This paper explores sentiment analysis in legal judgments using machine learning techniques. Six machine learning models, including Naïve Bayes, K Nearest Neighbors, Logistic Regression, Support Vector Machine, R...
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ISBN:
(数字)9798350394474
ISBN:
(纸本)9798350394481
This paper explores sentiment analysis in legal judgments using machine learning techniques. Six machine learning models, including Naïve Bayes, K Nearest Neighbors, Logistic Regression, Support Vector Machine, Random Forest, and Decision Tree, are used along with three data embeddings namely T5, RoBerta, and LegalBert. The study aims to evaluate the effectiveness of these models in sentiment analysis tasks tailored to legal documents. Each model is trained and evaluated based on the unique characteristics of legal texts. Hyperparameter tuning has also been performed for all the models. The focus is on achieving high accuracy, F1 score and understanding the interpretability of the models' predictions. The highest accuracy achieved was by Random Forest of 67.5%. The study aims to provide insights into the applicability of various machine learning algorithms and data embeddings for sentiment analysis in the legal domain.
Protein secondary structure prediction is a critical task in bioinformatics, essential for understanding protein function and aiding drug discovery. Traditional methods have achieved significant milestones, but there ...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
Protein secondary structure prediction is a critical task in bioinformatics, essential for understanding protein function and aiding drug discovery. Traditional methods have achieved significant milestones, but there remains a need for further accuracy and efficiency improvements. In this paper, we propose a novel approach that combines Bidirectional Long Short-Term Memory BiLSTM networks and Transformer models to enhance prediction accuracy. Our dual-model strategy leverages the sequential processing strengths of BiLSTM and the robust attention mechanisms of Transformers, enabling a more comprehensive analysis of protein sequences. Our extensive experiments demonstrate that this hybrid approach achieves superior performance, with Q3 accuracy reaching 92.03% and Q8 accuracy at 85.26%, significantly surpassing existing methods. This enhancement is ascribed to the capacity of BiLSTM to capture long-range dependencies and the Transformer’s capability to focus on relevant parts of the sequence. The integration of these models provides a powerful tool for predicting protein secondary structures with high precision. The findings suggest that our approach not only advances the current state of PSSP but also offers valuable insights that can accelerate bioinformatics research and drug discovery processes. This work underscores the potential of combining deep learning models to tackle complex biological challenges more effectively.
This paper addresses the significant global impact of lung cancer, the primary cause of cancer-related deaths worldwide and the second leading cause of mortality following cardiovascular disease. The challenge lies in...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
This paper addresses the significant global impact of lung cancer, the primary cause of cancer-related deaths worldwide and the second leading cause of mortality following cardiovascular disease. The challenge lies in detecting lung cancer, often diagnosed at an advanced stage, complicating treatment efforts. The proposed model integrates CT (Computed Tomography) and PET (Positron-Emission Tomography) scans to enhance lung cancer prediction. CT scans offer detailed anatomical insights, while PET scans excel in identifying abnormal metabolic activity and precise tumour localization. This combined approach provides a comprehensive assessment, empowering healthcare professionals with improved diagnostic accuracy and better-informed treatment planning for lung cancer patients.
The study aims to examine the effectiveness of three machine learning algorithms: Random Forest, Support Vector Machine (SVM), and Logistic Regression, navigating through the complex terrain of medical-related dataset...
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ISBN:
(数字)9798350379945
ISBN:
(纸本)9798350379952
The study aims to examine the effectiveness of three machine learning algorithms: Random Forest, Support Vector Machine (SVM), and Logistic Regression, navigating through the complex terrain of medical-related datasets. Through an in-depth analysis of three major datasets including demographics, health indicators, and assessments of Parkinson's disease, this study reveals the subtle advantages and disadvantages of each algorithm. Whereas Random Forest has unmatched flexibility in figuring out complex correlations, Logistic Regression stands out as an example of simplicity and openness. SVM struggles with computing loads and parameter complexities at the same time. This thorough investigation avoids claiming a model is supe-rior, allowing practitioners to identify the complex interactions amongst machine learning giants when it comes to healthcare datasets.
In a time where digital content consumption reigns, it is essential to efficiently deliver multimedia resources. This research seeks to transform how content is delivered by implementing and overseeing a secure, monit...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
In a time where digital content consumption reigns, it is essential to efficiently deliver multimedia resources. This research seeks to transform how content is delivered by implementing and overseeing a secure, monitored Content Delivery Network (CDN) on a scalable Kubernetes cluster, managed through a secure CI/CD pipeline built on Jenkins, using the Amazon Web Services (AWS) and essential DevOps tools like Docker, SonarQube, Trivy, Prometheus, Grafana, Argo CD and Helm. The results obtained in this study are derived from an emulation of a CDN. The SonarQube analysis confirms whether the system meets all code quality gates, Trivy identifies and resolves critical container vulnerabilities and OWASP vulnerability checks identify and mitigate security risks in software dependencies. The system is monitored by analyzing the performance metrics visualized in Grafana dashboards, which offer detailed insights into metrics including and not limited to: active jobs, job queue duration, queued rate, memory usage, CPU usage, executor health metrics, system load, RAM usage, root filesystem usage, disk space usage, disk I/O activities, time synchronized drift and time synchronized status. Additionally, SMTP e-mail notifications are configured to improve responsiveness to anomalies identified via monitoring. This study emphasizes making content delivery mechanisms more robust, providing advanced tools for developers, and enabling easy access to digital resources for the community.
Cyclones and wildfires are deadly; they do significant damage to human life and property. This requires an effective early warning system that would ensure a timely intervention since the normal methods are not effici...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
Cyclones and wildfires are deadly; they do significant damage to human life and property. This requires an effective early warning system that would ensure a timely intervention since the normal methods are not efficient. Frequently fall short of the necessary pace and precision in successful detection as well as signaling: this results in response lag times and escalation of loss. In addition, most current systems often work on their own without any cooperation. It lacks integration and immediate communication means to the concerned authorities. However, in this study, we put forward a novel system that combines detection with early warning for cyclones and wild fires through satellite images using advanced computer vision techniques. We apply the YOLOv9 object detection algorithm on the satellite pictures which allows us to detect cyclones and wildfires with a high level of precision as well as speed. In addition, we design an alert system Using the Twilio WhatsApp API to report detected incidents to the relevant authorities without delay. Through this we aim to improve disaster management potential minimizing loss to life and property and ensure safety to the communities vulnerable to natural disasters.
Understanding customer feedback is crucial for enhancing service quality in the competitive airline industry. This study analyzes Delta Airline's customer reviews using advanced natural language processing techniq...
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ISBN:
(数字)9798350376685
ISBN:
(纸本)9798350376692
Understanding customer feedback is crucial for enhancing service quality in the competitive airline industry. This study analyzes Delta Airline's customer reviews using advanced natural language processing techniques, focusing on topic modeling and traveler persona classification. Initially, the study was performed an elbow plot analysis to determine the optimal number of clusters, identifying three distinct clusters. Clustering algorithm K-means and Latent Dirichlet Allocation were then applied to uncover the main topics in the reviews. Next, this research classifies reviewers into different traveler personas using NLP models such as BERT, Word2Vec, One-Hot Encoding, DistilBERT and RoBERTa, and models which uses deep learning architectures such as LSTM, BiLSTM and GRU identifying categories like Family leisure, solo leisure, Couple leisure and business travelers. The insights from this analysis enable Delta Airline to tailor their services to specific customer segments. Classification results indicate that BiLSTM and GRU models performed the best, with BiLSTM achieving an accuracy of 92% with Roberta.
The increasing amount of hate speech and language which are inappropriate on platforms of social media has emerged as a serious issue, prompting measures to counter it. This study examines hate speech and languages th...
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ISBN:
(数字)9798350376685
ISBN:
(纸本)9798350376692
The increasing amount of hate speech and language which are inappropriate on platforms of social media has emerged as a serious issue, prompting measures to counter it. This study examines hate speech and languages that are objectionable in a Tamil Code-Mix dataset, where Tamil content is provided in English. It assesses the performance of various deep learning classifiers that use different multilingual transformer embedding models, which includes many models such as DistilBERT, IndicBERT, XLM-RoBERTa, LASER, MuRIL, and BERT, in this domain. Among the models tested, an ensemble stacked model integrating Long Short-Term Memory and Gated Recurrent Units with XLM-RoBERTa embeddings had the greatest accuracy (0.76) and weighted F1-score (0.72). This exceptional performance underscores the potential of advanced ensemble models for distinguishing hate speech in code-mixed languages. The study's findings are notable for several reasons. They enhance understanding of hate speech in Tamil, particularly in its code-mixed variant, and offer insights into the application of advanced deep learning methods and multilingual embeddings for detecting harmful content. By improving accuracy and reliability in hate speech detection, this research contributes to fostering safer and more inclusive online environments for Tamil-speaking communities. In summary, the research advances the domain of hate speech identification and emphasizes the importance of technological solutions in creating more safer and very inclusive online spaces.
Technology is expanding and applicants are making their resumes look better by developing their skill set for the current environment. Each resume now looks very promising and manually screening resumes and finding ou...
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ISBN:
(数字)9798350394474
ISBN:
(纸本)9798350394481
Technology is expanding and applicants are making their resumes look better by developing their skill set for the current environment. Each resume now looks very promising and manually screening resumes and finding out the potential candidate in time consuming and is not an efficient method. In this paper, we propose a method using Natural Language Processing (NLP) and Spark to filter relevant resumes. The work proposes a cosine similarity-based approach to measure text-similarity and hence find worthy candidates based on key information provided by the employer. The implementation of the proposed approach on a Spark platform helps in handling large-scale datasets, enabling efficient semantic similarity analysis in the context of job descriptions. The experiments conclude that the proposed method can efficiently speed up the process in short - listing worthy candidates and can effectively replace the process of hiring managers going through each resume. Our model demonstrates superior performance compared to a non-Spark-based resume filtering system, achieving an average reduction in processing time by 80%, translating to an average runtime decrease from 5 seconds to 1. This efficiency gain is scalable, presenting enhanced performance in candidate shortlisting, especially with larger datasets.
This paper seeks to establish the energy conservation by using piezo-electric effect through some Modern Technology's and the project aims to investigate the application of modern technology in various fields to r...
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ISBN:
(数字)9798350377972
ISBN:
(纸本)9798350377989
This paper seeks to establish the energy conservation by using piezo-electric effect through some Modern Technology's and the project aims to investigate the application of modern technology in various fields to reduce energy consumption and increase energy efficiency. We focus on three key areas: buildings, transportation, and industry. In each of these areas, we identify the most significant energy consumption sources and potential opportunities for energy savings using modern technology. This aims to explore innovative solutions for sustainable energy generation by harnessing mechanical energy from human movement. This research focuses on integrating piezoelectric cells into everyday footwear and flooring tiles to convert mechanical stress into electrical energy. The project involves designing and fabricating prototypes of energy-harvesting shoes and tiles. The efficiency of energy conversion, the durability of the materials, and the overall feasibility of integrating such systems into daily life will be evaluated. The study will also address the scalability of this technology and its potential impact on reducing the reliance on traditional power sources.
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