Music is a universal language that comes in a wide variety of genres to suit different interests and moods. In order to achieve this organization, music genre classification—the process of automatically classifying a...
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ISBN:
(数字)9798350376685
ISBN:
(纸本)9798350376692
Music is a universal language that comes in a wide variety of genres to suit different interests and moods. In order to achieve this organization, music genre classification—the process of automatically classifying a piece of music according to its auditory content—is essential. A multi-class music genre identification model has been constructed in the proposed study, and ten distinct classifiers, including K-NN, Random Forest, Cat Boost, XG Boost, Decision Tree, etc., have been tested. A popular benchmark dataset for classifying musical genres, the GTZAN dataset offers a wealth of information for developing and assessing classification algorithms. In contrast, CatBoost reports the best outcome, with an F1-score of 0.89.
Using Internet of Things (IoT) devices has become more efficient and convenient, but it has also increased the potential of security breaches, particularly from ransomware and Distributed Denial of Service (DDoS) assa...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
Using Internet of Things (IoT) devices has become more efficient and convenient, but it has also increased the potential of security breaches, particularly from ransomware and Distributed Denial of Service (DDoS) assaults. This study looks into the ever-changing threats posed by ransomware on the Internet of Things, emphasizing human-centered detection and mitigation techniques. It highlights attack methods like supply chain breaches and device exploitation that jeopardize privacy and service continuity. To enhance IoT security, a deep neural network-based approach is proposed for automated reaction and real-time monitoring. The superior precision and efficacy of the suggested approach in mitigating ransomware and DDoS assaults in contrast to existing techniques underscores the significance of robust security protocols and active stakeholder engagement. This paper’s goal is to provide a thorough study of these dangers along with a novel solution that will improve security protocols in IoT ecosystems. This study seeks to close the gap between sophisticated technological protections and workable, user-oriented tactics by highlighting the need of human-centered approaches, guaranteeing that IoT devices continue to be dependable and safe in an increasingly interconnected world. This study emphasizes the vital need for an integrated strategy to IoT security that incorporates both cutting-edge technology and proactive human engagement through thorough evaluation and suggested solutions.
Rainfall prediction is not an easy task when utilizing conventional approaches. For many stakeholders, including people planning their daily lives, farmers caring their crop, and fishermen who depend on the weather fo...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
Rainfall prediction is not an easy task when utilizing conventional approaches. For many stakeholders, including people planning their daily lives, farmers caring their crop, and fishermen who depend on the weather for their livelihood, accurate rainfall forecast is essential. Predicting rainfall more accurately has been a promising use of machine learning (ML) models in recent years. Our proposed framework suggests using classification techniques like Random Forest, K-Nearest Neighbors (KNN) classifier, and Decision Tree to construct and implement an ML model for rainfall prediction. SMOTE, an efficient data-balancing technique is used for balancing the minority and majority classes of the data, as rainfall is not a uniformly distributed event, leading to an imbalanced dataset. With an astounding F1-score of around 0.91, a precision of about 0.89, a recall of about 0.94, and an accuracy of roughly 0.91, the Random Forest classifier outperformed the others. These results demonstrate how machine learning (ML) models could possibly be used in producing accurate and depewhich are effective forndable rainfall forecasts, showing significant improvements over conventional prediction techniques.
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 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.
This study, employed the Fuzzy C-Means clustering algorithm to segment mall customers based on various demographic and spending attributes. Using a dataset of 200 mall customers, the study analyzed their annual income...
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Neighbor Discovery Protocol (NDP) is one of the core protocols of IPv6 networks. Since NDP messages are an unauthenticated stateless protocol, they are vulnerable to various types of attacks, and Man-In-The-Middle (MI...
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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.
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.
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