Remote sensing images present classification challenges due to the complexity of their structural and spatial patterns. This research explores a hybrid approach that combines convolutional neural network (CNN) and att...
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Algorithms for steganography are methods of hiding data transfers in media *** machine learning architectures have been presented recently to improve stego image identification performance by using spatial information...
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Algorithms for steganography are methods of hiding data transfers in media *** machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image *** with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for *** address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of *** Vector Machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or *** Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the *** WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.
Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language *** modeling,crucial for AI development,has evolved from statistical to neural models over the last two ***,trans...
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Since the 1950s,when the Turing Test was introduced,there has been notable progress in machine language *** modeling,crucial for AI development,has evolved from statistical to neural models over the last two ***,transformer-based Pre-trained Language Models(PLM)have excelled in Natural Language Processing(NLP)tasks by leveraging large-scale training *** the scale of these models enhances performance significantly,introducing abilities like context learning that smaller models *** advancement in Large Language Models,exemplified by the development of ChatGPT,has made significant impacts both academically and industrially,capturing widespread societal *** survey provides an overview of the development and prospects from Large Language Models(LLM)to Large Multimodal Models(LMM).It first discusses the contributions and technological advancements of LLMs in the field of natural language processing,especially in text generation and language ***,it turns to the discussion of LMMs,which integrates various data modalities such as text,images,and sound,demonstrating advanced capabilities in understanding and generating cross-modal content,paving new pathways for the adaptability and flexibility of AI ***,the survey highlights the prospects of LMMs in terms of technological development and application potential,while also pointing out challenges in data integration,cross-modal understanding accuracy,providing a comprehensive perspective on the latest developments in this field.
作者:
Sundfeld, DanielTeodoro, GeorgeMelo, Alba C. M. A.
Faculty of Science & Tech. in Engineering Brasilia Brazil
Department of Computer Science Belo Horizonte Brazil
Department of Computer Science Brasilia Brazil
Multiple Sequence Alignment (MSA) is an important operation in Bioinformatics, used to simultaneously compare 3 or more sequences. The MSA problem was proven NP-Hard, so strategies have been proposed to reduce the sea...
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Intelligent Transportation Systems (ITS) seek to enhance traffic safety, mobility, and efficiency through advanced technologies. Priority assignment, a fundamental component of ITS, allocates priority to various types...
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Industrial process plants use emergency shutdown valves(ESDVs)as safety barriers to protect against hazardous events,bringing the plant to a safe state when potential danger is *** ESDVs are used extensively in offsho...
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Industrial process plants use emergency shutdown valves(ESDVs)as safety barriers to protect against hazardous events,bringing the plant to a safe state when potential danger is *** ESDVs are used extensively in offshore oil and gas processing plants and have been mandated in the design of such systems from national and international standards and *** paper has used actual ESDV operating data from four mid/late life oil and gas production platforms in the North Sea to research operational relationships that are of interest to those responsible for the technical management and operation of *** first of the two relationships is between the closure time(CT)of the ESDV and the time it remains in the open position,prior to the close *** has been hypothesised that the CT of the ESDV is affected by the length of time that it has been open prior to being closed(Time since the last stroke).In addition to the general analysis of the data series,two sub-categories were created to further investigate this possible relationship for CT and these are“above mean”and“below mean”.The correlations(Pearson's based)resulting from this analysis are in the“weak”and“very weak”*** second relationship investigated was the effect of very frequent closures to assess if this improves the *** operational records for six subjects were analysed to find closures that occurred within a 24 h period of each ***,no discriminating trend was apparent where CT was impacted positively or negatively by the frequent closure *** was concluded that the variance of ESDV closure time cannot be influenced by the technical management of the ESDV in terms of scheduling the operation of the ESDV.
作者:
Khadse, ShrikantGourshettiwar, PalashPawar, Adesh
Faculty of Engineering and Technology Wardha442001 India
Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Wardha442001 India
Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India
Meta-learning aims to create Artificial Intelligence (AI) systems that can adapt to new tasks and improve their performance over time without extensive retraining. The advent of meta-learning paradigms has fundamental...
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Random deployment and wide surveillance of a geographical area can be carried out in the Wireless Sensor Networks (WSN). A wireless sensor network is called a network with several sensor nodes that operate in wireless...
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We present a geometric model of the differential sensitivity of the fidelity error for state transfer in a spintronic network based on the relationship between a set of matrix operators. We show an explicit dependence...
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Cloud computing has taken over the high-performance distributed computing area,and it currently provides on-demand services and resource polling over the *** a result of constantly changing user service demand,the tas...
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Cloud computing has taken over the high-performance distributed computing area,and it currently provides on-demand services and resource polling over the *** a result of constantly changing user service demand,the task scheduling problem has emerged as a critical analytical topic in cloud *** primary goal of scheduling tasks is to distribute tasks to available processors to construct the shortest possible schedule without breaching precedence *** and schedules of tasks substantially influence system operation in a heterogeneous multiprocessor *** diverse processes inside the heuristic-based task scheduling method will result in varying makespan in the heterogeneous computing *** a result,an intelligent scheduling algorithm should efficiently determine the priority of every subtask based on the resources necessary to lower the *** research introduced a novel efficient scheduling task method in cloud computing systems based on the cooperation search algorithm to tackle an essential task and schedule a heterogeneous cloud computing *** basic idea of thismethod is to use the advantages of meta-heuristic algorithms to get the optimal *** assess our algorithm’s performance by running it through three scenarios with varying numbers of *** findings demonstrate that the suggested technique beats existingmethods NewGenetic Algorithm(NGA),Genetic Algorithm(GA),Whale Optimization Algorithm(WOA),Gravitational Search Algorithm(GSA),and Hybrid Heuristic and Genetic(HHG)by 7.9%,2.1%,8.8%,7.7%,3.4%respectively according to makespan.
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