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Exploring current research trends in sound event detection: a systematic literature review

Multimedia Tools and Applications 2024年第37期83卷 84699-84741页

作者： Mohmmad, Sallauddin Sanampudi, Suresh Kumar School of Computer Science and Artificial Intelligence SR University Telangana Warangal India Research Scholar JNTU Hyderabad India Department of Information Technology JNTUH College of Engineering Nachupally Kondagattu Telangana Jagtial India

Sound Event Detection (SED) plays a significant role in the present research, implemented in several areas such as computer science, Healthcare, Environmental science, Security and Surveillance, etc. With the advancement of technology, SED can be deployed to mimic the human auditory system. In this paper, we have undertaken a Systematic Literature Review focused on sound event detection, presenting a comprehensive and well-structured analysis and in-depth discussions. This review is based on the authors' extensive knowledge and expertise in the field, and it compares various algorithms employed for sound event detection. The primary objective of this study is to offer valuable insights into datasets, feature extraction techniques, and execution models commonly used in SED, along with an examination of their corresponding accuracy, challenges, and limitations. Furthermore, the paper delves into identifying potential trends within the field, offering forward-looking information that can be invaluable for future research and development efforts in sound event detection. This systematic review aims to contribute to the continued advancement of SED technologies and applications by synthesizing existing knowledge and identifying emerging directions. It provides a foundation for researchers, practitioners, and stakeholders to make informed decisions and explore new possibilities within this evolving domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Using Blue Whale Technology: An ML Edge Self-Adaptable Vehicle Slowdown Earliest Warning information System 8th

Using Blue Whale Technology: An ML Edge Self-Adaptable Vehic...

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8th International Conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Murali, N. David, D. Beulah Research Scholar Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Department of Data Analytics Institute of Information Technology Saveetha School of Engineering SIMATS Tamilnadu Chennai India

ISBN: (纸本)9789819784219

Human life is challenged by this main work’s ultimate goal of reducing accidents and ensuring life safety due to the enormous growth of vehicles and those based on safety. Here, cases of suspected drunk driving, reckless driving, etc., are present. In this scenario, vehicles are traveling at full speed and irresponsibly using technology on all roads. An early warning system is used to stop auto accidents. In order to accommodate the city's steadily increasing automobile population, intelligent traffic systems must be designed in a resourceful and sustainable manner using all available technology in both light and dark. The advanced ML's work on unexpected traffic flow and all road conditions resulted in significant real-time changes in the flow of traffic for every significant issue that arose. Here, the Blue Whale technology makes use of clever intelligence to quickly identify, detect, and modify warning systems. The primary objective of the study was to train CNN/BWT on the collected data sets in order to operationalize the unsupervised approach for the prediction of critical situations. Additionally, it makes an effort to clarify the situation so that guests can feel more at ease and help them decide on transfers in time-sensitive situations. IOT is a practical way to locate the nearest object using machine learning methods that are accurate and apply received data sets, resulting in significant findings that support object recognition. The paperwork fully focuses on improving driving knowledge and offering new approaches for quick object recognition. The Fast Warning System Initiative, which aims to increase daytime safety and security, is providing a prior warning system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Backward wave tubes

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An empirical IoT and cloud-based customizable healthcare surveillance system

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International Journal of information Technology (Singapore) 2024年第8期16卷 5317-5323页

作者： Meti, Subhash Razauddin, S. Nallakumar, R. Mansingh, P. M. Benson Sameen, Aws Zuhair Pandey, Sakshi Bhatt, Sanjeev Kumar Jayabalan, Bhuvana Department of Electronics and Communication Engineering SKSVMACET Lakshmeshwar Karnataka Gadag India College of Computer Science and Engineering University of Hail Hail Saudi Arabia Department of Artificial Intelligence and Data Science Karpagam Institute of Technology Coimbatore India Department of Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Andhra Pradesh Guntur India College of Medical Techniques Al-Farahidi University Baghdad Iraq Centre of Interdisciplinary Research in Business and Technology Chitkara Institute of Engineering and Technology Chitkara University Punjab Rajpura India Software Architect and Research Scholar Digital Innovation College PDM University Bahadurgarh India Department of Computer Science and Information Technology JAIN (Deemed-to-Be University) Karnataka Bengaluru India

In certain emergencies, patients must be continuously monitored and cared for. However, visiting the hospital to do such activities is difficult because of time constraints. To modernize the healthcare sector, the study in question presents an empirical Internet of Things and cloud-based, customizable healthcare monitoring system. Initially, a cloud-based algorithm called particle swarm intelligence (PGI) is used in the proposed healthcare monitoring system to group recorded data, hence increasing the prediction rate. Subsequently, features are selected and extracted, and back propagation neural networks (BPNN) are employed to classify healthcare for humans. In the end, the effectiveness of the suggested system is evaluated using a healthcare dataset and contrasted with current deep learning/machine learning classifiers. The suggested surveillance system performs better than the methods employed in the previous research, according to the findings. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： BPNN Cloud computing Healthcare surveillance IoT Particle swarm intelligence (PSI)

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Pragmatic Analysis of IoMT Network Modelling Techniques from a Statistical Perspective

Pragmatic Analysis of IoMT Network Modelling Techniques from...

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2022 International Conference on Electronics and Renewable Systems, ICEARS 2022

作者： Fuke, Radhika P. Mahajan, R.P. Sage University Department of Information Technology Phd Scholar Indore India Sage University Department of Computer Science Engineering and It Professor Indore India

ISBN: (纸本)9781665484251

Internet of Medical Things (IoMT) networks are high precision wireless interfaces that require design of accurate sensing, actuation, and processing devices. These devices include wireless electrocardiograph (ECG) sensors, electroencephalograph (EEG) sensors, wireless blood pressure monitoring devices, etc. Due to direct patient interface, these devices are required to have superior performance in terms of sensing accuracy, processing efficiency, and communication quality of service (QoS) parameters. A wide variety of models are proposed by IoMT researchers to perform this task, but each of these models vary in terms of network size, deployment complexity, cost of deployment, processing delay, etc. These models include machine learning routing techniques, blockchain based security methods, privacy preservation methods, high-precision sensor design methods, high-performance communication interfaces, etc. Due to such a wide variation in performance, IoMT network design requires continuous validation, which increases time-to-market, thereby increasing deployment cost. In order to reduce number of validations, a statistical survey of models for IoMT network design is discussed in this text. This discussion is focussed towards evaluating various characteristics, advantages, limitations, and future research scopes in existing models. readers would be able to identify best performing model(s) for a given IoMT application. It is followed by a statistical analysis of the reviewed IoMT network design models in terms of end-to-end delay, communication QoS, network security, scalability, computational complexity, cost of deployment, and application of use. This statistical performance evaluation will further assist readers to statistically compare the reviewed methods, and identify best performing model(s) their combinations for context-specific network deployments. Due to this, readers would be able to reduce network design validation delay, which will further assist in reduci

关键词： Scalability

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Unleashing Facial Expression Recognition for Stress Detection Using Deep CNN Model.

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Procedia computer science 2025年 259卷 306-315页

作者： Geethu S Kumar Jo Cheriyan Aparna N Swathy J Research Scholar Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai- 600 119 Tamilnadu India Assistant Professor Department of Information Science and Engineering Cambridge Institute of Technology Bangalore-560 036 Karnataka India Assistant Professor Department of Computer Science and Engineering Saintgits College of Engineering Kottayam- 686 532 Kerala India Assistant Professor Department of Computer Science and Engineering Cambridge Institute of Technology Bangalore-560 036 Karnataka India

The technology and field of study known as Facial Emotion Recognition (FER) focuses on recognising and deciphering human facial expressions to ascertain emotions. It incorporates aspects of psychology, machine learning, and computer vision. Considering its ability to analyse facial expressions and spot signs of stress or emotional distress, FER plays a vital role in the detection of stress. Microexpressions are fleeting, uncontrollably expressed facial expressions that convey true feelings. These ephemeral expressions, which are frequently linked to stress, can be captured by FER systems. With its capacity to manage intricate patterns and variances in face expressions, FER utilising deep learning has grown in popularity. A novel FER system was designed to overcome issues that plague current FER systems, such as imbalanced datasets and robustness to noisy inputs. Large datasets are handled effectively by FER employing Deep CNN because of its architecture, which is built for high-dimensional input like images. Leveraging FER using Deep CNN achieves notable performance improvements, attaining an accuracy of 95.65 % and F1 score of 94.02%. Deep learning-based FER marks a substantial advancement in our ability to recognize and decipher facial expressions that convey emotions. FER technology has the ability to revolutionize several fields by improving intuition and sensitivity to human emotions through further development and thoughtful assessment of ethical ramifications. FER systems will advance in sophistication as science and technology develop, offering more profound understanding of human emotional states and promoting improved human-machine interactions.

关键词： Stress PPG Conv-XGBoost deep learning mental health

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Design of a Multiple Phased Convolution Model for Pre-emption of Breast Cancer via Extensive Learning Operations (MPC2EL)

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Procedia computer science 2024年 233卷 638-650页

作者： Pravin Malve Dr. Vijay Gulhane Research Scholar Department of Computer Science & Engineering Sipna College of Engineering & Technology Amravati 444607 India Professor Department of Information Technology Sipna College of Engineering & Technology Amravati 444607 India

Breast cancer diagnosis from mammographic scans is a complex challenge, demanding accurate and efficient methodologies. Existing approaches often rely on time-consuming manual feature extraction or lack scalability when employed with diverse clinical image sets. To address these limitations, this paper presents an innovative solution: the MPC2EL (Multiple Phased Convolution model for Pre-emption of Breast Cancer via Extensive Learning Operations). The core problem tackled by this research is the need for improved breast cancer detection, especially concerning scalability, accuracy, and practicality. To achieve this, MPC2EL leverages extensive learning operations and multiple phased convolutions to process extensive clinical datasets containing both cancerous and benign images. Our novel approach begins with the conversion of these images into multimodal feature sets, encompassing frequency, cosine, wavelet, and Gabor domains. These feature sets enable enhanced classification of images into different cancer stages. The proposed methodology lies in a Dual 1D Convolutional Neural Network (D1D CNN) architecture, capable of categorizing images initially into malignant and benign types and subsequently refining classification for malignant images. Experimental results reveal a substantial 5.9% increase in classification accuracy and a 4.5% improvement in recall compared to conventional deep learning methods. Moreover, the model demonstrates lower processing delay and higher precision, making it a promising candidate for real-world clinical deployment.

关键词： Cancer Mammogram Multiple Phased CNN Gabor Cosine

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Investigation of Task Scheduling in Cloud Computing by using Imperialist Competitive and Crow Search Algorithms

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Procedia computer science 2023年 230卷 879-889页

作者： Jayavadivel Ravi Rajkumar N Viji C Loganathan D K S N Sushma Stalin M Department of Computer Science & Engineering Alliance College of Engineering and Design Alliance University Bangalore Karnataka India Department of Information Science & Engineering Cambridge Institute of Technology Bangalore Karnataka India Research Scholar School of Computer Science and Engineering REVA University Bangalore Karnataka India

Cloud Storage is a complex method that is a method of processing and data of a cloud built by duplication of thousands of related devices in a complex manner.... 详细信息

Cloud Storage is a complex method that is a method of processing and data of a cloud built by duplication of thousands of related devices in a complex manner. The main function of the data processing server is to show how many users are being investigated and provide accurate, efficient and efficient information. Important Algorithm Editing Players in the Cloud Defines the virtual machine (VM) required for this purpose. The role of editing the algorithm reduces the effect of the schedule. Naturally affected algorithms have recently been used to quickly comply with the recent traditional algorithms. Given many consumers of many cloud computing services, many researchers may have a serious explanation that many researchers take and discuss the complex themes of NP. Some sites use imperialist algorithms (ICA) and birds. The purpose of the proposed project is to develop intelligent scientific algorithms that focus on the integration of ICA and CSA to obtain data. CSA is concentrated on the corner of food habits. The crow is looking for his friends to get enough food for today's food. This will help the CSA find suitable VMs for these machines and complete the equipment. Cloud Sim is used to calculate CSA output with minmin and ant algorithms. The simulation results show that the CSA is extra powerful than the MinMin and Ant procedures .

关键词： Crow search algorithm Imperialist competitive algorithm. Task scheduling Nature-inspired and Cloud computing

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SOSO: Symbiotic Organisms Search Optimization based Faster RCNN for Secure Data Storage in Cloud

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IETE Journal of Research 2024年第2期70卷 1196-1208页

作者： J. Thresa Jeniffer A. Chandrasekar S. Jothi 1 Research Scholar Department of Information Technology St. Joseph's College of Engineering Anna University Chennai India 2 Department of Computer Science and Engineering St. Joseph’s College of Engineering Chennai India

Because of the Internet of Things (IoT) and wearable devices, information obtained is subject to cyber attacks, making intrusion detection an essential component. The increased number of attacks on IoT devices exposes them to ongoing exploitation and data theft. Mirai, denial of Service (DoS), scan,etcare the major types of attacks conducted on IoT. Traditional attack detection algorithms have more disadvantages like lack of adaptability in all scenarios, low local minima computational complexity,etc. Thus developing a method that is suitable for all environments and has good convergence speed is taken as the motivation of this study. This paper presents a novel Symbiotic Organisms Search Algorithm (SOSA) optimized Faster region-based convolutional neural network (FRCNN) for attack detection and data type classification. The data to be stored in the cloud is analyzed whether they have restricted access, confidential, or unclassified. Depending upon the variety of data, the Optimal homomorphic encryption (OHE) approach is provided for security. The key selection process is done by an Elastic Collision Seeker Optimization Algorithm (ECSOA) which offers secure access to the data owner and prevents the data from unauthorized access. The model is optimized using the algorithm which improves the feature extraction performance. The effectiveness of the proposed model in attack detection is evaluated using two datasets namely the CSE-CIC-ID2018 dataset and the IoTID20 dataset. When compared to the existing techniques, the proposed model offers optimal performance in terms of multiclass attack detection.

关键词： Deep learning Intrusion detection IoT Machine learning Optimal homomorphic encryption Secure storage

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Household Appliance Identification Using Vision Transformers and Multimodal Data Fusion

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IEEE Transactions on Consumer Electronics 2025年

作者： Ayub, Mohammed El-Alfy, El-Sayed M. King Fahd University of Petroleum and Minerals Information and Computer Science Department Dhahran31261 Saudi Arabia Computer Engineering Department and Senior Research Scholar Saudi Arabia

Accurately identifying household appliances from power consumption data collected via smart meters opens up new possibilities for improving energy management in smart homes and providing substantial benefits to both utilities and consumers. It enables real-time optimization of energy use, offers personalized savings recommendations, enhances demand forecasting, provides detailed appliance load profiling, and supports the promotion of energy-efficient technologies. While low-resolution consumption data are preferred due to the limited processing capabilities of residential smart meters, they lack the granularity needed to capture detailed consumption patterns, resulting in performance degradation in many cases. This paper explores a novel approach based on a revised version of a vision transformer for household appliance identification using low-resolution and low-volume data. To maintain superior algorithmic performance, we first fuse different time-series imaging to augment and compensate for features that might be missed by a single technique, enabling efficient and robust feature representation. Next, real-time data augmentation and pretrained weights from Hugging Face transformers are leveraged and fine-tuned through transfer learning to enhance model performance with limited data, accelerate the training process, and improve model generalization. We compared the three variants of our proposed solution: (i) multi-class classification problem, (ii) multi-label classification problem, and (iii) multi-target appliance-specific classification problem. Extensive experiments on four public datasets (ENERTALK, UK-DALE, iWAE, and REFIT) demonstrate that our proposed multimodal data fusion vision transformer outperforms non-fusion baseline models. It achieved near-perfect results across multi-class, multi-label, and multi-target tasks, with overall F1 scores above 97% and perfect scores for several appliances. Several cross-house and cross-dataset experiments were also c

关键词： Energy utilization

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Heterogeneous IHRRN Scheduler Based on Closeness Centrality 3rd

Heterogeneous IHRRN Scheduler Based on Closeness Centrality

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3rd International Conference on Computing and Communication, IC3 2020

作者： Seethalakshmi, V. Govindasamy, V. Akila, V. Research Scholar Department of Computer Science and Engineering Pondicherry Engineering College Puducherry India Department of Information Technology Pondicherry Engineering College Puducherry India Department of Computer Science and Engineering Pondicherry Engineering College Puducherry India

ISBN: (纸本)9789811573934

Big Data is the current emerging trend in data analytics. MapReduce is the programming model which is widely used for the data intensive applications in the Big Data environment. Scheduling of job attempts to provide faster processing of jobs and to reduce the response time to the minimum by using innovative techniques for allocating system resources depending on the nature of jobs. The default scheduling algorithm takes up Hadoop homogeneous environment. This homogeneity consolidation does not always work in use and restricts the MapReduce efficiency. There is a need for addressing heterogeneous system. Data locality is primarily bringing computation nearer to input information so as to provide faster access. MapReduce does not always test the heterogeneity from the point of view of the data locality. Improvising the data locality for Map Reduce system is an endearing problem to increase the fruition of Hadoop environment. The existing job scheduling method of Hadoop is Self Adaptive Reduce Scheduling (SARS) algorithm. SARS concentrates only on the reducer phase and the algorithm may cause data locality problem. This paper highlights a new concept to further improve the utility of the SARS technique. This paper presents a new technique that efficiently uses closeness centrality—the shortest distance of all the nodes in the cluster from the data node. Node’s closeness centrality analyses its average distance to all other nodes. The proposed algorithm is Improved Highest Response Ratio Next scheduling with Closeness Centrality (IHRRN_CC). Considering the Node Processing Capacity (NPC), another variant of IHRRN Scheduling—IHRRN_NPC is also proposed. The SARS algorithm is compared with Improved Highest Response Ratio Next (IHRRN) scheduling with Closeness Centrality (IHRRN_CC). The proposed scheduling algorithm is compared to state-of-the-art schedulers utilizing heterogeneous workloads in a heterogeneous Hadoop environment from the Hi-Bench benchmark array. The result

关键词： MapReduce

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