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computer Science and Blockchain (CCSB), International Conference on

作者： Yaoyao Zhang Zhongyuan Yao Heng Pan Xueming Si Henan International Joint Laboratory of Blockchain and Data Sharing Zhengzhou Henan China Henan Key Laboratory of Network Cryptography Technology Zhengzhou Henan China The Frontier Information Technology Research Institute Zhongyuan University of Technology China Department of Computer Science and Engineer SHANGHAI Jiao Tong University Shanghai China

During the storing, transferring, processing, and exchanging phases of the full life-cycle of secure image sharing, there may exist security issues such as information leakage, infringement and abuse. To address those problems, a life-cycle security management mechanism is proposed. By utilizing the Commutative Encryption and Watermarking (CEW), the mechanism ensures that the ownership and confidentiality of one image can hardly be tampered with and compromised. Besides, with the help of the blockchain, the mechanism is enabled to record any changes during the whole process of the image sharing, which makes copyright traceability possible and thus reduces the occurrence of infringement and abuse. The experiment result shows that, in our mechanism, the quality of plaintext image with watermark obtained by decryption is high, and the invisibility of watermark is preserved. Also, the efficient analysis and experimenting of the mechanism exhibits that it is feasible in practice.

关键词： computer science Security management Watermarking Blockchains Encryption Recording

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A Fair and Reliable Data Trading Scheme based on Blockchain

A Fair and Reliable Data Trading Scheme based on Blockchain

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computer Science and Blockchain (CCSB), International Conference on

作者： Zhili Wu Heng Pan Xueming Si Haiyang Qian Fugang Cai Henan International Joint Laboratory of Blockchain and Data Sharing Zhengzhou Henan China Henan Key Laboratory of Network Cryptography Technology Zhengzhou Henan China The Frontier Information Technology Research Institute Zhongyuan University of Technology China Department of Computer Science and Engineer SHANGHAI Jiao Tong University Shanghai China

To solve the single point of failure and opaque transaction problems in the current centralized data trading model, we propose a blockchain-based data trading scheme. Firstly, we construct a blockchain-based data transaction model to provide users with peer-to-peer trusted transaction services. Then, we design a two-stage one-to-many data pricing method based on smart contracts and the Rubinstein model, which can guarantee the reasonableness of data transaction pricing and achieve a fair price between a single data demander and multiple data owners. Next, we suggest a forensic dispute arbitration method based on on-chain and off-chain data collaboration and a data integrity verification method, which aims to ensure the reliability of de-centralized data transactions and prevent malicious acts such as data owners providing false data, data requesters refusing to pay, and data stored in the cloud being tampered with. Finally, we perform experiments based on consortium blockchain. Experimental results show that the scheme of this paper has better functionality and performance than other schemes of data transactions.

关键词： Forensics Data integrity Smart contracts Collaboration Pricing Data models Blockchains

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Incentive Scheme for Shared Parking Space based on NFT

Incentive Scheme for Shared Parking Space based on NFT

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computer Science and Blockchain (CCSB), International Conference on

作者： Fugang Cai Heng Pan Xueming Si Zhili Wu Haiyang Qian Henan International Joint Laboratory of Blockchain and Data Sharing Zhengzhou Henan China Henan Key Laboratory of Network Cryptography Technology Zhengzhou Henan China The Frontier Information Technology Research Institute Zhongyuan University of Technology China Department of Computer Science and Engineer SHANGHAI Jiao Tong University Shanghai China

The lack of centralized management causes problems in the system of distributed parking spaces, such as unsecured revenues, poor service quality and malicious valuation. Therefore, this paper proposes a parking sharing scheme based on non-fungible tokens. Firstly, we propose the calculation method of parking space renter reputation value, parking space provider reputation value and the reputation incentive algorithm of parking space sharing, which can ensure the service quality of distributed parking space share and prevent malicious evaluation and implement bidirectional constraints on parking space provider and renter. Secondly, we propose a parking space sharing model with non-fungible tokens as sharing credentials to map parking space assets to on-chain assets via non-fungible tokens and introduce timers based on hash time locks. We propose an intelligent parking rental contract based on improved hash lock to guarantee the revenue of parking space providers in a distributed environment. Finally, experiments based on Ethereum show that the scheme can realize shared parking space at a lower cost; the proposed incentive algorithm can effectively prevent parking space providers and renters from misbehaving.

关键词： computer science Visualization Costs Smart contracts Behavioral sciences Complexity theory Nonfungible tokens

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Dynamic learning of individual-level suicidal ideation trajectories to enhance mental health care

npj Mental Health Research

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npj Mental Health research 2024年第1期3卷 26页

作者： Varidel, Mathew Hickie, Ian B. Prodan, Ante Skinner, Adam Marchant, Roman Cripps, Sally Oliveria, Rafael Chong, Min K. Scott, Elizabeth Scott, Jan Iorfino, Frank Brain and Mind Centre The University of Sydney Sydney NSW Australia Translational Health Research Institute Western Sydney University Sydney NSW Australia School of Computer Data and Mathematical Sciences Western Sydney University Sydney NSW Australia Human Technology Institute University of Technology Sydney NSW Australia School of Mathematical and Physical Sciences University of Technology Sydney Sydney NSW Australia Data61 CSIRO Sydney NSW Australia Academic Psychiatry Institute of Neuroscience Newcastle University Newcastle United Kingdom

There has recently been an increase in ongoing patient-report routine outcome monitoring for individuals within clinical care, which has corresponded to increased longitudinal information about an individual. However, many models that are aimed at clinical practice have difficulty fully incorporating this information. This is in part due to the difficulty in dealing with the irregularly time-spaced observations that are common in clinical data. Consequently, we built individual-level continuous-time trajectory models of suicidal ideation for a clinical population (N = 585) with data collected via a digital platform. We demonstrate how such models predict an individual’s level and variability of future suicide ideation, with implications for the frequency that individuals may need to be observed. These individual-level predictions provide a more personalised understanding than other predictive methods and have implications for enhanced measurement-based care. © The Author(s) 2024.

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Diabetic Retinopathy Image Classification using Quasi Cross UNet Discrete Transform with Gold Rush Optimizer

Diabetic Retinopathy Image Classification using Quasi Cross ...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： M.Siva Ramkumar Nivetha R Mathi Kannaiyan Mohammad Kanan S. Shankar Jayant Giri Dept of ECE SNS College of Technology Computer Science and Engineering Kumaraguru College of Technology lakshitha department of Mechanical Engineer University college of Kancheepuram Anna University kancheepuram campus Kanchipuram Department of Industrial Engineering College of Engineering University of Business and Technology Jeddah Saudi Arabia Department of Mechanical Engineering College of Engineering Zarqa University Zarqa Jordan Department of Mechanical Engineering Cambridge Institute of Technology Bengaluru India Department of Mechanical Engineering Yeshwantrao Chavan College of Engineering Nagpur India Division of Research and Development Lovely Professional University Phagwara India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The diagnosis of diabetic retinopathy faces three main challenges which include inconsistent image clarity and weak detection of small visual impairments and little ability to share information between different sets of data. The model suffers from two issues which limit its practical use: high expenses for operations as well as low decision accuracy because of complex tissue dynamics. Using the MESSIDOR dataset as input, this paper illustrates the application of the Quasi Cross UNet Discrete Transform with Gold Rush Optimizer (QCUNet-DT-GRO) algorithm for diabetic retinopathy picture categorization. The Unet Swin Transformer (UST) approach is used to precisely segment areas afflicted by diabetic retinopathy, while the Quasi-Cross Bilateral Filtering (QCBF) methodology improves image quality. The One-Dimensional Discrete Transform (ODDT) is used to extract features. The Gold Rush Optimizer (GdRO) does optimization, while the Gates-Controlled Deep Unfolding Network (GCDUNet) handles classification. The system detects diabetic retinopathy along with distinguishing between normal and diabetic retina structures. According to experimental results, QCUNet-DT-GRO performs better than current techniques, with 99.9% accuracy and 99.8% sensitivity. This method is shown to be a viable substitute for the manual diagnostic processes that are now in use, enabling robotic diagnosis systems in subsequent applications.

关键词： Diabetic retinopathy Gold Discrete transforms Accuracy Sensitivity Filtering Logic gates Transformers Feature extraction Robot sensing systems

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Age Detection based on Facial Features Using Support Vector Machine

Age Detection based on Facial Features Using Support Vector ...

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Knowledge engineering and Communication Systems (ICKECS), International Conference on

作者： Jayaprada S. Hiremath Shantala S. Hiremath Sujith Kumar Elukoti Hebbare Shantakumar B. Patil Mrutyunjaya S. Hiremath Computer Science and Engineering Research Scholar at VTU Belgaum India Specialist in Image Processing Tata Elxsi Limited Bangalore India Principal Engineer (Artificial intelligence / Machine learning) VVDN Technologies Pvt. Ltd. Bangalore India Department of Image Processing eMath Technology Pvt. Ltd. Bangalore India Computer Science and Engineering Sai Vidya Institute of Technology Bengaluru India

Face aging has been studied for decades. Determining age from a facial shot is key to our technique for diagnosing abnormal behavior. Security monitoring, forensics, biometrics, and Human-computer Interface (HCI) use facial age estimates. We only look at adults 1–75 in the UTK Face database, which covers 0 to 116 years. The database contains 23,708 face photos with age, gender, and ethnicity annotations. In work, preprocessing, feature extraction, feature selection, and age categorization are involved. Preprocessing adjusts images. computer vision uses Local Binary Pattern (LBP) and Histogram of Oriented Gradient (HOG) as visual descriptors, whereas fscmrmr is utilized for classification. Support Vector Machines (SVM) improve classification accuracy in highdimensional areas. The chosen characteristics are concatenated and passed to a multiclass SVM classifier to classify the images with 95.69% success.

关键词： Support vector machines Training Human computer interaction Visualization Databases Transfer learning Feature extraction

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Time-dependent elastic numerical model for Optical Coherence Elastography of the murine retina

Time-dependent elastic numerical model for Optical Coherence...

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Bioengineering (ENBENG), Portuguese Meeting in

作者： Carlos Correia Ana Batista Sílvia Barbeiro João Cardoso José P. Domingues Rafael Henriques Custódio Loureiro Mário J. Santos Pedro Serranho Rui Bernardes Miguel Morgado Institute for Biomedical Imaging and Translational Research (CIBIT) University of Coimbra Coimbra Portugal Center of Mathematics of the University of Coimbra (CMUC) Coimbra Portugal Department of Physics Faculty of Science and Technology University of Coimbra Coimbra Portugal Department of Electrical and Computer Engineering Faculty of Science and Technology University of Coimbra Coimbra Portugal Department of Science and Technology Mathematics Section Universidade Aberta Lisbon Portugal Faculty of Medicine Clinical Academic Center of Coimbra (CACC) University of Coimbra Portugal

We present the initial stages of development of a Finite Element Method-based time-dependent elastic numerical model which seeks to support the employment of our Optical Coherence Elastography system for assessing murine retinal elasticity. The current model is able to reconstruct displacement maps in both homogeneous and heterogeneous domains with errors up to a few hundredths relatively to a known exact displacement map, within 1 millisecond. The results demonstrate the robustness of the numerical algorithm under different elastic domains, and model parametrization with real Optical Coherence Elastography data is already in progress.

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The Various Techniques in Mmcn: An Overiview

The Various Techniques in Mmcn: An Overiview

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： Manisha Aeri Kadim A. Jabbar Huthaifa Alani Myasar Mundher Adnan Dharmesh Dhabliya Imad Al-Din Hussein Al-Saidi Nabaa Kareem Taaban Department of CSE Graphic Era Hill University Research Scholar Graphic Era Deemed to be University Dehradun Uttarakhand College of Technical Engineering National University of Science and Technology Dhi Qar Iraq Al-Farahidi university Baghdad Iraq Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Vishwakarma Institute of Information Technology Pune India Computer Engineer Department Al-kunooze University College Basra Iraq Business Administration Department Al kut Kut University College wasit Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Molecular communications provide a fresh approach to deciphering complex biological systems. The topic of mobile molecular communication networks is examined in this article, with a focus on calcium signaling within a population of mobile cells. Taking inspiration from conventional communication networks, we investigate multi-modality behavior using a multimedia method. We initially utilize a random walk model to study the stochastic mobility of biological cells and the dynamic features of cellular networks. Next, a mathematical model is proposed to explain how calcium signals arise and propagate throughout mobile networks. We further provide a thematic framework and expand our study to a multimedia context. Using this approach, we study two forms of calcium signaling transmission: waveform transmission and concentration transmission. To further explain our findings, we use simulations to show the patterning of calcium signal waveforms. The results show that the surroundings have an impact on performance. Additionally, the simulation shows performance differences between the two transmission methods, confirming the features that our proposed multimedia theme has.

关键词： Cellular networks Calcium Sociology Molecular communication Stochastic processes Mathematical models Data models

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Gender Detection using Facial Features with Support Vector Machine

Gender Detection using Facial Features with Support Vector M...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International Conference on

作者： Jayaprada S. Hiremath Shantala S. Hiremath Sujith Kumar Manjunath S. Chincholi Shantakumar B. Patil Mrutyunjaya S. Hiremath Computer Science and Engineering Research Scholar at VTU Belgaum Karnataka India Specialist in Image Processing Tata Elxsi Limited Bangalore Karnataka India Principal Engineer (Artificial Intelligence / Machine learning) VVDN Technologies Pvt. Ltd. Bangalore Karnataka India Dept. of Image Processing eMath Technology Pvt. Ltd. Bangalore Karnataka India Computer Science and Engineering Sai Vidya Institute of Technology Bengaluru Karnataka India

ISBN: (纸本)9781665491129

Innovative security technologies have increased the need for accurate identification. Gender identification has been widespread use of image analysis in recent decades. The face is one of the most popular biometric features in image processing. In Image Processing and video surveillance, systems that automatically discern gender from facial photos are gaining prominence. This study discusses face traits and gender categorization. In this study, we created a technique with good runtime and efficiency to determine human gender using face photos. It uses characteristics taken from pre-processed face photographs of various ages. Support Vector Machine (SVM) Classification was used to determine class thresholds. Our method classifies UTK-FACE gender with 91.63% accuracy.

关键词： Support vector machines Wireless communication Training Machine learning algorithms Face recognition Support vector machine classification Feature extraction

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Cashew Apple Nutrients Prediction from Optical Spectroscopic Streaming Data Using Machine Learning-Based Approach

SSRN

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SSRN 2024年

作者： Ishaya, Jeremiah Ayock Adoni, Wilfried Yves Hamilton Zoueu, Jérémie Thouakesseh Assoi, Koko Eliane Mutombo, Franck Kalala Krichen, Moez Abdalzaher, Mohamed S. African Institute for Mathematical Sciences Senegal Academic City University College Accra Ghana Helmholtz Zentrum Dresden Rossendorf Center for Advanced Systems Understanding Görlitz Germany Helmholtz Zentrum Dresden Rossendorf Helmholtz Institute Freiberg for Resource Technology Freiberg Germany University of San Pedro San-Pedro Ivory Coast National Polytechnic Institute - Felix Houphouet Boigny Yamoussoukro Ivory Coast Faculty of Computer Science and Information Technology Al-Baha University Al Baha Saudi Arabia ReDCAD Laboratory National School of Engineers of Sfax Sfax Tunisia University of Lubumbashi Lubumbashi Democratic Republic Congo Department of Seismology National Research Institute of Astronomy and Geophysics Cairo Egypt African Institute for Mathematical Sciences Ghana African Institute for Mathematical Sciences Rwanda

The fleshy portion of the cashew fruit that is joined to the nut is known as the cashew apple. It is an economically important fruit, rich in minerals, vitamins, carbohydrates, organic acids, and antioxidants. The cashew apple is also renowned for its antiscorbutic, pharmacological, and therapeutic characteristics due to its health benefits, and can be consumed fresh or processed into candies, alcohol, and syrup. Due to its economic importance, measuring its quality enables good management and enhances its olfactory, dietary, and sanitary qualities. The main criterion for harvesting the cashew apple is its maturity, and this is usually made known by some specific parameters such as pH, water, sugar, drought measure, and acidity (cashew apple nutrients). However, determining the quality of the cashew apple using these parameters is always tedious, time-consuming, and requires expert knowledge. In this study, we proposed two different approaches (single target and multi-output) used in predicting the parameters using four different machine learning algorithms (Cashew Multiple Linear Regression (CMLR), Cashew Ridge Regression (CRR), Cashew K-Nearest Neighbor (CKNN), and Cashew Lasso Regression (CLAR) for the predictions. The performance was measured using Mean Square Error(MSE) and Average Root-Mean-Square Error (aRMSE), where we obtained promising results. In the single target prediction results, it is observed that the CRR model (optimized) mse performs better than the other algorithms for pH and water prediction, having a mse of 0.126009, and 3.787282 respectively. Furthermore, for sugar, and drought measures, the optimization of the model’s hyperparameters did not have any significant difference with the baseline model, with the CLAR model outperforming the other models having 4.00923, and 6.364642 mse respectively. For acidity, the CMLR-optimized model outperformed the other algorithms with 0.000670 mse. In the multi-output prediction results, it is observed that

关键词： Forecasting

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