Journal of Ambient Intelligence and Humanized Computing最新文献_第8页

An improved blockchain framework for ORAP verification and data security in healthcare 用于医疗保健领域 ORAP 验证和数据安全的改进型区块链框架

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-12 DOI: 10.1007/s12652-024-04780-4

Parag Rastogi, Devendra Singh, Sarabjeet Singh Bedi

{"title":"An improved blockchain framework for ORAP verification and data security in healthcare","authors":"Parag Rastogi, Devendra Singh, Sarabjeet Singh Bedi","doi":"10.1007/s12652-024-04780-4","DOIUrl":"https://doi.org/10.1007/s12652-024-04780-4","url":null,"abstract":"Currently, the move from traditional healthcare to smart healthcare systems is greatly aided by current technology. Healthcare proposes a new healthcare model that is patient-centered using advancements in wearable sensors, connectivity, and the Internet of Things (IoT). The administration of enormous amounts of data, including reports and pictures of every individual, increases human labour requirements and security hazards. This study shows how a blockchain-based Internet of Things might improve patient care while lowering costs by using medical resources more wisely. Initially, Resource Provider’s IoT data will be sensed and encrypts using Diffie Hellman Galois–Elliptic-curve cryptography (DHG-ECC). Next, from the extracted attributes, the optimal features will be selected by using Pearson Correlation Coefficient based Sand Cat Optimization Algorithm (PCC-SCOA). After that, the selected optimal features will be combined and converted into hashcode using the Digit Folding–Streebog Hashing algorithm. This hashcode will be constructed in the form of Smart Contract. Next, the Resource Requester (Doctor or Nurse) sends the Role Request with the Combined Linear Congruential Generator–Digital Signature Algorithm (CLCG-DSA). The next Resource Requester will be matching the hashed access policy with Blockchain. The proposed models are used to compare the performance of proposed design using feature selection time, Encryption time, Decryption time, security level, signature creation time and signature verification time. Our proposed method DHGECC approach achieves 96.123% higher security.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic design of W-operators using membership functions: a case study in brain MRI segmentation 使用成员函数自动设计 W 运算器：脑磁共振成像分割案例研究

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-11 DOI: 10.1007/s12652-024-04789-9

Emilio José Robalino Trujillo, Agustina Bouchet, Virginia Laura Ballarin, Juan Ignacio Pastore

{"title":"Automatic design of W-operators using membership functions: a case study in brain MRI segmentation","authors":"Emilio José Robalino Trujillo, Agustina Bouchet, Virginia Laura Ballarin, Juan Ignacio Pastore","doi":"10.1007/s12652-024-04789-9","DOIUrl":"https://doi.org/10.1007/s12652-024-04789-9","url":null,"abstract":"A W-operator is an image transformation that is locally defined inside a window W, invariant to translations. The automatic design of the W-operators consists of the design of functions, whose domain is a set of patterns or vectors obtained by translating a window through training images and the output of each vector is a class or label. The main difficulty to consider when designing W-operators is the generalization problem that occurs due to lack of training images. In this work, we propose the use of membership functions to solve the generalization problem in gray level images. Membership functions are defined from the training images to model regions that are often inaccurate due to ambiguous gray levels in the images. This proposal was applied to brain magnetic resonance image segmentation to test its performance in a field of interest in biomedical images. The experiments were carried out with different numbers of training and test images, windows sizes of (3times 3), (5times 5), (7times 7), (11times 11), and (15times 15), and images with noise levels at 0, 1, 3, 5, 7, and 9(%). To calculate the performance of each designed W-operator, the classification error, sensitivity, and specificity were used. From the experimental results, it was concluded that the best performance is achieved with a window of size (3times 3). In images with noise levels from 1 to 5(%), the classification error is less than 4(%) and the sensitivity and specificity are greater than 94 and 98(%), respectively.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Special issue on advancements in ambient assisted living: integrating technology and human-centered design for enhancing user well-being and care 环境辅助生活的进步特刊：整合技术和以人为本的设计，提高用户福祉和护理水平

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-09 DOI: 10.1007/s12652-024-04799-7

A. Monteriù, A. Freddi, S. Longhi

引用次数: 0

Decision making using novel Fermatean fuzzy divergence measure and weighted aggregation operators 利用新型费尔马特模糊分歧度量和加权聚合算子进行决策

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-08 DOI: 10.1007/s12652-024-04774-2

Adeeba Umar, Ram Naresh Saraswat

{"title":"Decision making using novel Fermatean fuzzy divergence measure and weighted aggregation operators","authors":"Adeeba Umar, Ram Naresh Saraswat","doi":"10.1007/s12652-024-04774-2","DOIUrl":"https://doi.org/10.1007/s12652-024-04774-2","url":null,"abstract":"The fuzzy set theory was introduced to handle uncertainty due to imprecision, vagueness and partial information. Then, its extensions such as intuitionistic fuzzy set, intuitionistic interval-valued fuzzy set, Pythagorean fuzzy set were introduced and applied successfully in many fields. Then another extension of orthopair fuzzy set was introduced as Fermatean fuzzy set which is characterized by membership degree and non-membership degree which makes it to provide an excellent tool to present imprecise opinions of humans in decision-making processes. This study is devoted to construct a novel Fermatean fuzzy divergence measure along with its evidence of legitimacy and to deliberate its key properties. The proposed divergence measure for Fermatean fuzzy sets with weighted aggregation operators is applied to fix decision-making problems through numerical illustrations. A comparative study is given between the proposed Fermatean fuzzy divergence measure and the extant methods to test its effectiveness, viability and expediency. Their results were compared in order to check the superiority of the proposed measure.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian inference in the framework of uncertainty theory 不确定性理论框架下的贝叶斯推理

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-07 DOI: 10.1007/s12652-024-04785-z

引用次数: 0

A reinforcement learning based mobile charging sequence scheduling algorithm for optimal sensing coverage in wireless rechargeable sensor networks 基于强化学习的移动充电序列调度算法，用于优化无线充电传感器网络中的传感覆盖范围

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-06 DOI: 10.1007/s12652-024-04781-3

{"title":"A reinforcement learning based mobile charging sequence scheduling algorithm for optimal sensing coverage in wireless rechargeable sensor networks","authors":"","doi":"10.1007/s12652-024-04781-3","DOIUrl":"https://doi.org/10.1007/s12652-024-04781-3","url":null,"abstract":"<h3>Abstract</h3> Mobile charging provides a new way for energy replenishment in the Wireless Rechargeable Sensor Network (WRSN), where the Mobile Charger (MC) is employed for charging nodes sequentially via wireless energy transfer according to the mobile charging sequence scheduling result. Mobile Charging Sequence Scheduling for Optimal Sensing Coverage (MCSS-OSC) is a critical problem for providing network application performance; it aims to maximize the Quality of Sensing Coverage (QSC) of the network by optimizing the MC’s mobile charging sequence and remains a challenging problem due to its NP-completeness in nature. In this paper, we propose a novel Improved Q-learning Algorithm (IQA) for MCSS-OSC, where MC is taken as an agent to continuously learn the space of mobile charging strategies through approximate estimation and improve the charging strategy by interacting with the network environment. A novel reward function is designed according to the network sensing coverage contribution to evaluate the MC charging action at each charging time step. In addition, an efficient exploration strategy is also designed by introducing an optimal experience-strengthening mechanism to record the current optimal mobile charging sequence regularly. Extensive simulation results via Matlab2021 software show that IQA is superior to existing heuristic algorithms in network QSC, especially for large-scale networks. This paper provides an efficient solution for WRSN energy management and new ideas for performance optimization of reinforcement learning algorithms.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ensemble deep learning for high-precision classification of 90 rice seed varieties from hyperspectral images 利用高光谱图像对 90 个水稻种子品种进行高精度分类的集合深度学习

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-05 DOI: 10.1007/s12652-024-04782-2

AmirMasoud Taheri, Hossein Ebrahimnezhad, Mohammadhossein Sedaaghi

{"title":"Ensemble deep learning for high-precision classification of 90 rice seed varieties from hyperspectral images","authors":"AmirMasoud Taheri, Hossein Ebrahimnezhad, Mohammadhossein Sedaaghi","doi":"10.1007/s12652-024-04782-2","DOIUrl":"https://doi.org/10.1007/s12652-024-04782-2","url":null,"abstract":"To develop rice varieties with better nutritional qualities, it is important to classify rice seeds accurately. Hyperspectral imaging can be used to extract spectral information from rice seeds, which can then be used to classify them into different varieties. The challenges of precise classification increase when there are many classes and few training samples. In this paper, we present a novel method for high-precision Hyperspectral Image (HSI) classification of 90 different classes of rice seeds using ensemble deep learning. Our method first employs band selection techniques to select the optimal hyperspectral bands for rice seed classification. Then, a deep neural network is trained with the selected hyperspectral and RGB data from rice seed images to obtain different models for different bands. Finally, an ensemble of deep learning models is employed to classify rice seed images and improve classification accuracy. The proposed method achieves an overall precision ranging from 92.73 to 96.17% despite a large number of classes and low data samples for each class and with only 15 selected hyperspectral bands. This precision is significantly higher than the state-of-the-art classical machine learning methods like random forest, confirming the effectiveness of the proposed method in classifying hyperspectral images of rice seeds.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Software security with natural language processing and vulnerability scoring using machine learning approach 利用自然语言处理和机器学习方法进行漏洞评分的软件安全性

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-03 DOI: 10.1007/s12652-024-04778-y

Birendra Kumar Verma, Ajay Kumar Yadav

{"title":"Software security with natural language processing and vulnerability scoring using machine learning approach","authors":"Birendra Kumar Verma, Ajay Kumar Yadav","doi":"10.1007/s12652-024-04778-y","DOIUrl":"https://doi.org/10.1007/s12652-024-04778-y","url":null,"abstract":"As software gets more complicated, diverse, and crucial to people’s daily lives, exploitable software vulnerabilities constitute a major security risk to the computer system. These vulnerabilities allow unauthorized access, which can cause losses in banking, energy, the military, healthcare, and other key infrastructure systems. Most vulnerability scoring methods employ Natural Language Processing to generate models from descriptions. These models ignore Impact scores, Exploitability scores, Attack Complexity and other statistical features when scoring vulnerabilities. A feature vector for machine learning models is created from a description, impact score, exploitability score, attack complexity score, etc. We score vulnerabilities more precisely than we categorize them. The Decision Tree Regressor, Random Forest Regressor, AdaBoost Regressor, K-nearest Neighbors Regressor, and Support Vector Regressor have been evaluated using the metrics explained variance, r-squared, mean absolute error, mean squared error, and root mean squared error. The tenfold cross-validation method verifies regressor test results. The research uses 193,463 Common Vulnerabilities and Exposures from the National Vulnerability Database. The Random Forest regressor performed well on four of the five criteria, and the tenfold cross-validation test performed even better (0.9968 vs. 0.9958).","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Existence of fixed points in soft metric spaces with application to boundary value problem 软度量空间中定点的存在及其在边界值问题中的应用

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-03 DOI: 10.1007/s12652-024-04772-4

Vishal Gupta, Aanchal Gondhi

引用次数: 0

Static video summarization with multi-objective constrained optimization 利用多目标约束优化进行静态视频总结

3区计算机科学

Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-04-01 DOI: 10.1007/s12652-024-04777-z

{"title":"Static video summarization with multi-objective constrained optimization","authors":"","doi":"10.1007/s12652-024-04777-z","DOIUrl":"https://doi.org/10.1007/s12652-024-04777-z","url":null,"abstract":"<h3>Abstract</h3> Video summarization is an emerging research field. In particular, static video summarization plays a major role in abstraction and indexing of video repositories. It extracts the vital events in a video such that it covers the entire content of the video. Frames having those important events are called keyframes which are eventually used in video indexing. It also helps in giving an abstract view of the video content such that the internet users are aware of the events present in the video before watching it completely. The proposed research work is focused on efficient static video summarization by extracting various visual features namely color, texture and shape features. These features are aggregated and clustered using a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. In order to produce good video summary by clustering, the parameters of DBSCAN algorithm are optimized by using a meta heuristic population based optimization called Artificial Algae Algorithm (AAA). The experimental results on two public datasets namely VSUMM and OVP dataset show that the proposed Static Video Summarization with Multi-objective Constrained Optimization (SVS_MCO) achieves better results when compared to existing methods.","PeriodicalId":14959,"journal":{"name":"Journal of Ambient Intelligence and Humanized Computing","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140570439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0