Pub Date : 2023-12-12DOI: 10.2174/0126662558274597231204114801
J. Jebrane, S. Lazaar
In the last decades, the development of Internet activities has been significantly accelerated, particularly with the emergence of the Internet of Things (IoT). Heterogeneous devices in the IoT can seamlessly and feasibly inter-connect with each other without human interaction. Due to this revolution, many applications have been adopted in the arena of smart healthcare, e-commerce, environmental and habitat monitoring, etc. In order to promote and facilitate people's standards of living around the world. However, these unbounded applications bring more challenges to the storage capabilities of devices, and their security and privacy preservation. Moreover, security issues suffer from weak authentication protocols. To address these issues, suitable and secure lightweight mutual authentication schemes based on Elliptic Curve Cryptography (ECC) are required for the approval of Identity Management (IDM) of devices in the IoT. In this paper, we will propose an improved mutual authentication scheme based on ECC, coupled with a relevant seminal work considered as a reference in the field. This scheme is combined with U-Quark, a lightweight hash function, to guarantee the security needed in the IoT environment. We will compare our amended protocol with a seminal scheme as an established reference in terms of computation cost, storage cost, and executing CPU time to demonstrate that our version can ensure the most favorable performance during the authentication process. Finally, our proposed mutual authentication scheme has demonstrated its effectiveness in enhancing the security of IoT devices when compared to the seminal work in the same computational environment.
过去几十年来，互联网活动的发展速度明显加快，尤其是随着物联网（IoT）的出现。物联网中的异构设备可以无缝、可行地相互连接，无需人工交互。在这场革命的推动下，智能医疗、电子商务、环境和人居监测等领域出现了许多应用。以促进和便利世界各地人们的生活水平。然而，这些无限制的应用给设备的存储能力及其安全性和隐私保护带来了更多挑战。为了解决这些问题，物联网设备的身份管理（IDM）审批需要基于椭圆曲线加密法（ECC）的合适而安全的轻量级相互验证方案。在本文中，我们将提出一种基于椭圆曲线加密算法的改进型相互验证方案，并结合被视为该领域参考文献的相关开创性工作。我们将在计算成本、存储成本和执行 CPU 时间方面，将我们的改进协议与作为既定参考的开创性方案进行比较，以证明我们的版本能够在验证过程中确保最有利的性能。最后，在相同的计算环境下，与开创性工作相比，我们提出的相互验证方案证明了它在增强物联网设备安全性方面的有效性。
{"title":"ILAPU-Q: An Improved Lightweight Authentication Protocol for IoT\u0000Based on U-Quark Hash Function","authors":"J. Jebrane, S. Lazaar","doi":"10.2174/0126662558274597231204114801","DOIUrl":"https://doi.org/10.2174/0126662558274597231204114801","url":null,"abstract":"\u0000\u0000In the last decades, the development of Internet activities has been\u0000significantly accelerated, particularly with the emergence of the Internet of Things (IoT). Heterogeneous devices in the IoT can seamlessly and feasibly inter-connect with each other without human interaction. Due to this revolution, many applications have been adopted in the arena of smart healthcare, e-commerce, environmental and habitat monitoring, etc. In order to\u0000promote and facilitate people's standards of living around the world. However, these unbounded applications bring more challenges to the storage capabilities of devices, and their security\u0000and privacy preservation. Moreover, security issues suffer from weak authentication protocols.\u0000\u0000\u0000\u0000To address these issues, suitable and secure lightweight mutual authentication\u0000schemes based on Elliptic Curve Cryptography (ECC) are required for the approval of Identity\u0000Management (IDM) of devices in the IoT. In this paper, we will propose an improved mutual\u0000authentication scheme based on ECC, coupled with a relevant seminal work considered as a\u0000reference in the field. This scheme is combined with U-Quark, a lightweight hash function, to\u0000guarantee the security needed in the IoT environment.\u0000\u0000\u0000\u0000We will compare our amended protocol with a seminal scheme as an established reference in terms of computation cost, storage cost, and executing CPU time to demonstrate that\u0000our version can ensure the most favorable performance during the authentication process.\u0000\u0000\u0000\u0000Finally, our proposed mutual authentication scheme has demonstrated its effectiveness in enhancing the security of IoT devices when compared to the seminal work in the\u0000same computational environment.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139009219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-12DOI: 10.2174/0126662558270314231129051456
Ankit D. Patel, R. Jhaveri, Kaushal A. Shah, Ashish D. Patel, Rajkumar Singh Rathore, Manish Paliwal, Kumar Abhishek, Dhavalkumar Thakker
The Internet of Things (IoT) has revolutionized our society and become indispensable to modern existence. The IoT allows users to access their electronic gadgets from any location. The widespread adoption of IoT across sectors, from manufacturing to surveillance to elder care, has contributed to its rising profile. New security risks and challenges arise with the growth of the IoT. With the development of IoT, the likelihood of an attack by hackers has increased. The burden of addressing these dangers falls on researchers and security professionals. This article looks into the challenges of IoT security in a real-world Ambient Assisted Living (AAL) environment. This work discusses the numerous security attacks employed by cybercriminals in AAL IoT. In addition, this research investigates the varied responses to the risks. We discussed the state-of-the-art technologies available for protecting AAL IoT networks. This work analyses and compares the majority of the latest technologies available. In conclusion, we offer a few suggestions for where the field could go from the current scenario
{"title":"Security Trends in Internet-of-things for Ambient Assistive Living: A Review","authors":"Ankit D. Patel, R. Jhaveri, Kaushal A. Shah, Ashish D. Patel, Rajkumar Singh Rathore, Manish Paliwal, Kumar Abhishek, Dhavalkumar Thakker","doi":"10.2174/0126662558270314231129051456","DOIUrl":"https://doi.org/10.2174/0126662558270314231129051456","url":null,"abstract":"\u0000\u0000The Internet of Things (IoT) has revolutionized our society and become indispensable to modern existence. The IoT allows users to access their electronic gadgets from any location. The widespread adoption of IoT across sectors, from manufacturing to surveillance to elder care, has contributed to its rising profile. New security risks and challenges arise with the\u0000growth of the IoT. With the development of IoT, the likelihood of an attack by hackers has increased. The burden of addressing these dangers falls on researchers and security professionals.\u0000This article looks into the challenges of IoT security in a real-world Ambient Assisted Living\u0000(AAL) environment. This work discusses the numerous security attacks employed by cybercriminals in AAL IoT. In addition, this research investigates the varied responses to the risks.\u0000We discussed the state-of-the-art technologies available for protecting AAL IoT networks. This\u0000work analyses and compares the majority of the latest technologies available. In conclusion, we\u0000offer a few suggestions for where the field could go from the current scenario\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139007688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-12DOI: 10.2174/0126662558264870231122113715
Sriram G.K., Umamaheswari Rajasekaran, A. Malini, Vandana Sharma
In the field of meteorology, temperature forecasting is a significant task as it has been a key factor in industrial, agricultural, renewable energy, and other sectors. High accuracy in temperature forecasting is needed for decision-making in advance. Since temperature varies over time and has been studied to have non-trivial long-range correlation, non-linear behavior, and seasonal variability, it is important to implement an appropriate methodology to forecast accurately. In this paper, we have reviewed the performance of statistical approaches such as AR and ARIMA with RNN, LSTM, GRU, and LSTM-RNN Deep Learning models. The models were tested for short-term temperature forecasting for a period of 48 hours. Among the statistical models, the AR model showed notable performance with a r2 score of 0.955 for triennial 1 and for the same, the Deep Learning models also performed nearly equal to that of the statistical models and thus hybrid LSTM-RNN model was tested. The hybrid model obtained the highest r2 score of 0.960. The difference in RMSE, MAE and r2 scores are not significantly different for both Statistical and Vanilla Deep Learning approaches. However, the hybrid model provided a better r2 score, and LIME explanations have been generated for the same in order to understand the dependencies over a point forecast. Based on the reviewed results, it can be concluded that for short-term forecasting, both Statistical and Deep Learning models perform nearly equally.
{"title":"Analysis of Statistical and Deep Learning Techniques for Temperature\u0000Forecasting","authors":"Sriram G.K., Umamaheswari Rajasekaran, A. Malini, Vandana Sharma","doi":"10.2174/0126662558264870231122113715","DOIUrl":"https://doi.org/10.2174/0126662558264870231122113715","url":null,"abstract":"\u0000\u0000In the field of meteorology, temperature forecasting is a significant task as it has\u0000been a key factor in industrial, agricultural, renewable energy, and other sectors. High accuracy\u0000in temperature forecasting is needed for decision-making in advance. Since temperature varies\u0000over time and has been studied to have non-trivial long-range correlation, non-linear behavior,\u0000and seasonal variability, it is important to implement an appropriate methodology to forecast\u0000accurately. In this paper, we have reviewed the performance of statistical approaches such as\u0000AR and ARIMA with RNN, LSTM, GRU, and LSTM-RNN Deep Learning models. The models were tested for short-term temperature forecasting for a period of 48 hours. Among the statistical models, the AR model showed notable performance with a r2 score of 0.955 for triennial 1 and for the same, the Deep Learning models also performed nearly equal to that of the statistical models and thus hybrid LSTM-RNN model was tested. The hybrid model obtained the\u0000highest r2 score of 0.960. The difference in RMSE, MAE and r2 scores are not significantly\u0000different for both Statistical and Vanilla Deep Learning approaches. However, the hybrid model provided a better r2 score, and LIME explanations have been generated for the same in order\u0000to understand the dependencies over a point forecast. Based on the reviewed results, it can be\u0000concluded that for short-term forecasting, both Statistical and Deep Learning models perform\u0000nearly equally.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139007944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pneumonia is one of the leading causes of death and disability due to respiratory infections. The key to successful treatment of pneumonia is in its early diagnosis and correct classification. PneumoniaNet is a unique deep-learning model based on CNN for identifying pneumonia on chest X-rays. A deep learning model that combines convolutional, pooling, and fully connected layers is presented in this study. In order to learn how to identify cases of pneumonia and healthy controls on chest X-ray pictures, PneumoniaNet was trained on a large labeled library of such images. A robust data augmentation technique was adopted to enhance the model generalization and training set diversity. Standard measures like as accuracy, precision, recall, and F1-score were applied to PneumoniaNet's performance evaluation. The suggested model performed effectively in detecting pneumonia cases with an accuracy of 93.88%. The model was evaluated against the current state-of-art methods and showed that PneumoniaNet outperformed the other models.
肺炎是呼吸道感染导致死亡和残疾的主要原因之一。肺炎的早期诊断和正确分类是成功治疗肺炎的关键。PneumoniaNet 是一种基于 CNN 的独特深度学习模型，用于识别胸部 X 光片上的肺炎。为了学习如何识别胸部 X 光片上的肺炎病例和健康对照组，PneumoniaNet 在一个大型标注的此类图像库上进行了训练。为了学习如何在胸透图片上识别肺炎病例和健康对照组，PneumoniaNet 在此类图片的大型标注库中进行了训练，并采用了鲁棒数据增强技术来提高模型的泛化和训练集的多样性。对 PneumoniaNet 的性能评估采用了准确率、精确度、召回率和 F1 分数等标准衡量指标，结果表明所建议的模型在检测肺炎病例方面表现出色，准确率高达 93.88%。
{"title":"Pneumonia Net: Pneumonia Detection and Categorization in Chest X-ray\u0000Images","authors":"Somya Srivastava, Seema Verma, Nripendra Narayan Das, Shraddha Sharma, Gaurav Dubey","doi":"10.2174/0126662558269484231121112300","DOIUrl":"https://doi.org/10.2174/0126662558269484231121112300","url":null,"abstract":"\u0000\u0000Pneumonia is one of the leading causes of death and disability due to\u0000respiratory infections. The key to successful treatment of pneumonia is in its early diagnosis\u0000and correct classification. PneumoniaNet is a unique deep-learning model based on CNN for\u0000identifying pneumonia on chest X-rays.\u0000\u0000\u0000\u0000A deep learning model that combines convolutional, pooling, and fully connected\u0000layers is presented in this study.\u0000\u0000\u0000\u0000In order to learn how to identify cases of pneumonia and healthy controls on chest\u0000X-ray pictures, PneumoniaNet was trained on a large labeled library of such images. A robust\u0000data augmentation technique was adopted to enhance the model generalization and training set\u0000diversity. Standard measures like as accuracy, precision, recall, and F1-score were applied to\u0000PneumoniaNet's performance evaluation.\u0000\u0000\u0000\u0000The suggested model performed effectively in detecting pneumonia cases with an accuracy of 93.88%.\u0000\u0000\u0000\u0000The model was evaluated against the current state-of-art methods and showed that\u0000PneumoniaNet outperformed the other models.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138980802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-08DOI: 10.2174/0126662558275210231121044758
Souvik Datta, Subbulekshmi D
This study conducts a comprehensive review of Deep Learning-based approaches for accurate object segmentation and detection in high-resolution imagery captured by Unmanned Aerial Vehicles (UAVs). The methodology employs three different existing algorithms tailored to detect roads, buildings, trees, and water bodies. These algorithms include Res-UNet for roads and buildings, DeepForest for trees, and WaterDetect for water bodies. To evaluate the effectiveness of this approach, the performance of each algorithm is compared with state-of-the-art (SOTA) models for each class. The results of the study demonstrate that the methodology outperforms SOTA models in all three classes, achieving an accuracy of 93% for roads and buildings using Res-U-Net, 95% for trees using DeepForest, and an impressive 98% for water bodies using WaterDetect. The paper utilizes a Deep Learning-based approach for accurate object segmentation and detection in high-resolution UAV imagery, achieving superior performance to SOTA models, with reduced overfitting and faster training by employing three smaller models for each task
本研究对基于深度学习的方法进行了全面回顾，以便在无人机（UAV）捕获的高分辨率图像中准确地进行物体分割和检测。该方法采用了三种不同的现有算法，专门用于检测道路、建筑物、树木和水体。这些算法包括用于道路和建筑物的 Res-UNet、用于树木的 DeepForest 和用于水体的 WaterDetect。为了评估这种方法的有效性，我们将每种算法的性能与每个类别的最新（SOTA）模型进行了比较。研究结果表明，该方法在所有三个类别中的表现都优于 SOTA 模型，使用 Res-U-Net 对道路和建筑物的准确率达到 93%，使用 DeepForest 对树木的准确率达到 95%，使用 WaterDetect 对水体的准确率达到令人印象深刻的 98%。本文利用基于深度学习的方法在高分辨率无人机图像中实现了精确的物体分割和检测，取得了优于 SOTA 模型的性能，并通过为每个任务采用三个较小的模型减少了过拟合，加快了训练速度。
{"title":"Review of Deep Learning Algorithms for Urban Remote Sensing Using\u0000Unmanned Aerial Vehicles (UAVs)","authors":"Souvik Datta, Subbulekshmi D","doi":"10.2174/0126662558275210231121044758","DOIUrl":"https://doi.org/10.2174/0126662558275210231121044758","url":null,"abstract":"\u0000\u0000This study conducts a comprehensive review of Deep Learning-based approaches\u0000for accurate object segmentation and detection in high-resolution imagery captured by Unmanned Aerial Vehicles (UAVs). The methodology employs three different existing algorithms tailored to detect roads, buildings, trees, and water bodies. These algorithms include\u0000Res-UNet for roads and buildings, DeepForest for trees, and WaterDetect for water bodies. To\u0000evaluate the effectiveness of this approach, the performance of each algorithm is compared\u0000with state-of-the-art (SOTA) models for each class. The results of the study demonstrate that\u0000the methodology outperforms SOTA models in all three classes, achieving an accuracy of 93%\u0000for roads and buildings using Res-U-Net, 95% for trees using DeepForest, and an impressive\u000098% for water bodies using WaterDetect. The paper utilizes a Deep Learning-based approach\u0000for accurate object segmentation and detection in high-resolution UAV imagery, achieving superior performance to SOTA models, with reduced overfitting and faster training by employing\u0000three smaller models for each task\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139010940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-08DOI: 10.2174/0126662558264263231127062519
Shubham Pandey, Archana Patel
The advent of powerful tools like ChatGPT has opened up exciting new possibilities for augmenting law enforcement capabilities, elevating its efficiency and expanding its capacity. The AI-driven capabilities of ChatGPT can be harnessed to usher in novel applications in diverse areas such as language translation, customer service, content generation, and even law enforcement. In the realm of law enforcement, ChatGPT's potential is boundless, ranging from the generation of detailed reports and the transcription and translation of vital documents to the profiling of criminals and the automation of legal documents. However, the deployment of such technology in law enforcement raises critical issues of privacy, safeguarding fundamental rights, and the perpetuation of societal biases. Furthermore, the deployment of emerging technologies like ChatGPT could also open up new avenues for committing cybercrimes. Therefore, it is crucial to address these issues and establish regulatory frameworks that govern the use of such advanced technologies. In this paper, we provide legal and technical recommendations to regulate the use of ChatGPT and mitigate the potential risks associated with its usage, including the perpetration of various cybercrimes.
{"title":"Leveraging ChatGPT in Law Enforcement","authors":"Shubham Pandey, Archana Patel","doi":"10.2174/0126662558264263231127062519","DOIUrl":"https://doi.org/10.2174/0126662558264263231127062519","url":null,"abstract":"\u0000\u0000The advent of powerful tools like ChatGPT has opened up exciting new possibilities\u0000for augmenting law enforcement capabilities, elevating its efficiency and expanding its capacity.\u0000The AI-driven capabilities of ChatGPT can be harnessed to usher in novel applications in\u0000diverse areas such as language translation, customer service, content generation, and even law\u0000enforcement. In the realm of law enforcement, ChatGPT's potential is boundless, ranging from\u0000the generation of detailed reports and the transcription and translation of vital documents to the\u0000profiling of criminals and the automation of legal documents. However, the deployment of\u0000such technology in law enforcement raises critical issues of privacy, safeguarding fundamental\u0000rights, and the perpetuation of societal biases. Furthermore, the deployment of emerging technologies\u0000like ChatGPT could also open up new avenues for committing cybercrimes. Therefore,\u0000it is crucial to address these issues and establish regulatory frameworks that govern the\u0000use of such advanced technologies. In this paper, we provide legal and technical recommendations\u0000to regulate the use of ChatGPT and mitigate the potential risks associated with its usage,\u0000including the perpetration of various cybercrimes.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139011502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-05DOI: 10.2174/0126662558268813231120114051
Amrita Jyoti, Vikash Yadav, Amita Pal, M. Rahul, Sonu Kumar Jha
This journal paper examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in shaping human psychology. It investigates how cognitive processes, emotional states, and social interactions are impacted by AI and ML technology. The use of AI and ML in psychology is covered in this study, covering social behaviour analysis, emotion identification, mental health assessment, and personalised therapies. It also explores the moral issues and prospective effects of AI and ML in comprehending and influencing human psychology. This paper emphasises the enormous influence of AI and ML on the comprehension and research of human psychology through a thorough analysis of pertinent literature and empirical evidence. This paper seeks to offer a thorough explanation of the profound effects that AI and ML have had on psychology. We will offer insight into the possible advantages, difficulties, and ethical issues that occur when integrating AI and ML into the study of human psychology by looking at recent developments and implementations of these technologies in psychological research. We will also look at how other areas of psychology, such as cognitive psychology, clinical psychology, social psychology, and neurology, have been impacted by AI and ML.
{"title":"The Transformative Impact of AI and Machine Learning on Human\u0000Psychology","authors":"Amrita Jyoti, Vikash Yadav, Amita Pal, M. Rahul, Sonu Kumar Jha","doi":"10.2174/0126662558268813231120114051","DOIUrl":"https://doi.org/10.2174/0126662558268813231120114051","url":null,"abstract":"\u0000\u0000This journal paper examines the transformative role of Artificial Intelligence (AI)\u0000and Machine Learning (ML) in shaping human psychology. It investigates how cognitive processes,\u0000emotional states, and social interactions are impacted by AI and ML technology. The\u0000use of AI and ML in psychology is covered in this study, covering social behaviour analysis,\u0000emotion identification, mental health assessment, and personalised therapies. It also explores\u0000the moral issues and prospective effects of AI and ML in comprehending and influencing human\u0000psychology. This paper emphasises the enormous influence of AI and ML on the comprehension\u0000and research of human psychology through a thorough analysis of pertinent literature\u0000and empirical evidence. This paper seeks to offer a thorough explanation of the profound effects\u0000that AI and ML have had on psychology. We will offer insight into the possible advantages,\u0000difficulties, and ethical issues that occur when integrating AI and ML into the study\u0000of human psychology by looking at recent developments and implementations of these technologies\u0000in psychological research. We will also look at how other areas of psychology, such\u0000as cognitive psychology, clinical psychology, social psychology, and neurology, have been\u0000impacted by AI and ML.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138599738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-12-04DOI: 10.2174/0126662558270259231122040821
Ruihua Zhang, Shubo Bi
In using a camera to take photos, due to environmental limitations, uneven lighting can cause uneven distribution of the image light field, resulting in distortion of the image background and target, blurring of details, and distorted light field images. In view of this, research is conducted on the correction of distorted light field images based on the Hough transform. First, the improved Hough transform is utilized to locate the four coordinates, the matrix information of the normal image is applied to calculate the corresponding parameter amount, and then the low-frequency part of the image spectrum is removed. Finally, it uses the Gaussian function for difference, inputs the original data, and gets the correction result of the distorted light field image. The research results indicate that in the practical application of the distorted light field image correction algorithm based on the Hough transform, the improved Hough transform algorithm is superior to the traditional one. In comparative experiments, the research algorithm outperforms the other three algorithms, with an average color restoration of 93.76% and an average signal-to-noise ratio of 54.22dB. The superiority of the research algorithm has been verified, indicating that the research method can perfectly correct distorted light field images and achieve good correction results.
{"title":"A Distorted Light Field Image Correction Method Based on Improved\u0000Hough Transform","authors":"Ruihua Zhang, Shubo Bi","doi":"10.2174/0126662558270259231122040821","DOIUrl":"https://doi.org/10.2174/0126662558270259231122040821","url":null,"abstract":"\u0000\u0000In using a camera to take photos, due to environmental limitations,\u0000uneven lighting can cause uneven distribution of the image light field, resulting in distortion of\u0000the image background and target, blurring of details, and distorted light field images.\u0000\u0000\u0000\u0000In view of this, research is conducted on the correction of distorted light field images\u0000based on the Hough transform. First, the improved Hough transform is utilized to locate the\u0000four coordinates, the matrix information of the normal image is applied to calculate the corresponding parameter amount, and then the low-frequency part of the image spectrum is removed. Finally, it uses the Gaussian function for difference, inputs the original data, and gets\u0000the correction result of the distorted light field image.\u0000\u0000\u0000\u0000The research results indicate that in the practical application of the distorted light field\u0000image correction algorithm based on the Hough transform, the improved Hough transform algorithm is superior to the traditional one.\u0000\u0000\u0000\u0000In comparative experiments, the research algorithm outperforms the other three\u0000algorithms, with an average color restoration of 93.76% and an average signal-to-noise ratio of\u000054.22dB. The superiority of the research algorithm has been verified, indicating that the research method can perfectly correct distorted light field images and achieve good correction\u0000results.\u0000","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138602154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-28DOI: 10.2174/0126662558263462231020111428
Keerti Singh, Charu Krishna, Divya Kumar
The healthcare sector faces numerous issues, such as insurance fraud, electronic medical record management, interoperability, insecure dissemination of information, etc. The novel Blockchain technology holds tremendous potential to transform the healthcare sector by addressing these rising challenges in the industry. It provides a secure platform for storing, disseminating, and retrieving sensitive patient data and health records while preserving the ethical principles of the healthcare sector. In this study, we systematically reviewed the literature on blockchain technology in healthcare using PRISMA and highlighted how blockchain technology might promote innovation and deliver major improvements in the healthcare sector. Our goal is to examine the present status of this discipline, focusing on limits and potential advances. Queries were used to gather Scopus, PubMed, SpringerLink, IEEE Xplore, and Web of Science publications that met the criteria for the selection of papers. This article, thus, analyses the potential for blockchain in the healthcare industry and outlines blockchain-based products in healthcare. Our study enhances and complements prior healthcare blockchain research.
医疗保健行业面临着保险欺诈、电子病历管理、互操作性、信息传播不安全等诸多问题。新颖的区块链技术具有巨大的潜力，可以解决医疗行业面临的这些日益严峻的挑战，从而改变医疗行业。它为存储、传播和检索敏感的患者数据和健康记录提供了一个安全的平台，同时维护了医疗保健行业的道德原则。 在本研究中，我们利用 PRISMA 系统地回顾了有关医疗保健领域区块链技术的文献，并重点介绍了区块链技术如何促进医疗保健领域的创新并带来重大改进。 我们的目标是研究该学科的现状，重点关注其局限性和潜在进展。通过查询，我们收集了符合论文筛选标准的 Scopus、PubMed、SpringerLink、IEEE Xplore 和 Web of Science 出版物。 因此，本文分析了区块链在医疗保健行业的潜力，并概述了医疗保健领域基于区块链的产品。我们的研究加强并补充了之前的医疗保健区块链研究。
{"title":"Professional Ethics, Challenges and Opportunities for Blockchain Technology in Healthcare Sector: A Systematic Review","authors":"Keerti Singh, Charu Krishna, Divya Kumar","doi":"10.2174/0126662558263462231020111428","DOIUrl":"https://doi.org/10.2174/0126662558263462231020111428","url":null,"abstract":"The healthcare sector faces numerous issues, such as insurance fraud, electronic medical record management, interoperability, insecure dissemination of information, etc. The novel Blockchain technology holds tremendous potential to transform the healthcare sector by addressing these rising challenges in the industry. It provides a secure platform for storing, disseminating, and retrieving sensitive patient data and health records while preserving the ethical principles of the healthcare sector. In this study, we systematically reviewed the literature on blockchain technology in healthcare using PRISMA and highlighted how blockchain technology might promote innovation and deliver major improvements in the healthcare sector. Our goal is to examine the present status of this discipline, focusing on limits and potential advances. Queries were used to gather Scopus, PubMed, SpringerLink, IEEE Xplore, and Web of Science publications that met the criteria for the selection of papers. This article, thus, analyses the potential for blockchain in the healthcare industry and outlines blockchain-based products in healthcare. Our study enhances and complements prior healthcare blockchain research.","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139227605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract: The extensive use of electricity and the increasing number of consumers challenge matching power consumption with the power generated. Having a traditional way of power generation and distribution, power is also widely fetched through renewable energy sources. So, to have improved efficiency and reliable means of the power source, to be able to integrate multiple sources of power generation like PV Cells, Solar Power, and Wind Power into the existing standards of the power source, precise calculations of the power consumption in the multisource environment, provision to scale up the smart and electric vehicle and most importantly, to reduce the carbon emissions, several attempts have been made to convert the traditional grids into smart grids. A tiny step in the smart grid's development is the smart metering infrastructure, in which smart meters are deployed through the consumer end. Through smart meters, it is possible to establish the link, either through wireless media or wired connections, between the consumer and the grid. Once the smart meters are deployed through the Advanced Metering Infrastructure (AMI), the meters remain active round the clock, giving a window to hackers. Through this window, utility bill manipulations, payment transaction information, and other significant data can be accessed by unethical approaches and threaten the consumer's privacy. This review-research paper discusses various methods presented by distinct authors to address the issues related to customer privacy protection in the smart grid.
{"title":"A Review on Privacy Protection Techniques in Smart Grid Applications","authors":"Rakhi Gupta, Parminder Singh, Avinash Kaur, Mustapha Hedabou","doi":"10.2174/0126662558254700231024015340","DOIUrl":"https://doi.org/10.2174/0126662558254700231024015340","url":null,"abstract":"Abstract: The extensive use of electricity and the increasing number of consumers challenge matching power consumption with the power generated. Having a traditional way of power generation and distribution, power is also widely fetched through renewable energy sources. So, to have improved efficiency and reliable means of the power source, to be able to integrate multiple sources of power generation like PV Cells, Solar Power, and Wind Power into the existing standards of the power source, precise calculations of the power consumption in the multisource environment, provision to scale up the smart and electric vehicle and most importantly, to reduce the carbon emissions, several attempts have been made to convert the traditional grids into smart grids. A tiny step in the smart grid's development is the smart metering infrastructure, in which smart meters are deployed through the consumer end. Through smart meters, it is possible to establish the link, either through wireless media or wired connections, between the consumer and the grid. Once the smart meters are deployed through the Advanced Metering Infrastructure (AMI), the meters remain active round the clock, giving a window to hackers. Through this window, utility bill manipulations, payment transaction information, and other significant data can be accessed by unethical approaches and threaten the consumer's privacy. This review-research paper discusses various methods presented by distinct authors to address the issues related to customer privacy protection in the smart grid.","PeriodicalId":36514,"journal":{"name":"Recent Advances in Computer Science and Communications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135372776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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