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Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

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Tsinghua science and technology 2024年第4期29卷 985-1001页

作者： N.Meenakshi Sultan Ahmad A.V.Prabu J.Nageswara Rao Nashwan Adnan Othman Hikmat A.M.Abdeljaber R.Sekar Jabeen Nazeer SRM Institute of Science and Technology KattankulathurChennai 603203India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj 11942Saudi Arabia University Center for Research and Development(UCRD) Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali 140413India Department of ECE Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522502India Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering(A)NTR District 521230India Department of Computer Engineering College of EngineeringKnowledge UniversityErbil 44001Iraq Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmman 11937Jordan Department of Electronics and Communication Engineering(ECE) School of EngineeringPresidency UniversityBangalore 834001India

The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human *** widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military *** make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the *** data have to be picked up by the sensor,and then sent to the sink node where they may be *** nodes of the WSNs are powered by batteries,therefore they eventually run out of *** energy restriction has an effect on the network life span and environmental *** objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of *** lifespan of WSNs is being extended often using clustering and routing *** Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do *** cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node *** on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS *** overall experimentation is carried out under the MATLAB *** the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.

关键词： wireless sensor networks energy efficient engroove leach protocol meta inspired Hawks fragment optimization heuristic wing antfly optimization

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Enhancing Skin Disease Diagnosis: Light GBM-DMS Algorithm for Accurate Image Classification

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Cognitive Computation 2025年第3期17卷 1-18页

作者： Kalpana, B. Senthilselvi, A. Muruganandam, S. Kumar, S. Vinodh Department of Information Technology RMD Engineering College Tamil Nadu Kavaraipettai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai India Department of Computer Science and Business Systems Panimalar Engineering College Poonamallee Chennai Tamil Nadu India Department of Computer Science and Engineering Rajalakshmi Engineering College Tamil Nadu Thandalam Chennai India

Globally, skin diseases are emerging as the most common health problem. It initiates depressive disorder, and it also causes physical health distress. It rarely led to skin cancer in extreme cases. Diagnosing skin disease is subjective and time-consuming when it is performed by health experts manually. Hence, automatic skin disease prediction is required by both dermatologists and patients to enhance and accelerate treatment plans. Even though many machine-learning techniques are available to detect skin diseases, the timely prediction and accurate classification of skin lesions are still in demand. Hence, the Discrete Mycorrhizal Search Algorithm is used to optimize the novel LightGBM to meet this objective. Firstly, intrusions like noise elimination and hair removal are performed and then the image resizing takes place by utilizing the preprocessing pipelines. Subsequent to data pre-processing, the prime features are extracted using the convolutional self-attention based L2 regularization, thus minimizing the data complexity. At last, the extracted features are shifted to the final categorization phase which is done by the novel approach for effective classification of skin diseases. Two skin disease classification images, including datasets, the Skin Lesions Dermatoscopic Image dataset and Skin Cancer MNIST HAM10000, are selected to validate the classification performance. Comprehensive analyses are performed with significant measures that showed the proposed model attained better performances of 98.6%, 0.95, and 10.43 s from accuracy, intersection of union, and execution time, respectively. The stability and robustness under different environmental conditions for clinical applications in skin disease detection and classification tasks are analyzed for making decisions in real-time scenarios. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Dermatitis

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Brain haemorrhage classification from CT scan images using fine-Tuned transfer learning deep features

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International Journal of Business Intelligence and Data Mining 2024年第2期24卷 111-130页

作者： Ghosh, Arpita Soni, Badal Baruah, Ujwala Department of Computer Science and Engineering National Institute of Technology Assam Silchar India

Classification of brain haemorrhage is a challenging task that needs to be solved to help advance medical treatment. Recently, it has been observed that efficient deep learning architectures have been developed to detect such bleeding accurately. The proposed system includes two different transfer learning strategies to train and fine-Tune ImageNet pre-Trained state-of-The-Art architecture such as that VGG 16, Inception V3 and DenseNet121. The evaluation metrics have been calculated based on the performance analysis of the employed networks. Experimental results show that the modified fine-Tuned Inception V3 performed well and achieved the highest test accuracy. © 2024 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Image classification

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Hierarchical Patch Selection: An Improved Patch Sampling for No Reference Image Quality Assessment

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 541-555页

作者： Nandhini, C. Brindha, M. National Institute of Technology Department of Computer Science and Engineering Tiruchirappalli620015 India

Quality degradation due to the compression and the transmission of images is a significant threat to multimedia applications. Blind image quality assessment (BIQA) is a principal technique to measure the distortion and dynamically set the optimal parameters for developing image compression standards, image restoration algorithms, etc. Insufficient training data with quality scores are a challenge for image quality assessment (IQA) tasks. The existing solutions to deep-convolution-neural-network-based BIQA, which rely on patchwise training, struggle to find an ideal set of patches consistent with the human visual system. To address these issues, HIerarchical paTch Selection (HITS) is proposed. HITS keeps the patches with considerable details in each quadrant according to their intensity variance scatter ratio (IVSR). IVSR identifies the best nonhomogeneous patches by computing patch intensity variance. Extensive trials are conducted, and the performance reveals that the proposed approach achieves excellent performance on synthetic and authentic distortion datasets with less memory and processing power. Moreover, the proposed approach outperforms other BIQA methods that adapt patchwise training strategies. © 2023 IEEE.

关键词： Feature extraction

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Cross-modal heterogeneous graph reasoning network for visual question answering

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Neural Computing and Applications 2025年 1-21页

作者： Zhang, Jing Teng, Jiong Ding, Weichao Wang, Zhe Department of Computer Science and Engineering East China University of Science and Technology Shanghai China

Most current Visual Question Answering (VQA) methods struggle to achieve effective cross-modal interaction between visual and semantic information, resulting in difficulties in accurately combining visual content with contextual semantics for answer prediction. To address this problem, a Cross-modal Heterogeneous Graph Reasoning Network (CHGRN) is proposed for VQA, incorporating a novel Cross-modal Reasoning Module (CRM) to enhance the interactive analysis between images and questions, enabling more profound joint reasoning of visual and semantic features. The CRM improves cross-modal information reasoning by effectively analyzing and understanding the visual semantic information in the target regions of images. Additionally, an answer type prediction module is introduced, employing multi-task learning with answer type annotations to filter out irrelevant semantic information, thereby improving reasoning accuracy. Moreover, the semantic-assisted attention-aligned decoder ensures precise alignment between visual and semantic data. Extensive experiments demonstrate that the proposed CHGRN achieves excellent performance in visual question answering and outperforms most state-of-the-art methods on widely used public datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Multi-task learning

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Multi-scale Vision Transformer toward improved non-invasive anaemia detection using palm video

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Multimedia Tools and Applications 2024年第38期83卷 85825-85848页

作者： Kesarwani, Abhishek Das, Sunanda Kisku, Dakshina Ranjan Dalui, Mamata Department of Computer Science and Engineering National Institute of Technology Durgapur Durgapur India

Anaemia, a condition characterised by reduced haemoglobin levels, exerts a significant global impact, affecting billions of individuals worldwide. According to data from the World Health Organisation (WHO), India exhibits notably high anaemia prevalence in comparison to other developing nations. The urgency arises for the development of non-invasive, cost-effective, and accurate anaemia screening technologies, primarily due to the substantial costs and logistical complexities associated with invasive screening methods, which impede their broad adoption on a global scale. The current endeavor aims to establish a robust anaemia detection system through the integration of cutting-edge computational methodologies with the traditional yet less precise approach of assessing haemoglobin levels based on observation of palm pallor. The proposed method employs time-domain analysis to establish a correlation between blood haemoglobin concentration and the observed changes in palm colour resulting from the application and release of pressure. This involves employing a custom device to capture and record the complete sequence of palm colour changes using a smartphone camera sensor, followed by data processing and analysis. The Multi-scale Vision Transformer is then employed to extract features from dominant frames. The approach further incorporates the application of a multi-layer perceptron (MLP) network as a regression model based on the reduced feature outcomes. The proposed system accurately estimates blood haemoglobin levels with RMSE, sensitivity, specificity, and accuracy of 0.509, 96.88%, 86.39%, and 92.59%, respectively based on 531 video samples of palm evidence. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Diagnosis

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MCFusion: Frequency Domain Characteristics Enhancement and Feature Compensation Fusion Network for RGB-T Object Detection

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IEEE Sensors Journal 2025年第11期25卷 20880-20893页

作者： Gao, Yinbo Liao, Zhuhua Liu, Yizhi Yi, Aiping Zhang, Guoqiang Hunan University of Science and Technology China Hunan University of Science and Technology Department of Computer Science and Engineering Xiangtan China Hainan Normal University Department of Information Science and Technology Haikou China

The multi-modal object detection technology based on visible-thermal vision sensors has drawn significant attention as it is capable of achieving reliable object detection in complex scenes with challenging lighting conditions such as low light or backlight. However, there has been a lack of focus on the frequency domain feature information of the visible and thermal modalities themselves, as well as the complementarity of cross-modal features. Furthermore, current visible and thermal feature fusion methods only utilize feature information from the current layer, neglecting context information. Therefore, this paper proposes a novel network framework for enhancing frequency domain Characteristics and fusing cross-layer and cross-modal features to compensate for these limitations. This framework introduces two key modules: the Frequency Domain Characteristics Enhancement (FCE) module and the Cross-Layer and Cross-Modal Feature Compensation Fusion (CCF) module. The FCE module consists of two sub-modules. Reduce high-frequency information loss(FCE-RHFL) module operates on the visible modality to reduce high-frequency information loss, utilizing methods such as high-frequency masking and frequency recombination. Meanwhile, Enhance high-frequency information representation(FCE-EHFR) module enhances high-frequency information representation for the thermal modality through convolutions with different kernels and frequency domain enhancement techniques. In the CCF module, cross-modal and cross-layer feature compensation methods are employed to compensate for differences in modalities, followed by capture complementary information across modalities using a query-guided cross-attention mechanism. Finally, we conduct experimental comparisons on the KAIST and FLIR datasets, and the experimental results demonstrate that our method has excellent performance and robust detection results. © 2001-2012 IEEE.

关键词： Frequency domain analysis

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A Local Differential Privacy Trajectory Protection Method Based on Temporal and Spatial Restrictions for Staying Detection

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Tsinghua science and technology 2024年第2期29卷 617-633页

作者： Weiqi Zhang Zhenzhen Xie Akshita Maradapu Vera Venkata Sai Qasim Zia Zaobo He Guisheng Yin College of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China School of Computer Science and Technology Shandong UniversityQingdao 266237China Department of Computer Science Georgia State UniversityAtlantaGA 30303USA College of Information Science and Technology Jinan UniversityGuangzhou 510632China

The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based ***-based social networks have become very popular as they provide end users like us with several such services utilizing GPS through our ***,when users utilize these services,they inevitably expose personal information such as their ID and sensitive location to the *** to untrustworthy servers and malicious attackers with colossal background knowledge,users'personal information is at risk on these ***,many privacy-preserving solutions for protecting trajectories have significantly decreased utility after *** have come up with a new trajectory privacy protection solution that contraposes the area of interest for ***,Staying Points Detection Method based on Temporal-Spatial Restrictions(SPDM-TSR)is an interest area mining method based on temporal-spatial restrictions,which can clearly distinguish between staying and moving ***,our privacy protection mechanism focuses on the user's areas of interest rather than the entire ***,our proposed mechanism does not rely on third-party service providers and the attackers'background knowledge *** test our models on real datasets,and the results indicate that our proposed algorithm can provide a high standard privacy guarantee as well as data availability.

关键词： local-differential privacy stay points detection trajectory data areas of interest temporal-spatial clustering

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THE IMPACT OF USING AI AND CHATGPT IN ORGANIZATIONS

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2025年第1期87卷 53-64页

作者： Tudose, Cătălin Boiangiu, Costin Anton Antal, Jasmyna Cristina Bănăţeanu, Cristiana Breazu, Laura Piele, Mihai Computer Science and Engineering Department National University of Science and Technology POLITEHNICA Bucharest Romania

This article investigates the impact of Artificial Intelligence (AI) and ChatGPT in the business sector. It highlights the evolution of AI, focusing on the integration and applications of technologies like machine learning, natural language processing, and ChatGPT in various business areas. A central aspect of the study is a survey that reflects professionals' views on AI's effectiveness, data security concerns, and its influence on workplace dynamics across multiple industries. The research reveals both the benefits, such as improved efficiency and innovation, and challenges, including data security risks and potential overreliance on technology. Concluding, the article emphasizes the need for a balanced approach to AI adoption in businesses, advocating for a combination of technological and human expertise for optimal results. This study provides key insights for companies looking to leverage AI for sustainable growth and competitive advantage. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Adversarial machine learning

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TRE-DSP:A traceable and revocable CP-ABE based data sharing scheme for IoV with partially hidden policy

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Digital Communications and Networks 2025年第2期11卷 455-464页

作者： Yousheng Zhou Rundong Peng Yuanni Liu Pandi Vijayakumar Brij Gupta College of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065China College of Cyber Security and Information Law Chongqing University of Posts and TelecommunicationsChongqing 400065China Department of Computer Science and Engineering University College of EngineeringTindivanamMelpakkamTamil nadu 604001India Department of Computer Engineering National Institute of Technology KurukshetraIndia

With the popularity of the Internet of Vehicles(IoV),a large amount of data is being generated every *** to securely share data between the IoV operator and various value-added service providers becomes one of the critical *** to its flexible and efficient fine-grained access control feature,Ciphertext-Policy Attribute-Based Encryption(CP-ABE)is suitable for data sharing in ***,there are many flaws in most existing CP-ABE schemes,such as attribute privacy leakage and key *** paper proposes a Traceable and Revocable CP-ABE-based Data Sharing with Partially hidden policy for IoV(TRE-DSP).A partially hidden access structure is adopted to hide sensitive user attribute values,and attribute categories are sent along with the ciphertext to effectively avoid privacy *** addition,key tracking and malicious user revocation are introduced with broadcast encryption to prevent key *** the main computation task is outsourced to the cloud,the burden of the user side is relatively *** of security and performance demonstrates that TRE-DSP is more secure and practical for data sharing in IoV.

关键词： Privacy-preserving Attribute-based encryption Partially hidden policy Traceability User revocation Internet of vehicles

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