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3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Basha, S.K. Mahaboob Gurumoorthi, E. Beulah, E. Mercy Kesavan, R. Kalaiselvan, S.A. Kumar, Lenin Tkr College of Engineering and Technology Department of Computer Science and Engineering Telangana India Cmr College of Engineering & Technology Department of Computer Science and Engineering Telangana India Vel Tech Multi Tech Dr. Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Chennai Avadi India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India Vignan Institute of Technology and Sciences Department of Electronics Communication and Engineering Telangana India

ISBN: (纸本)9798350369083

An essential part of wireless sensor networks (WSN) is managing congestion, which occurs when the amount of traffic exceeds the combined or individual capacities of the underlying channel. As a result, developing more advanced methods to prevent, detect, and alleviate congestion requires particular considerations. In order to obtain the highest throughput, these strategies must take into account the limited resources of the WSN. Dedicated congestion control strategies and routing protocols that help in congestion detection as well as management are two of the many methods that have been created in recent years. As a means of avoiding congestion, the sink node in the previous designs would reset the topology and reduce bulk traffic. In this article, we lay the groundwork for WSNs and AI, the latter of which will try to prevent congestion in WSNs from the get-go and the former of which will try to minimize the spread of impending congestion by rerouting traffic. An integral part of modern life, Artificial Intelligence (AI) allows us to create self-governing systems. It is possible to enhance performance and reliability by making use of several sophisticated AI approaches. Better network management and routing in WSN may be the outcome of studying the AI algorithm used by WSN, leading to a more dependable network. Here we'll take a look at several AI algorithms that have found their way into WSNs, and we'll talk about how these algorithms might help WSNs overcome their challenges and become more reliable and efficient. Additionally, artificial intelligence has often been disregarded in these networks because of limitations on data processing as well as power consumptions. Thanks to AI, WSN is now lightning fast, error-free, as well as effective in almost every way. Using AI methods in nodes of wireless sensors for congestion aware routes is the goal of this work. © 2024 IEEE.

关键词： Traffic congestion

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Distributed Voltage Regulation of Distribution Network Based on Partition Control 2

Distributed Voltage Regulation of Distribution Network Based...

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2nd International Conference on Cyber-Energy Systems and Intelligent Energy, ICCSIE 2024

作者： Jine, Wang Hui, Chen Liying, Zhao Guangxin, Zhang School of Electrical Information Changchun University of Architecture and Civil Engineering Changchun City China School of Computer Technology and Engineering Changchun Institute of Technology Changchun City China School of Information and Engineering College of Science & Technology Ningbo University Ningbo City China

ISBN: (纸本)9798350373318

As China's social economy progresses, the amount of equipment in distribution networks continues to grow, leading to increasingly complex system operations. In this environment, traditional centralized control methods consume excessive computing resources and fail to achieve the processing speeds required by modern intelligent distribution networks. To address these challenges, this article introduces the concept of electrical distance to segment the distribution network and proposes voltage regulation algorithms based on the alternating direction multiplier method and dual ascent method for efficient distributed voltage regulation. © 2024 IEEE.

关键词： DC distribution systems

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Enhanced Camouflage Detection Using Advanced Image Processing Techniques for Real-Time Object Recognition and Pattern Differentiation 5

Enhanced Camouflage Detection Using Advanced Image Processin...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Manikandan, G. Deepa, R. Jayalakshmi, V. Thilakavathy, P. Surendran, R. St. Joseph's College of Engineering Anna University Department of Artificial Intelligence and Machine Learning Chennai India School of Computing SRM Institute of Science and Technology Department of Computing Technologies Chennai India SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India Vels Institute of Science and Engineering Vels Institute of Science Technology and Advanced Studies Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530013

The identification of objects that blend in with their surroundings has long been a concern in fields including defence, wildlife monitoring, and surveillance due to the difficulty of detecting such objects. This finding is significant because it could greatly enhance our capacity to detect camouflaged objects, transforming the field of real-time object detection. Existing image processing methods often fail miserably when adequately capturing this aspect. This dissertation's primary focus is identifying devices with complicated patterns, legacy interference, and inadequate contrast;nonetheless, in disguised scenarios, all of these features cause identification to be unsuccessful. This research often suggests a new approach called the Linear Edge Detecting Scheme for Camouflaged Object Detection (LEDS-COD) in these difficult cases. The LEDS-COD system can enhance item visibility in low-comparison settings by highlighting critical edges and descriptions. Pattern differentiation methodologies and modern facility-side recognition techniques are combined to accomplish this goal. The approach successfully differentiated items because it zeroed in on texture and comparative details that would otherwise go unnoticed by humans or advanced algorithms. LEDS-COD is useful in many areas, including the Navy, autonomous surveillance systems, and animal protection, where the real-time identification of hidden devices is essential. Results from a simulation study using various datasets, including concealed objects, show that LEDS-COD surpasses conventional detection methods in recognition accuracy, processing speed, and perseverance. The recommended technique exceeded the requirements for obtaining improved object detection estimates for challenging conditions like those with complex backdrops and changing illumination. According to this study, LEDS-COD can be devastating in improving real-time camouflage detection. © 2024 IEEE.

关键词： Accuracy Image processing Surveillance Image edge detection Wildlife Lighting Object detection Real-time systems Object recognition Protection

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Enhanced Speech Emotion and Gender Recognition Using Hybrid CNN-LSTM Models 4

Enhanced Speech Emotion and Gender Recognition Using Hybrid ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Priyadharshini, S. Sundar, G. Naveen Narmadha, D. Thangavel, Senthil Kumar Ajibesin, Adeyemi Abel Kolandapalayam Shanmugam, Selvanayaki Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Amrita School of Computing Department of Computer Science and Engineering Coimbatore India Cape Peninsula University of Technology Faculty of Informatics and Design Department of Information Technology South Africa Ashland University Department of Mathematics and Computer Science United States

ISBN: (纸本)9798331523923

The current paper proposes a new approach for peripheral speech emotion analysis and gender estimation incorporating the best machine learning architectures such as CNNs and LSTMs. Its correct depiction of emotions and the variation of the gendered speech to indicate manifold signal-to-noise ratio make the approach accommodate the difficulty inherent in recognizing emotions from the recording of voices as well as predicting the gender of a speaker. It is then able to ally with preprocessing and collection of a vast range of different speech samples, feature extraction of pitch, tone and spectral data. These features serve as input to the CNNs and LSTMs: LSTMs work well at temporal regular features and the CNNs work well in feature extraction by location (spatio feature). The approach is tested on the RAVDESS and IEMOCAP datasets and establishes a near perfect match to prior approaches with reference to recognition accuracy of emotion analysis and gender prediction. Thus, applying the identified dual method approach, which could be described by such characteristics as highly accurate data pre-preprocessing, clear indication of the features of interest, a different CNN-LSTM model joint training, it offers a reasonably good platform to gain certain insights into such characteristics of the speech as our experiment demonstrates. This work offers satisfactory configurations on concerns such as human computer relation, customer relation and mental health surveillance to suggest that other applications could be achieved in future. © 2025 IEEE.

关键词： Convolutional neural networks

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Identification of Children with ADHD from EEG Signals Based on Entropy Measures and Support Vector Machine 11

Identification of Children with ADHD from EEG Signals Based ...

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11th European Workshop on Visual Information Processing, EUVIP 2023

作者： Maniruzzaman, Md. Al Mehedi Hasan, Md. Suzuki, Taro Shin, Jungpil The University of Aizu School of Computer Science and Engineering Aizuwakamatsu965-8580 Japan Rajshahi University of Engineering and Technology Dept. of Computer Science & Engineering Rajshahi6204 Bangladesh

ISBN: (纸本)9798350342185

Attention deficit hyperactivity disorder (ADHD) is one of the major psychiatric and neurodevelopment disorders that affects 11% of children worldwide. Moreover, the prevalence of ADHD has rapidly increased over time worldwide. According to DSM-V, three types of symptoms such as inattention, hyperactivity, and combined type (inattention with hyperactivity). So, it is necessary to use a simple, non-invasive, and automatic detection system for identifying children with ADHD at an early stage. The objective of this study was to propose a machine learning (ML)based ADHD-combine type (ADHD-CT) detection system from electroencephalogram (EEG) signals. EEG signals were recorded from nineteen ADHD-CT children and fourteen healthy children. We extracted five entropy-based features such as approximate-based entropy, Shanon-based entropy, permutation-based entropy, sample-based entropy, and singular value decomposition (SVD)-based entropy from each signal. The subset of the most informative and discriminative features was selected for ADHDCT using sequential forward floating selection (SFFS). Following that, support vector machine (SVM) was implemented with leave-one-out cross-validation for the identification of ADHD-CT children and assessed its performances based on classification accuracy. Our results illustrated that SVM with polynomial kernel provided 96.87% classification accuracy to discriminate children as ADHD-CT and healthy children. Our findings showed that our proposed system can be used to detect children with ADHD-CT. © 2023 IEEE.

关键词： Support vector machines

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Wavelet-Based Mamba with Fourier Adjustment for Low-Light Image Enhancement 17th

Wavelet-Based Mamba with Fourier Adjustment for Low-Light ...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Tan, Junhao Pei, Songwen Qin, Wei Fu, Bo Li, Ximing Huang, Libo School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Computer Science and Artificial Intelligence Liaoning Normal University Liaoning116081 China College of Computer Science and Technology Jilin University Jilin134000 China School of Computer National University of Dense Technology Changsha410073 China

ISBN: (纸本)9789819609109

Frequency information (e.g., Discrete Wavelet Transform and Fast Fourier Transform) has been widely applied to solve the issue of Low-Light Image Enhancement (LLIE). However, existing frequency-based models primarily operate in the simple wavelet or Fourier space of images, which lacks utilization of valid global and local information in each space. We found that wavelet frequency information is more sensitive to global brightness due to its low-frequency component while Fourier frequency information is more sensitive to local details due to its phase component. In order to achieve superior preliminary brightness enhancement by optimally integrating spatial channel information with low-frequency components in the wavelet transform, we introduce channel-wise Mamba, which compensates for the long-range dependencies of CNNs and has lower complexity compared to Diffusion and Transformer models. So in this work, we propose a novel Wavelet-based Mamba with Fourier Adjustment model called WalMaFa, consisting of a Wavelet-based Mamba Block (WMB) and a Fast Fourier Adjustment Block (FFAB). We employ an Encoder-Latent-Decoder structure to accomplish the end-to-end transformation. Specifically, WMB is adopted in the Encoder and Decoder to enhance global brightness while FFAB is adopted in the Latent to fine-tune local texture details and alleviate ambiguity. Extensive experiments demonstrate that our proposed WalMaFa achieves state-of-the-art performance with fewer computational resources and faster speed. Code is now available at: https://***/mcpaulgeorge/WalMaFa. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image enhancement

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Real-Time Multi Fractal Trust Evaluation Model for Efficient Intrusion Detection in Cloud

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Intelligent Automation & Soft Computing 2023年第8期37卷 1895-1907页

作者： S.Priya R.S.Ponmagal Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyChengapattu603203Tamil NaduIndia Department of Computing Technologies School of ComputingSRM Institute of Science and TechnologyChengapattu603203Tamil NaduIndia

Handling service access in a cloud environment has been identified as a critical challenge in the modern internet world due to the increased rate of intrusion *** address such threats towards cloud services,numerous techniques exist that mitigate the service threats according to different *** rule-based approaches are unsuitable for new threats,whereas trust-based systems estimate trust value based on behavior,flow,and other ***,the methods suffer from mitigating intrusion attacks at a higher *** article presents a novel Multi Fractal Trust Evaluation Model(MFTEM)to overcome these *** method involves analyzing service growth,network growth,and quality of service *** process estimates the user’s trust in various ways and the support of the user in achieving higher service performance by calculating Trusted Service Support(TSS).Also,the user’s trust in supporting network stream by computing Trusted Network Support(TNS).Similarly,the user’s trust in achieving higher throughput is analyzed by computing Trusted QoS Support(TQS).Using all these measures,the method adds the Trust User Score(TUS)value to decide on the clearance of user *** proposed MFTEM model improves intrusion detection accuracy with higher performance.

关键词： Intrusion detection cloud systems trusted service support trusted network support trust user score trusted QoS support

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Image Denoising Framework Employing Auto Encoders for Image Reconstruction 1

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Jain, Shruti Bharti, Monika Jindal, Himanshu Department of Electronics and Communications Engineering Jaypee University of Information Technology Himachal Pradesh Waknaghat Solan173234 India Department of Computer Science Engineering ASET Amity University Punjab Punjab Mohali140603 India

ISBN: (数字)9789819986460

ISBN: (纸本)9789819986453

Auto Encoder (AE) can be used in denoising of images. It is a type of neural network that can reconstruct the input. An auto-encoder is to represent a (sparse) input dataset in a compressed form that retains the most relevant information such that it may be reconstructed at the output with minimal loss from the compressed representation. In this paper, deep AE, denoising AE, and variational AE are used. Any AE where an extra constraint is put on the bottleneck to have a low KL divergence from a Normal Distribution is a Variational AE. There are multiple ways in which Variational AE is used, but the most common one is generative. The decoders on the top of the bottleneck can be used to generate new data points. Maximum accuracy of 88.85% is observed using the Denoising autoencoder while 76.25% and 81.44% are observed for deep and Variational autoencoder, respectively. 8.3% and 14.18% accuracy improvement is observed in Variational and deep AE, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image reconstruction

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TSR: a Location Privacy Preservation Mechanism in Public Transportation Route Planning Service 25th

TSR: a Location Privacy Preservation Mechanism in Public T...

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25th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2024

作者： Wu, Wei Sang, Yingpeng Zhang, Haibo Hong, Shen School of Computer Science and Engineering Sun Yet-sen University Guangzhou China School of Computing University of Otago Dunedin New Zealand School of Engineering and Technology Central Queensland University Brisbane Australia

ISBN: (纸本)9789819642069

Location-based service (LBS) applications are increasingly popular for travelling. The public transit scenario is very common in urban areas, yet there is a lack of effective privacy protection mechanisms to safeguard users’ location privacy when using services from untrusted third-party LBS providers. This paper introduces Transportation Shift-based Routing (TSR), a privacy protection mechanism for public transportation travel scenarios. It allows users to query public transit routes through applications that comply with this mechanism without disclosing any significant details about the location, and fills the gap in privacy protection mechanisms in public transportation scenarios. The fundamental concept is to shift the query’s starting and ending points to nearby stations and repeatedly perform perturbations that satisfy geo-indistinguishability, submitting a collection of perturbed station pairs to the LBS. TSR computes the set of obtained routes to derive an optimal route plan. The best route calculated can maintain service availability and protect user location privacy, with only a minimal difference from the best route obtained by direct query. TSR also allows users to freely choose privacy parameters for different scenarios and strives to ensure service quality, offering high flexibility. We implement the TSR application and conduct experiments with real-world map services. The experimental outcomes indicate that TSR successfully strikes a balance between preserving location privacy and maintaining service usability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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CBAR-UNet: A novel methodology for segmentation of cardiac magnetic resonance images using block attention-based deep residual neural network

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Multimedia Tools and Applications 2024年第37期83卷 85047-85063页

作者： Kumar, Rakesh Gupta, Meenu Agarwal, Aman Nayyar, Anand Department of Computer Science and Engineering Chandigarh University Punjab140413 India Graduate School Faculty of Information Technology Duy Tan University Da Nang550000 Viet Nam

computer-aided Medical Image Segmentation (MIS) plays a leading role in diagnosing diseases automatically. MIS is used extensively in diagnosing medical ailments to obtain clinically relevant information of the shapes and volumes of the target organs and tissues. In order to facilitate accurate segmentation, a Block Attention Based Deep Residual Neural Network, i.e., CBAR-UNet model, is proposed to perform cardiac image segmentation on short axis Magnetic Resonance Images (MRI) stacks from top to bottom slice. The Automated Cardiac Diagnosis Challenge (ACDC) dataset is used in the proposed work, which comprise of a 3D MRI of 100 patients (i.e., 200 MRI stacks). Further, 3D MRIs are converted into 2D by slicing each image to train the model, and Contrast Limited Adaptive Histogram Equalization (CLAHE) is performed on the 2D dataset. Residual connections are introduced that aid in training deeper models. Convolutional Block Attention Module (CBAM) is also added in the proposed network, which consists of Spatial and Channel attention that enhances the spatial and channel-wise features in the image by providing information like ‘what’ and ‘where’ to pay attention respectively. To test and validate the proposed methodology, extensive experimentations were conducted and it was observed that proposed image segmentation model delivers better results by 2.47% when compared to both 2D and 3D State-Of-The-Art (SOTA) methods with a Dice Score of 0.9428. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Image segmentation

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