Modern organizations face rising levels of cyber risks, making cybersecurity a top priority for safeguarding sensitive data and maintaining operational continuity. Recent studies find human factors as the most critica...
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ISBN:
(数字)9798331518592
ISBN:
(纸本)9798331518608
Modern organizations face rising levels of cyber risks, making cybersecurity a top priority for safeguarding sensitive data and maintaining operational continuity. Recent studies find human factors as the most critical contributors to cyber risks. These human-related risks include user misuse, user mistakes, and user malice. A large proportion of cyber risks arise when hackers use social engineering or phishing techniques to manipulate employees into disclosing confidential information. While some employees knowingly engage in these actions, some incidents occur accidentally due to a lack of awareness about cybersecurity protocols. As a result, governments around the world are enacting data protection legislation aimed at enhancing employees' cyber risk. This study explores the perception of US employees on the effectiveness of data protection legislation in countering cyber threats. It surveyed 30 randomly selected employees from various industries. The results showed that employees perceive data protection legislation as effective in mitigating employeerelated cyber risks.
White matter (WM) has traditionally been undervalued in functional brain connectivity studies due to its weaker blood-oxygenation-level-dependent (BOLD) signals compared to gray matter (GM). Conventional atlas-based a...
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ISBN:
(数字)9798331520526
ISBN:
(纸本)9798331520533
White matter (WM) has traditionally been undervalued in functional brain connectivity studies due to its weaker blood-oxygenation-level-dependent (BOLD) signals compared to gray matter (GM). Conventional atlas-based approaches often overlook the individualized aspects of functional networks, limiting their applicability for WM. In this study, we introduce the first WM intrinsic connectivity network (ICN) templates derived from a large dataset of over 100,000 scans, demonstrating the feasibility of estimating individualized WM networks using multivariate-spatially constrained ICA (scICA). By combining WM and GM ICNs, our work provides an expanded framework for evaluating functional network connectivity (FNC) across both tissue types, offering new insights into the integrative role of WM in functional connectivity. This approach holds particular promise for enhancing the understanding of connectivity alterations in clinical populations, such as individuals with schizophrenia.
The swift progression of wireless communication technologies-specifically from 5G to 6G is an approach that could be the most significant revolutionary leap towards changing connectivity and data transmission forever....
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ISBN:
(数字)9798331523923
ISBN:
(纸本)9798331523930
The swift progression of wireless communication technologies-specifically from 5G to 6G is an approach that could be the most significant revolutionary leap towards changing connectivity and data transmission forever. Additionally, algorithms of machine learning (ML) and deep learning (DL) in 5G and 6G can offer promising advancements toward predictive maintenance, dynamic spectrum management, and enhanced security. The review of the potentially transformative nature of these technologies underlines the need for future directions of research efforts called for in circumventing challenges towards making full capability exploitation of 6G possible.
The opportunity for electric transportation system optimization has never been greater with the combination of cloud-based machine learning algorithms and 5G-enabled Vehicle-to-Everything (V2X) connectivity. To improv...
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ISBN:
(数字)9798350353068
ISBN:
(纸本)9798350353075
The opportunity for electric transportation system optimization has never been greater with the combination of cloud-based machine learning algorithms and 5G-enabled Vehicle-to-Everything (V2X) connectivity. To improve the effectiveness, reliability, and longevity of electric vehicle (EV) networks, it provides a new architecture combining V2X communication enabled by 5G and an XGBoost algorithm driven by the cloud. Vehicles may exchange data in real time and make decisions with the help of infrastructure and the cloud via the seamless communication made possible by 5G networks’ high-speed, low-latency connection. The XGBoost algorithm, hosted on cloud servers, can use this data to forecast several metrics, including traffic flow, energy usage, and the best way to charge. Cloud computing allows more sophisticated analysis and prediction models by moving the processing load from individual cars to large, powerful servers. It improves transportation efficiency, decreases congestion, minimizes energy usage, and optimizes charging infrastructure via simulation and real-world experiments.
In worldwide, the wheat is a significant crop which generates main source of food for numerous peoples. Through, the development of crop productions is vulnerable through various diseases such as bacterial, viral and ...
In worldwide, the wheat is a significant crop which generates main source of food for numerous peoples. Through, the development of crop productions is vulnerable through various diseases such as bacterial, viral and fungal infections. These type of disease can cause important damage in crops which leads to diminish the yield production and grain quality. This paper proposed a Three-Dimensional Convolutional Neural Network (3D-CNN) for wheat rust disease classification that learns to recognize the pattern and structure of rust disease using convolution filter layers. The CGIAR dataset is used which contains 1486 images and it is pre-processed by gaussian filter which reduces the noise and smoothens the image. Then, the Discrete Wavelet Transform (DWT) is used for feature extraction which works in discrete timespan that outputs in low computational cost. Then, 3D-CNN is used for the classification of wheat rust disease. The performance of 3D-CNN is estimated by accuracy, recall, f1score and precision. The 3D-CNN attains accuracy 99.83%, recall 98.89%, f1score 98.81 % and precision 98.79 % when compared to existing techniques like GNet+FERSPNET-50 and Few-shot learning based EfficientNet.
This paper investigates a novel generative artificial intelligence (GAI) empowered multi-user semantic communication system called semantic feature multiple access (SFMA) for video transmission, which comprises a base...
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In over two decades of research, the field of dictionary learning has gathered a large collection of successful applications, and theoretical guarantees for model recovery are known only whenever optimization is carri...
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In over two decades of research, the field of dictionary learning has gathered a large collection of successful applications, and theoretical guarantees for model recovery are known only whenever optimization is carried out in the same model class as that of the underlying dictionary. This work characterizes the surprising phenomenon that dictionary recovery can be facilitated by searching over the space of larger over-realized models. This observation is general and independent of the specific dictionary learning algorithm used. We thoroughly demonstrate this observation in practice and provide an analysis of this phenomenon by tying recovery measures to generalization bounds. In particular, we show that model recovery can be upper-bounded by the empirical risk, a model-dependent quantity and the generalization gap, reflecting our empirical findings. We further show that an efficient and provably correct distillation approach can be employed to recover the correct atoms from the over-realized model. As a result, our meta-algorithm provides dictionary estimates with consistently better recovery of the ground-truth model.
The concrete business has just come out with a brand-new product called geopolymer concrete. Because of its resilience in harsh environments and its high strength, it has the potential to revolutionise the whole const...
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Monitoring the student's entry and exit time into a classroom is a crucial task. This system will be a solution for performing that crucial task. In this system, for face detection and face recognition, two pre-tr...
Monitoring the student's entry and exit time into a classroom is a crucial task. This system will be a solution for performing that crucial task. In this system, for face detection and face recognition, two pre-trained models were imported and utilized to create face embeddings for a collection of photographs kept in a file directory. The face embeddings and corresponding labels are then used to train an SVM classifier using the scikit-learn library's SVC class. To detect and recognize the students, the pre-trained face detection and recognition models were loaded using OpenCV's dnn module. Then the saved face embeddings and labels were also loaded using pickle, which was previously generated by a face recognition model. Face detection is performed using a Caffe model trained on the SSD framework, while face recognition is done using OpenFace. The system identifies the student's faces using embeddings that were generated by pre-processing the images and were saved in the face_*** file. It then matches these embeddings to the corresponding labels that were saved in the *** file. Then the input is captured from the webcam and the optical flow analysis is applied to detect motion in the video stream. After that, it initializes the input image with a blurred, grayscale image and sets the starting points for the motion tracking. It then loops over the captured frames, blurs and converts them to grayscale, and uses calcOpticalFlowPyrLK to calculate the new points based on the old ones. If the new points are within a certain range, it draws a line between them on the input image and displays it. If the points move outside a certain threshold, it triggers an event to log the entry or exit of a student from a classroom and appends these details into a text file accordingly. Finally, this text file is sent through email to a specified list of recipients (faculties) on a daily basis.
Time series forecasting (TSF) possesses great practical values in various fields, including power and energy, transportation, etc. TSF methods have been studied based on knowledge from classical statistics to modern d...
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