The protection of confidential data has been a critical concern since ancient times. Steganography and cryptography are two key techniques used to enhance data security. Cryptography transforms confidential data into ...
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ISBN:
(数字)9798331505189
ISBN:
(纸本)9798331505196
The protection of confidential data has been a critical concern since ancient times. Steganography and cryptography are two key techniques used to enhance data security. Cryptography transforms confidential data into an unreadable format, while steganography conceals the very existence of the communication. Both are crucial for safeguarding information in today's rapidly expanding networks. This paper proposes a novel method for secure data transmission over unsecured channels. The process begins by encrypting the secret data using the AES algorithm, which ensures robust security even if the hidden communication is detected. The encrypted data is then embedded into a cover medium at its edge locations, identified using a Sobel edge detector, and placed with the help of a hash function. The method employs the 1-bit Least Significant Bit (LSB) technique, implemented in C#, offering high data hiding capacity and minimal distortion in the stego image. Experimental results demonstrate that the proposed approach effectively conceals data while maintaining the quality of the cover image, making it highly suitable for secure data transmission.
Capacitated Vehicle Routing Problems (CVRPs), a widely acknowledged NP-hard issue, pertains to the optimal routing of a limited-capacity vehicle fleet to fulfill customer demand, aiming for the least possible travel d...
Capacitated Vehicle Routing Problems (CVRPs), a widely acknowledged NP-hard issue, pertains to the optimal routing of a limited-capacity vehicle fleet to fulfill customer demand, aiming for the least possible travel distance or cost. Despite the presence of numerous heuristic and exact approaches, the combinatorial characteristic of CVRP renders it challenging, especially for large-scale instances. This research provides an in-dept. exploration of utilizing Genetic Algorithms (GAs) to address Capacitated Vehicle Routing Problems (CVRPs), a recognized and intricate optimization issue in the realm of logistics and supply chain management. Our paper concentrates on the innovative usage of GAs, a category of stochastic search methodologies inspired by natural selection and genetics, to grapple with CVRP. We put forth a fresh framework grounded in GA that infuses unique crossover and mutation operations tailor-made for CVRP. Our comprehensive computational trials on benchmark datasets suggest that our GA-centric method is proficient in deriving high-standard solutions within acceptable computational durations, surpassing multiple contemporary techniques concerning solution quality and resilience. Our results also underscore the scalability of our proposed approach, marking it as a viable choice for tackling extensive, real-world CVRPs. This paper enriches the current knowledge bank by demonstrating the prowess of GAs in deciphering complicated combinatorial optimization issues, thus offering a novel viewpoint for future advancements in crafting more robust and efficient CVRP resolutions.
Technical Debt, considered by many to be the 'silent killer' of software projects, has undeniably become part of the everyday vocabulary of software engineers. We know it compromises the internal quality of a ...
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YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. Developing a custom object detection solution that can detect specific objects in real-time...
YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. Developing a custom object detection solution that can detect specific objects in real-time video streams has the potential to revolutionize various fields and has been the subject of extensive research. Although there have been advances in object detection, there is still a gap in the research for real-time detection of custom objects with high accuracy and speed. This research addresses this gap by training a YOLOv8 detector on a custom dataset of objects and evaluating its performance on real-time video streams which is by far the latest model and thus is faster and more accurate. Our experimental results demonstrate that our custom-trained YOLOv8 detector achieves high accuracy and real-time performance on a custom dataset of objects. The detector achieved an overall mAP50 of 0.864 and a mAP50-95 of 0.758, with individual class results ranging from 0.47 to 0.995. These findings show that custom training data and YOLOv8 are effective in real-time object detection, which has practical applications in various fields. The significance of the results and our contribution lies in demonstrating the effectiveness of custom training data for improving object detection accuracy and speed using YOLO, which has implications for a wide range of real-world applications.
A glance on the developments of the railway safety system reveals that the consistent research has made a remarkable progresses finding and devising techniques to alleviate noise and vibration resulting from wheel and...
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Edge computing is characterised by a varying workload intensity that has a strong effect in the applications performance and their resource requirements. Thus, in order to maintain a sustainable performance a resource...
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Natural language processing (NLP) implemented in digital scientific records (EMRs) can substantially enhance the nice and efficiency of affected person care. The purpose of NLP implemented in EMRs is to extract applic...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
Natural language processing (NLP) implemented in digital scientific records (EMRs) can substantially enhance the nice and efficiency of affected person care. The purpose of NLP implemented in EMRs is to extract applicable facts from affected persons’ notes written in a human language together with English. This information can then be stored in a suitable structured form for further evaluation and records mining. NLP has been carried out in the clinical field for the reason that Fifties as a green approach for retrieving textual content-based data and reading interactions among affected persons and healthcare professionals. With the arrival of electronic facts, NLP has come to be extra extensively applied for the diffusion of purposes, inclusive of automatic coding, scientific choice aid, and medical doctor order access. This summary makes a of exploring the usage of NLP in EMRs. The scope of this research consists of an evaluate of present NLP technologies and their software in EMRs. It additionally outlines a number of the present-day demanding situations inside the use of NLP for clinical information and shows capability answers. Finally, the potential applications of NLP-driven EMRs are discussed, inclusive of making use of in-health practitioner order entry, scientific choice assistance, and population health control.
Code obfuscation is a technique that makes programs harder to understand. Malware writers widely the obfuscation technique to evade detection from anti-malware software, or to deter reverse engineering attempts for th...
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In this study, cortical brain activity during a pattern matching task (PMT) was measured by employing electroencephalography (EEG). The EEG data were recorded from 128 scalp locations during a pattern-matching task an...
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Network embedding is an important method to learn low-dimensional vector representations of nodes in networks, which has wide-ranging applications in network analysis such as link prediction. Most existing network emb...
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