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Composer Classification Using Maximum Probability Partitioning Based on Compression Principles

Transactions of the Japanese Society for Artificial Intelligence 2024年第2期39卷 1-10页

作者： Takamoto, Ayaka Hironaka, Shiori Umemura, Kyoji Department of Computer Science and Engineering Toyohashi University of Technology Japan Academic Center for Computing and Media Studies Kyoto University Japan

Music classification is a fundamental task in the field of Music Information Retrieval. This paper focuses on composer classification, a specific task within music classification. Compressive techniques are commonly employed in such music classification tasks. In this study, we propose a method to apply the computing Information Quantity using Maximum Probability partitioning to music classification. To evaluate the effectiveness of our proposed method, we perform composer classification, specifically distinguishing between Haydn and Mozart, who are well-known for their stylistic similarities. The experimental results demonstrate that our proposed approach outperforms traditional compression-based classification methods. Furthermore, we compare our method with non-compressive techniques, discussing the significance of feature extraction methods. Our proposed method is a parameter-free classification approach that does not require domain-specific musical expertise or feature extraction based on such expertise. © 2024, Japanese Society for Artificial Intelligence. All rights reserved.

关键词： Classification (of information)

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Privacy and Security Concerns in Edge-Cloud Systems: An In-Depth Analysis 3

Privacy and Security Concerns in Edge-Cloud Systems: An In-D...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Kaki, Samuel Suhas Kantareddygari, Jagadeswara Reddy Aishwarya, R. School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

Edge-Cloud systems provide efficient computation and storage close to data sources, with lower latency, scalability, and application performance for various applications, such as IoT, autonomous vehicles, and real-time analytics. These systems are highly vulnerable to significant security and privacy challenges, which include the following: data confidentiality and integrity in the process of transmission, secure protection of sensitive data stored on the edge against physical and remote attacks, and maintaining availability under dynamic network conditions. More challenges related to ensuring users' private nature through anonymizing data, obtaining consent for all processes involved, and compliance with specific regulations like GDPR and CCPA. This work incorporates more enhanced cryptographic methods in the transfer of data between heterogeneous systems and environment-based anomaly detection, advanced privacy-preserving analytics to avoid extraction of private data, adaptive control mechanisms in place for Edge-Cloud for minimizing vulnerability with enhanced trust along with the maintenance of data integrity. The key benefits would be robust mitigation of threats, better interoperability between edge and cloud platforms, and alignment with evolving security standards, which promotes wider adoption across various sectors. © 2025 IEEE.

关键词： Cloud platforms

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An Insightful Approach to Cardiomegaly through CNN and Feature Mapping 1

An Insightful Approach to Cardiomegaly through CNN and Featu...

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1st International Conference on Computational Intelligence for Security, Communication and Sustainable Development, CISCSD 2024

作者： Manikandan, R. Mythili, B. Pushyaraga, R. Reddy, M. Karthik Reddy, B. Mallikarjun Madanapalle Institute of Technology and Science Computer Science and Technology Madanapalle India

ISBN: (纸本)9798350365405

The study presents a new approach to identifying cardiomegaly using advanced convolutional neural networks, more specifically, MobileNet and VGG architectures, with interpretative feature maps. Cardiomegaly, or an enlarged heart, is an important sign of particular cardiovascular disorders. Consistently, this topic is an early finding in individual x-ray projection, and because of the general precision of classical methods, innovative deep learning algorithms are needed. In the first stage, the MobileNet and VGG models are designed on distinct heart photo galleries used to cardiac image classification. Systems are seen throughout the domain as vital utilities designed for photo classification. Ultimately, to simplify the solution, an interpreted feature map is employed. Therefore, the technique becomes more reliable, and the design presents a quantitative statement of whether the accessible results say anything concerning the causes of the cardiomegaly estimates. This research has been extensively tested with datasets and produces better results than the existing methods. The presented model grammar map is interpretable, which aids in the medical professional's comprehension of the model's prioritizes and boosts their self-assurance in the diagnostic procedure. Furthermore, the novel approach is versatile with various imaging modalities and patient populace, which suggests extensive practical plays. With the help of an interpretable feature map and the combined usage of VGG appearances and MobileNet advantages, this work complements to the field of substantiation the identification of cardiomegaly. As a final point, the introduced model acquires an excellent amount of truthfulness and resolves a serious predicament of interpretability. Thus, it satisfies the growing stress on lucidness and dependability of deep learning applications in medical substantiation. © 2024 IEEE.

关键词： Convolutional neural networks

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Applications of Clustering in Cognitive Radio Networks 15

Applications of Clustering in Cognitive Radio Networks

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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Joykutty, Anu Maria Baranidharan, B. Rajagiri School of Engineering & Technology Department of Computer Science & Engineering Kochi India SRM Institute of Science & Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331300579

With the use of cognitive radio (CR) technology, wireless devices can utilize licensed spectrum when it is unused by the licensed users. CR was developed as a response to the wireless spectrum shortage issue caused by the static spectrum allocation. Using CR technology, the unlicensed users can opportunistically access the spectrum during the absence of usage by the licensed users. Clustering is a widely used technique in Cognitive Radio Networks (CRNs) to efficiently manage network resources, optimize spectrum utilization, and enhance overall network performance. This study focuses on the applications of clustering techniques in the area of CRNs like spectrum management, QoS Support, topology management, energy efficiency, routing etc. © Grenze Scientific Society, 2024.

关键词： Cognitive radio

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A brief review on Quantum Key Distribution Protocols

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Multimedia Tools and Applications 2025年 1-40页

作者： Kumar, Mandeep Mondal, Bhaskar Department of Computer Science & Engineering National Institute of Technology Patna Bihar Patna800005 India

Development in Quantum computing paves the path to Quantum key distribution (QKD) by using the principles of quantum physics. QKD enables two remote parties to produce and share secure keys while removing all computing constraints on an adversary. The basic physics laws are used to identify any outside parties eavesdropping on the key exchange. In recent years many revolutionary developments in the field of QKD have been developed to overcome security and networking constraints. This survey provides an overview of the QKD protocol’s evolution and quantum network architecture. The paper also demonstrates QKD deployment techniques and elements of the QKD network. It also highlights ongoing design challenges by considering security and error estimation and correction, in contrast to studies concentrating on optical channels and equipment. Finally, this paper examines the possible directions for future research and offers design principles to guide the development of QKD and its related area. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Quantum computers

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Using a Predictive Approach to Identify and Treat Hormonal Imbalance and Irregular Period of Ovarian System 3

Using a Predictive Approach to Identify and Treat Hormonal I...

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3rd International Conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Vijayakumaran, C. Sharma, Pulkit Ankit, Rajeet Srm Institute of Science and Technology Department of Computing Technology Kattankulathur Chennai India

ISBN: (纸本)9798350375190

The syndrome of polycystic ovary has a wide range of symptoms and inconsistent manifestations, making diagnosis difficult. The proposed model intends to differentiate hormonal imbalance from related conditions by evaluating various datasets that include clinical and hormonal parameters. By combining the medical knowledge with machine learning's potential for enabling precision diagnostics, this study advances early detection and enhances patient care. To empower women to achieve optimal reproductive health and well-being, our understanding of hormonal imbalances in the ovaries must be advanced to develop targeted interventions. This research study highlights the importance of multidisciplinary collaboration, ongoing research efforts, and personalized approaches in addressing the complexities of hormonal imbalance-related disorders in the ovary. The predictive model gains the ability to precisely predict irregular periods and accurately forecast fluctuations in hormone levels through iterative refinement and validation processes. The proposed model also includes personalized treatment recommendations based on each patient's unique needs. These recommendations include dietary adjustments, lifestyle adjustments, hormonal therapies, and stress reduction methods. The model attempts to empower women to take proactive measures towards optimizing their reproductive health and general well-being by fusing predictive analytics with proactive intervention strategies. © 2024 IEEE.

关键词： Machine learning

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Scalability and Reliability of Port Simulation Workflow on Slurm 15th

Scalability and Reliability of Port Simulation Workflow on...

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15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Drozdowski, Maciej Wawrzyniak, Jakub Marszalkowski, Jakub Institute of Computing Science Poznań University of Technology Poznań Poland

ISBN: (纸本)9783031856990

Parallelizing of container terminal seaside layout optimizer using a Slurm cluster is considered in this paper. Maritime container terminals have quays divided into discrete berth segments. The number of berths and their lengths have to be adjusted to the arriving vessel traffic for high quality of service. A particular partition of the terminal quay into berths is called terminal layout. The vessel traffic is represented by the stochastic ship traffic model (STM). An instantiation of the STM is a specific ship arrival scenario. Evaluation of a quay layout on an arrival scenario requires scheduling vessels on the berths, which is a classic Berth Allocation Problem (BAP) of maritime logistics. The problem of partitioning terminal quay into berths, subject to vessel traffic, for high quality of service will be called a Stochastic Quay Partitioning Problem (SQPP). In this paper, we present a hill-climber metaheuristic for SQPP. The process of the partition evaluation is conducted on a Slurm cluster by a simple workflow. We report on the scalability and reliability of this workflow. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Stochastic systems

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An Innovative Deep Learning-Based Predictive Maintenance Model for Smart Automotive System

An Innovative Deep Learning-Based Predictive Maintenance Mod...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Indumathi, V. Ashokkumar, C. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India

ISBN: (纸本)9798331509828

This research presents an innovative deep learning-based predictive maintenance model designed for smart automotive systems, utilizing the EnsembleAE-Boost (EAE-Boost) algorithm. The primary objective of the proposed model is to enhance the prediction accuracy and fault detection capabilities of smart vehicles through advanced machine learning techniques. The EAE-Boost algorithm, an ensemble model integrating auto encoders and boosting, is compared with existing algorithms, namely K-Nearest Neighbors (KNN), Deep Neural Networks (DNN), and Extreme Gradient Boosting (XGBoost), based on key simulation metrics, including accuracy, precision, prediction time, and fault detection rate. Simulation results demonstrate that the EAE-Boost algorithm outperforms the existing algorithms, achieving superior performance in all metrics. Notably, the proposed method showed significant improvements in accuracy and fault detection rate, with a reduction in prediction time, highlighting its potential for real-time maintenance applications in smart automotive systems. The findings underscore the effectiveness of the proposed deep learning model in enhancing vehicle reliability, safety, and operational efficiency. © 2025 IEEE.

关键词： Predictive maintenance

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Enhancing sustainable 5G powered agriculture 4.0 : Summary of low power connectivity, internet of UAV things, AI solutions and research trends

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Multimedia Tools and Applications 2025年第17期84卷 17389-17433页

作者： Majumdar, Parijata Mitra, Sanjoy Bhattacharya, Diptendu Bhushan, Bharat Department of Computer Science and Engineering Techno College of Engineering Agartala Maheshkhola Madhuban799004 India Department of Computer Science and Engineering Tripura Institute of Technology Agartala Narsingarh Agartala799009 India Department of Computer Science and Engineering National Institute of Technology Agartala Jirania Agartala799046 India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida201310 India

Emerging technologies of Agriculture 4.0 such as the Internet of Things (IoT), Cloud computing, Artificial Intelligence (AI), and 5G network services are being rapidly deployed to address smart farming implementation-related concerns and to fulfill the increasing food demand of the expanding demographics. Adoption of 5G offers numerous benefits including Ultra-Reliable Low Latency Communication (URLLC) required to reduce communication delays in time-sensitive applications, non-public networks to allocate part of the frequency spectrum on demand, and network slicing to fulfill the diverse requirements requested by a specific application etc. for realizing the full potential of Agriculture 4.0 operations. In this paper, a comprehensive review of 5G radio enhancements, enabling technologies, and multi-tier heterogeneous 5G network architecture are presented. The main focus is to concisely cover AI integration with Unmanned Aerial Vehicles (UAVs) leveraging 5G technologies and Low Power Wide Area Networks (LPWANs) for supporting intelligent, long-range, cost-effective and energy-saving data transmission across the network and seamless Agriculture 4.0 operations. Cloud computing architectures that permit centralized management of Agriculture 4.0 operations through sharing of computations, configurable networks, and servers are also studied in detail to reveal the need for edge computation as increasing number of IoT devices allows only to perform complex computations on-site. Extensive survey reveals the recent advances of 5G-enabled Agriculture 4.0 operations, future research opportunities of 5G networks including Energy Harvesting techniques for the deployment of massive to critical IoT applications, and the socio-demographic impacts of 5G networks in Agriculture 4.0 to support ubiquitous connectivity of exponentially increasing IoT devices. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Energy efficiency

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Exploring ensemble optimized voting and stacking classifiers through Cross-validation for early detection of suicidal ideation

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Journal of Intelligent and Fuzzy Systems 2024年第5-6期47卷 335-349页

作者： Shukla, Shiv Shankar Prasad Singh, Maheshwari Prasad Department of Computer Science Engineering National Institute of Technology Patna Bihar Patna India

Detecting behavioral changes associated with suicidal ideation on social media is essential yet complex. While machine learning and deep learning hold promise in this regard, current studies often lack generalizability due to single dataset reliance. Traditional embedding techniques struggle with semantic analysis,leading to challenges in achieving high accuracy models and conventional validation methods have data drift limitations. To address these challenges, this study proposes a novel evaluation approach using natural language processing across diverse platforms like Twitter and Reddit. By integrating BERT embedding, adept at handling semantic nuances, with an optimized Stacked Classifier combining different base classifiers and XGBoost as the meta-classifier, the model excels in swiftly detecting signs of suicidal ideation compared to the Voting Classifier, i.e., the combination of Decision Tree, Random Forest, Gradient Boost and XGBoost and several machine learning models. Additionally, the study explores advanced embedding techniques like MUSE and LLM, and deep learning models including Bi-LSTM, Bi-GRU, and Text-CNN for *** ensemble approach aims to create a model that is not only interpretable but also robust, reducing computational complexity and enhancing resilience against noisy data - common challenges faced in text classification tasks. Through K-fold validation, which involves partitioning the dataset into k equal-sized subsets or "folds"and training the model k times, using k-1 folds for training and one-fold for testing each time, the proposed model achieves impressive accuracy rates of 97% on Reddit and 96% on Twitter datasets, underscoring its effectiveness in identifying suicidal ideation across social media platforms. © 2024 - IOS Press. All rights reserved.

关键词： Semantics

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