achieving a remarkable accuracy of 85%. The findings emphasize the significant potential of XGBoost in advancing heart failure diagnosis, paving the way for earlier intervention, and potentially improving patient prognosis. The study suggests that integrating XGBoost into diagnostic processes could represent a valuable and impactful advancement in the realm of heart failure prediction, offering promising avenues for improved healthcare outcomes. 2024 IEEE. -
Social Media Sentiment Analysis of Stock Market on Tweets
Sentiment categorization is utilized in today's world to analyze social media data about the stock market and estimate its future stock movement. We investigated the possible influence of 'public sentiment' on 'market trends' using sentiment analysis and machine learning concepts. Due to the enormous number of components involved, such as economic situations, political events, and other environmental factors that may affect the stock price, stock price prediction is an exceedingly complicated and challenging process. Because of these considerations, evaluating a single factor's influence on future pricing and trends is challenging. As more individuals spend time online, the popularity and impact of numerous social media platforms has skyrocketed in recent years. Twitter is one such social media tool that has exploded in popularity. Twitter is a terrific place to stay up to speed on current societal trends and perspectives. The 'Twittersphere' is a melting pot that supports diverse viewpoints, emotions, and trends, and it has the potential to be a crucial influencer in influencing and shaping perceptions. 2022 IEEE. -
Trident Shaped Compact Planar Antenna for Microwave Applications
A compact planar antenna for X/Ku-band microwave communication is suggested in this paper. The presented geometry is capable of radiating the large frequency band from 6.8 to 20GHz, which covers the X-Band/Ku-Band Communication with high efficiency. The impedance bandwidth of the radiator is 98.5%, with an electrical size of. 34?x.34?x0.034A in lambda. The suggested design includes a modified patch in the trident shape fed by a microstrip line. Rectangular elements have been designed for better resonances at lower modes. The antenna is simulated with an FR4 substrate using CST Simulator. The exact dimensions of the antenna are 15x15x1.5 cubic millimeter. Five stages evolution process is also investigated by simulations, and corresponding S-parameter results are presented. The proposed structure also demonstrates stable radiation patterns across the operating bandwidth. The proposed radiator has a high gain of 3.1 dBi, and an efficiency of 87%. Therefore, it is useful for X-band, and Ku-band, including Radar, Space, Terrestrial, and Satellite microwave communication. 2022 IEEE. -
Ethical and Societal Implications of Artificial Intelligence in Space Mining
The advent of Artificial Intelligence (AI) in space mining marks a pivotal shift in the exploration and utilization of extraterrestrial resources. This paper presents a thematic analysis of the ethical, societal, technological, economic, and environmental implications of integrating AI in space mining operations. Through topic modeling of relevant literature, five key themes were identified: AI integration and ethical considerations, economic efficiency and equity, technological innovations and advancements, international collaboration and governance, and environmental sustainability and planetary protection. These themes highlight the potential of AI to revolutionize space mining, enhancing efficiency and enabling the extraction of valuable resources beyond Earth. However, they also underscore the need for robust ethical frameworks, equitable economic models, international cooperation, and sustainable practices to address the multifaceted challenges posed by this frontier. The paper concludes with recommendations for future research and policy-making, emphasizing the importance of inclusive, collaborative approaches to ensure the responsible and beneficial advancement of space mining. 2024 IEEE. -
Exploring the Frontier: Space Mining, Legal Implications, and the Role of Artificial Intelligence
This analysis delves into the multifaceted dimensions of space mining and artificial intelligence, exploring technological advancements, legal challenges, environmental concerns, and ethical implications. Through topic modeling and sentiment analysis of 160 articles, five core themes are identified: Technological and Exploration Advances, Resource Extraction and Environmental Concerns, Legal and AI Integration, Ethical and Paradigm Shifts, and challenges and Innovations in Space Mining. The discussion highlights the optimistic yet cautious outlook on space mining, emphasizing the need for continued innovation, comprehensive legal frameworks, ethical stewardship, and environmental protection as humanity ventures into this new frontier. 2024 IEEE. -
Calibration of Optimal Trigonometric Probability for Asynchronous Differential Evolution
Parallel optimization and strong exploration are the main features of asynchronous differential evolution (ADE). The population is updated instantly in ADE by replacing the target vector if a better vector is found during the selection operation. This feature of ADE makes it different from differential evolution (DE). With this feature, ADE works asynchronously. In this work, ADE and trigonometric mutation are embedded together to raise the performance of an algorithm. The work finds out the best trigonometric probability value for asynchronous differential evolution. Two values of trigonometric mutation probability (PTMO) are tested to obtain the optimum setting of PTMO. The work presented in this paper is tested over a number of benchmark functions. The benchmark functions results are compared for two values of PTMO and discussed in detail. The proposed work outperforms the competitive algorithms. A nonparametric statistical analysis is also performed to validate the results. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Linear Regression Tree and Homogenized Attention Recurrent Neural Network for Online Training Classification
Internet has become a vital part in people's life with the swift development of Information Technology (IT). Predominantly the customers share their opinions concerning numerous entities like, products, services on numerous platforms. These platforms comprises of valuable information concerning different types of domains ranging from commercial to political and social applications. Analysis of this immeasurable amount of data is both laborious and cumbersome to manipulate manually. In this work, a method called, Linear Regression Tree-based Homogenized Attention Recurrent Neural Network (LRT-HRNN) for online training is proposed. In the first step, a dataset consisting of student's reactions on E-learning is provided as input. A Linear Regression Decision Tree (LRT) - based feature (i.e., student's reactions and posts) selection model is applied in the second step. The feature selection model initially selects the commonly dispensed features. In the last step, HRNN sentiment analysis is employed for aggregating characterizations from prior and succeeding posts based on student's reactions for online training. During the experimentation process, LRT-HRNN method when compared with existing methods such as Attention Emotion-enhanced Convolutional Long Short Term Memory (AEC-LSTM) and Adaptive Particle Swarm Optimization based Long Short Term Memory (APSO-LSTM, performed better in terms of accuracy(increased by 6%), false positive rate (decreased by 22%), true positive rate (increased by 7%) and computational time (reduced by 21%). 2022 IEEE. -
Improved tweets in English text classification by LSTM neural network
This paper analyzes the performance of an LSTM-type neural network in the sentiment analysis task in tweets in English about the COVID-19 pandemic. Primarily, the organization and cleaning a database of tweets about the COVID-19 pandemic is performed. From the original database, two other databases through different discretizations of the polarities of the tweets using Heaviside-type functions are created. Vectorization of tweets using the Word2Vec word embedding technique is carried out. Computational implementations of LSTM neural networks to the context of our research problem are adapted. Analyzes and discussions on the feasibility of the proposed solution taking into account different types of hyperparametric adjustments in the neural network models is carried out. Publicly available databases organized through the Mendeley Data public data repository are used. 2023 IEEE. -
Identification of Consumer Buying Patterns using KNN in E-Commerce Applications
In recent days, with the advancement of technologies, people use electronic medium to carry out their businesses. E-commerce is a process of allowing people to buy and sell products online using electronic medium. E-commerce has a wide range of customer base as well. The data generated through transaction helps the enterprises to develop the marketing strategy. The growth of this e-commerce application depends on several factors. Some of the factors are follows 1) Customer demand, 2) Analyzing buying pattern of the users, 3) Customer retention, 4) dynamic pricing etc. It is very difficult to analyze the buying pattern of customers as there is a wide range of customer base in the online platform. To overcome this problem, this research study discusses about the challenges and issues in e-commerce applications, also identifies and analyses the buying patterns of customer using various machine learning techniques. From the implementation it is identified that, KNN algorithm performed well while comparing it with various other machine learning algorithms. Performances of these algorithms have been analyzed using various matrices. For analyzing, the model is tested using e-commerce dataset (Amazon dataset downloaded from Kaggle.com). From the analysis it found that KNN algorithm computes and predicts better compared to other machine learning algorithms either Nae Bayes, or Random Forest, or Logistic Regression etc. 2023 IEEE. -
Subscriber Preference and Content Consumption Pattern toward OTT platform: An Opinion Mining
Introduction: The outburst of the pandemic has paved the way for the immense popularity of over-The-Top (OTT) platforms among viewers. Furnishing an alternate medium to watch favorite shows and making it a new normal, the OTT platform has replaced the traditional entertainment platform. However, migrating from traditional television to an OTT platform is still a challenge in developing countries. Hence, the understanding of subscriber preferences and content consumption patterns becomes essential to planning and strategizing future business models. Purpose: The purpose of the paper is to examine the subscriber preference and content consumption pattern toward the OTT platform. In addition, this paper also investigates the popularity of leading OTT platforms among Indian viewers. Methodology: Data has been collected from the subscribers of three major OTT: Amazon Prime, Netflix Video, and Disney+Disney+Hotstar. A total of 1860 reviews were scraped as textual data and analyzed using the lexicon-based method. The polarity of the sentiments pertaining to the reviews of different platforms was analyzed using sentiment analysis. Furthermore, the topic modeling on the reviews was performed using natural language programming(NLP). Findings: The findings of sentiment analysis showed that Netflix and Disney+Disney+Hotstar had a considerable number of positive sentiments among viewers when compared to Amazon Prime Video. Eventually, the paper also showed negative sentiment towards Amazon Prime Video regarding streaming content, ad pop-ups, interface issue, shows, etc. Our findings help OTT platforms to determine which factors are driving this dramatic shift in viewer behaviour so that better strategies for attracting and retaining subscribers can be developed. Despite the rise in OTT platform popularity, this is the first study to investigate the content consumption pattern of OTT viewers comprehensively. 2022 IEEE. -
Sentiment Analysis on Time-Series Data Using Weight Priority Method on Deep Learning
Sentiment Analysis (SA)is the process to gain an overview of the public opinion on certain topics and it is useful in commerce and social media. The preference on certain topics can be varied on different time periods. To analyze the sentiments on topics in different time periods, priority weight based deep learning approaches like Convolutional-Long Short-Term Memory (C-LSTM)and Stacked- Long Short-Term Memory (S-LSTM)is explored and analyzed in this research. The research method focuses on three phases. In the first phase text data (review given by the customers on various products)is collected from social networking e-commerce site and temporal ordering is done. In the second phase, different deep learning models are created and trained with different time-series data. In the final phase the weights are assigned based on temporal aspect of the data collected. For the obtained results verification and validation processes are carried out. Precision and recall measures are computed. Results obtained shows better performance in terms of classification accuracy and F1-score. 2019 IEEE.