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Specificity-preserving RGB-D saliency detection

Computational Visual Media 2023年第2期9卷 297-317页

作者： Tao Zhou Deng-Ping Fan Geng Chen Yi Zhou Huazhu Fu School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Key Laboratory of System Control and Information Processing Ministry of EducationShanghaiChina Computer Vision Lab ETH Z¨urichZ¨urichSwitzerland School of Computer Science and Engineering Northwestern Polytechnical UniversityXi’anChina School of Computer Science and Engineering Southeast UniversityNanjingChina Inception Institute of Artificial Intelligence Abu DhabiUnited Arab Emirates

Salient object detection(SOD)in RGB and depth images has attracted increasing research *** RGB-D SOD models usually adopt fusion strategies to learn a shared representation from RGB and depth modalities,while few methods explicitly consider how to preserve modality-specific *** this study,we propose a novel framework,the specificity-preserving network(SPNet),which improves SOD performance by exploring both the shared information and modality-specific ***,we use two modality-specific networks and a shared learning network to generate individual and shared saliency prediction *** effectively fuse cross-modal features in the shared learning network,we propose a cross-enhanced integration module(CIM)and propagate the fused feature to the next layer to integrate cross-level ***,to capture rich complementary multi-modal information to boost SOD performance,we use a multi-modal feature aggregation(MFA)module to integrate the modalityspecific features from each individual decoder into the shared *** using skip connections between encoder and decoder layers,hierarchical features can be fully *** experiments demonstrate that our SPNet outperforms cutting-edge approaches on six popular RGB-D SOD and three camouflaged object detection *** project is publicly available at https://***/taozh2017/SPNet.

关键词： salient object detection(SOD) RGB-D cross-enhanced integration module(CIM) multi-modal feature aggregation(MFA)

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Identity – Attribute Inference in Online Social Network(s) Using Bio-Inspired Algorithms and Machine Learning Approaches

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Journal of Mobile Multimedia 2023年第3期19卷 645-678页

作者： Shetty, Nisha P. Muniyal, Balachandra Teja, Daita Ravi Maben, Leander Melroy Vinil, Tummala Srinag Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal 567104 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal 567104 India

Twitter is one of the most popular social networking sites today, and it has become a critical tool for gathering data from numerous individuals throughout the world. The platform hosts a variety of debates spanning from current events and news to entertainment, advertising, and technology. In contrast to earlier approaches, the proposed work employs the concept of both direct (via tweets) and indirect stance detection (via homophily elements) to infer sensitive attributes. Along with attribute-based inference, the proposed work also matches user profiles across cross platforms via user-generated posts. Unlike prior efforts, usernames are not included in the feature set here since they are a bit of a giveaway. Bio-inspired algorithms are used along with ensemble methods to extract the best set of features. © 2023 River Publishers.

关键词： attribute inference bio-inspired algorithms ensemble homophily Identity inference

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Fake review detection using transformer-based enhanced LSTM and RoBERTa

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International Journal of Cognitive Computing in engineering 2024年第1期5卷 250-258页

作者： Mohawesh, Rami Bany Salameh, Haythem Jararweh, Yaser Alkhalaileh, Mohannad Maqsood, Sumbal College of Engineering Al Ain University Al Ain United Arab Emirates Hijjawi Faculty of Engineering Technology Yarmouk University Irbid Jordan Department of Computer Science Jordan University of Science and Technology Jordan College of Education Al Ain University Al Ain United Arab Emirates School of Information Technology University of Tasmania TAS Australia

Internet reviews significantly influence consumer purchase decisions across all types of goods and services. However, fake reviews can mislead both customers and businesses. Many machine learning (ML) techniques have been proposed to detect fake reviews, but they often suffer from poor accuracy due to their focus on linguistic features rather than semantic content. This paper presents a novel semantic- and linguistic-aware model for fake review detection that improves accuracy by leveraging advanced transformer architecture. Our model integrates RoBERTa with an LSTM layer, enabling it to capture intricate patterns within fake reviews. Unlike previous methods, our approach enhances the robustness of fake review detection and authentic behavior profiling. Experimental results on semi-real benchmark datasets show that our model significantly outperforms state-of-the-art methods, achieving 96.03 % accuracy on the OpSpam dataset and 93.15 % on the Deception dataset. To further enhance transparency and credibility, we utilize Shapley Additive Explanations (SHAP) and attention techniques to clarify our model's classifications. The empirical findings indicate that our proposed model can offer rational explanations for classifying specific reviews as fake. © 2024

关键词： Fake detection

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A Bluetooth Low Energy Indoor Positioning Algorithm for an Online to Offline Commerce System

A Bluetooth Low Energy Indoor Positioning Algorithm for an O...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Chang, Ing-Chau Yen, Chin-En Sheu, Yung-Hoh National Changhua University of Education Department of Computer Science and Information Engineering Changhua City Taiwan Chaoyang University of Technology Department of Early Childhood Development and Education Taichung Taiwan National Formosa University Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

By adopting the Online to Offline (O2O) commerce strategy, this paper designs and implements the Bluetooth Low Energy (BLE) indoor positioning technique for an Android APP to let the customer browse online and promote the offline shopping conversely. This APP supports Bluetooth indoor positioning, multi-floor route planning, push message notification and various feedback mechanisms. Hence, it is able to locate current position of the customer in the department store and navigate the customer to each retailer for buying the commodities ordered online through the shortest path. © 2023 IEEE.

关键词： Bluetooth

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A Comprehensive Approach to Groundwater Quality Prediction and Management in the State of Telangana, India, Using Stacking Ensemble Approach 4

A Comprehensive Approach to Groundwater Quality Prediction a...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Senapati, Chinmayee Patra, Kumar Janardan Swain, Satyaprakash Jayasingh, Suvendra Kumar Senapati, Swagatika Ray, Niranjan Kumar Siksha 'o' Anusandhan Deemed to Be University Institute of Technical Education and Research Department of Civil Engineering Bhubaneswar India Odisha University of Technology and Research Department of Computer Science and Engineering Bhubaneswar India Institute of Management of Information Technology Cuttack Bput Department of Computer Science and Engineering Odisha India Institute of Management of Information Technology Cuttack Bput Department of Computer Science and Engineering Odisha India Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350349856

Groundwater, a vital resource for various purposes, faces increasing threats to its quality due to factors like overuse, pollution and climate change. In the semi-arid climate of the state of Telangana, India, exacerbated by intensive agricultural practices, groundwater quality is a significant concern. This study presents a machine learning-based approach for predicting and managing groundwater quality in Telangana, utilizing a stacking ensemble method. Our proposed approach combines decision trees (DT) with meta-models such as logistic regression, K-nearest neighbors (K-NN), Bayesian ridge regression (BRR) and random forest (RF). Trained on a comprehensive dataset from the Telangana Open Data portal (2018-2021), the stacking ensemble demonstrates superior performance, achieving an accuracy of 96.07%, surpassing the best individual model (DT) at 92.68%. It not only enhances predictive capabilities but also provides insights into the spatial distribution of groundwater quality, identifying areas vulnerable to contamination. These findings offer valuable information for policymakers, environmental scientists and water resource managers, guiding informed decision-making for sustainable water resource management in Telangana. This research contributes to the ongoing discourse on environmental conservation and the imperative for effective groundwater management. © 2024 IEEE.

关键词： Decision trees

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A KNN Algorithm Based Predictive Model for Heart Disease Progression

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Recent Advances in computer Science and Communications 2023年第5期16卷 76-81页

作者： Jaffrin, Lijetha C. Srikanth, G. Umarani Department of Information Technology Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai India

Background: Disease diagnosis is a useful phenomenon in healthcare. Machine learning classification methods would considerably improve the healthcare industry by providing a quick diagnosis of the disease. Thus, time could be saved for doctors. Nearly 17.9 million people expire due to heart disease every year. Objectives: World Health Organization (WHO) predicted that rate of death might increase by 24.5 million in 2030. Since heart illness was the major cause of death in comparison with other diseases today, it was the most challenging disease to diagnose. Methods: One of the reasons for death due to heart disease was due to the fact that risks were not identified in the earlier stage. Earlier diagnosis of disease was very much important. Machine Learning algorithms were used for predicting the prognosis of disease. Results: Here K-NN algorithm was used to predict the presence of heart disease in an individual. Thus, patients were classified as either positive or negative for heart disease and this model enhanced medical care and reduced the cost. This gave us significant knowledge that helps us to predict the patients with heart disease. Conclusion: The Python sci-kit library was used to implement this in Anaconda Navigator's Spyder Integrated Development Environment. Experiments revealed that technique worked well and was more accurate than before. © 2023 Bentham Science Publishers.

关键词： Diseases

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An energy-efficient resource allocation strategy in massive MIMO-enabled vehicular edge computing networks

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High-Confidence Computing 2023年第3期3卷 40-49页

作者： Yibin Xie Lei Shi Zhenchun Wei Juan Xu Yang Zhang School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of EducationHefei 230009China Hefei Origin IoT Technology Co. LtdHefei 230088China

The vehicular edge computing(VEC)is a new paradigm that allows vehicles to offload computational tasks to base stations(BSs)with edge servers for *** general,the VEC paradigm uses the 5G for wireless communications,where the massive multi-input multi-output(MIMO)technique will be ***,considering in the VEC environment with many vehicles,the energy consumption of BS may be very *** this paper,we study the energy optimization problem for the massive MIMO-based VEC *** at reducing the relevant BS energy consumption,we first propose a joint optimization problem of computation resource allocation,beam allocation and vehicle grouping *** the original problem is hard to be solved directly,we try to split the original problem into two subproblems and then design a heuristic algorithm to solve *** results show that our proposed algorithm efficiently reduces the BS energy consumption compared to other schemes.

关键词： Vehicular edge computing Massive MIMO Resource allocation Energy-efficient

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Detection of Perfect Stealthy Attacks on Cyber-Physical Systems Subject to Measurement Quantizations: A Watermark-Based Strategy

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 114-125页

作者： Yu-Ang Wang Zidong Wang Lei Zou Bo Shen Hongli Dong the College of Information Science and Technology Donghua University the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of Education IEEE the College of Electrical Engineering and Automation Shandong University of Science and Technology the Department of Computer Science Brunel University London Uxbridge the Sanya Offshore Oil and Gas Research Institute Northeast Petroleum University the Artificial Intelligence Energy Research Institute Northeast Petroleum University Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast Petroleum University

In this paper, the attack detection problem is investigated for a class of closed-loop systems subjected to unknownbutbounded noises in the presence of stealthy attacks. The measurement outputs from the sensors are quantized before transmission.A specific type of perfect stealthy attack, which meets certain rather stringent conditions, is taken into account. Such attacks could be injected by adversaries into both the sensor-toestimator and controller-to-actuator channels, with the aim of disrupting the normal data flow. For the purpose of defending against these perfect stealthy attacks, a novel scheme based on watermarks is developed. This scheme includes the injection of watermarks(applied to data prior to quantization) and the recovery of data(implemented before the data reaches the estimator).The watermark-based scheme is designed to be both timevarying and hidden from adversaries through incorporating a time-varying and bounded watermark signal. Subsequently, a watermark-based attack detection strategy is proposed which thoroughly considers the characteristics of perfect stealthy attacks,thereby ensuring that an alarm is activated upon the occurrence of such attacks. An example is provided to demonstrate the efficacy of the proposed mechanism for detecting attacks.

关键词： Attack detection cyber-physical systems (CPSs) perfect stealthy attacks watermark-based strategy

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EACR-LEACH:Energy-Aware Cluster-based Routing Protocol for WSN Based IoT

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computers, Materials & Continua 2022年第8期72卷 2159-2174页

作者： Sankar Sennan Kirubasri Youseef Alotaibi Digvijay Pandey Saleh Alghamdi Department of Computer Science and Engineering Sona College of TechnologySalem636005India Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Technical Education Department of Electronics EngineeringInstitute of Engineering and TechnologyDr.A.P.J.Abdul Kalam Technical UniversityLucknowIndia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Internet of Things(IoT)is a recent paradigm to improve human ***,number devices are connected to the Internet ***,the people can control and monitor the physical things in real-time without *** IoT plays a vital role in all kind of fields in our world such as agriculture,livestock,transport,and healthcare,grid system,connected home,elderly people carrying system,cypher physical system,retail,and intelligent *** IoT energy conservation is a challenging task,as the devices are made up of low-cost and low-power sensing devices and local *** networks have significant challenges in two areas:network lifespan and energy ***,the clustering is a right choice to prolong the energy in the *** LEACH clustering protocol,sometimes the same node acts as CH again and again *** overcome these issues,this paper proposes the Energy-Aware Cluster-based Routing(EACRLEACH)protocol in WSN based *** Cluster Head(CH)selection is a crucial task in clustering protocol inWSN based *** EACR-LEACH,the CH is selected by using the routing metrics,Residual Energy(RER),Number of Neighbors(NoN),Distance between Sensor Node and Sink(Distance)and Number of Time Node Act as CH(NTNACH).An extensive simulation is conducted on MATLAB *** accomplishment of EACR-LEACH is compared to LEACH and *** proposed EACR-LEACH protocol extends the network’s lifetime by 4%-8%and boosts throughput by 16%–24%.

关键词： Cluster head clustering protocol internet of things routing metrics wireless sensor networks

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A Novel Convolutional Neural Networks Based Spinach Classification and Recognition System

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computers, Materials & Continua 2022年第10期73卷 343-361页

作者： Sankar Sennan Digvijay Pandey Youseef Alotaibi Saleh Alghamdi Department of Computer Science and Engineering Sona College of TechnologySalem636005India Department of Technical Education Department of Electronics EngineeringInstitute of Engineering and TechnologyDr.A.P.J.Abdul Kalam Technical UniversityLucknowIndia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

In the present scenario,Deep Learning(DL)is one of the most popular research algorithms to increase the accuracy of data *** to intra-class differences and inter-class variation,image classification is one of the most difficult jobs in image *** or spinach recognition or classification is one of the deep learning applications through its *** is more critical for human skin,bone,and hair,*** provides vitamins,iron,minerals,and *** is beneficial for diet and is readily available in people’s *** researchers have proposed various machine learning and deep learning algorithms to classify plant images more accurately in recent *** paper presents a novel Convolutional Neural Network(CNN)to recognize spinach more *** proposed CNN architecture classifies the spinach category,namely Amaranth leaves,Black nightshade,Curry leaves,and Drumstick *** dataset contains 400 images with four classes,and each type has 100 *** images were captured from the agricultural land located at Thirumanur,Salem district,Tamil *** proposed CNN achieves 97.5%classification *** addition,the performance of the proposed CNN is compared with Support Vector Machine(SVM),Random Forest,Visual Geometry Group 16(VGG16),Visual Geometry Group 19(VGG19)and Residual Network 50(ResNet50).The proposed provides superior performance than other models,namely SVM,Random Forest,VGG16,VGG19 and ResNet50.

关键词： Accuracy convolutional deep learning plant neural networks spinach

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