作者：
Kumar, LalitSharma, Isha
Department of Computer Science and Engineering Ambala Haryana Mullana133207 India CodeQuotient
Department of Computer Science and Engineering
Operator precedence and associativity are fundamental concepts in the C programming language that govern the order of evaluation of expressions. Understanding these principles is essential for writing correct and effi...
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The Microservice Architecture (MSA) plays a pivotal role in contemporary e-business, promoting service independence, autonomy, and continual evolution in line with the principles of DevOps. However, the distributed na...
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Point clouds offer realistic 3D representations of objects and scenes at the expense of large data volumes. To represent such data compactly in real-world applications, Video-Based Point Cloud Compression (V-PCC) conv...
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Bot detection is considered a crucial security issue that is extensively analysed in various existingapproaches. Machine Learning is an efficient way of botnet attack detection. Bot detectionis the major issue faced b...
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Bot detection is considered a crucial security issue that is extensively analysed in various existingapproaches. Machine Learning is an efficient way of botnet attack detection. Bot detectionis the major issue faced by the existing system. This research concentrates on adopting a graphbasedfeature learning process to reduce feature dimensionality. The incoming samples arecorrectly classified and optimised using an Adaboost classifier with an improved grey wolfoptimiser (g-AGWO). The proposed IGWO optimisation approach is adopted to fulfil the multiconstraintissues related to bot detection and provide better local and global solutions (to satisfyexploration and exploitation). The extensive results show that the proposed g-AGWO model outperformsexisting approaches to reduce feature dimensionality, under-fitting/over-fitting andexecution time. The error rate prediction shows the feasibility of the given model to work over thechallenging environment. This model also works efficiently towards the unseen data to achievebetter generalization.
Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws...
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Machine learning(ML)is increasingly applied for medical image processing with appropriate learning *** applications include analyzing images of various organs,such as the brain,lung,eye,etc.,to identify specific flaws/diseases for *** primary concern of ML applications is the precise selection of flexible image features for pattern detection and region *** of the extracted image features are irrelevant and lead to an increase in computation ***,this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image *** process trains the learning paradigm using similarity and correlation-based features over different textural intensities and pixel *** similarity between the pixels over the various distribution patterns with high indexes is recommended for disease ***,the correlation based on intensity and distribution is analyzed to improve the feature selection ***,the more congruent pixels are sorted in the descending order of the selection,which identifies better regions than the ***,the learning paradigm is trained using intensity and region-based similarity to maximize the chances of ***,the probability of feature selection,regardless of the textures and medical image patterns,is *** process enhances the performance of ML applications for different medical image *** proposed method improves the accuracy,precision,and training rate by 13.19%,10.69%,and 11.06%,respectively,compared to other models for the selected *** mean error and selection time is also reduced by 12.56%and 13.56%,respectively,compared to the same models and dataset.
The increasing global incidence of glioma tumors has raised significant healthcare concerns due to their high mortality rates. Traditionally, tumor diagnosis relies on visual analysis of medical imaging and invasive b...
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Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed *** closed network is intended to share sensitive location-centric information from a source node to the ba...
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Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed *** closed network is intended to share sensitive location-centric information from a source node to the base station through efficient routing *** efficiency of the sensor node is energy bounded,acts as a concentrated area for most researchers to offer a solution for the early draining power of *** management plays a significant role in wireless sensor networks,which was obsessed with the factors like the reliability of the network,resource management,energy-efficient routing,and scalability of *** topology of the wireless sensor networks acts dri-ven factor for network efficiency which can be effectively maintained by perform-ing the clustering process *** solutions and clustering algorithms have been offered by various researchers,but the concern of reduced efficiency in the routing process and network management still *** research paper offers a hybrid algorithm composed of a memetic algorithm which is an enhanced version of a genetic algorithm integrated with the adaptive hill-climbing algorithm for performing energy-efficient clustering process in the wireless sensor *** memetic algorithm employs a local searching methodology to mitigate the premature convergence,while the adaptive hill-climbing algorithm is a local search algorithm that persistently migrates towards the increased elevation to determine the peak of the mountain(i.e.,)best cluster head in the wireless sensor *** proposed hybrid algorithm is compared with the state of art clus-tering algorithm to prove that the proposed algorithm outperforms in terms of a network life-time,energy consumption,throughput,etc.
Advancements in digital technologies make it easy to modify the content of digital images. Hence, ensuring digital images' integrity and authenticity is necessary to protect them against various attacks that manip...
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In today’s rapidly evolving landscape of communication technologies,ensuring the secure delivery of sensitive data has become an essential *** overcome these difficulties,different steganography and data encryption m...
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In today’s rapidly evolving landscape of communication technologies,ensuring the secure delivery of sensitive data has become an essential *** overcome these difficulties,different steganography and data encryption methods have been proposed by researchers to secure *** of the proposed steganography techniques achieve higher embedding capacities without compromising visual imperceptibility using LSB *** this work,we have an approach that utilizes a combinationofMost SignificantBit(MSB)matching andLeast Significant Bit(LSB)*** proposed algorithm divides confidential messages into pairs of bits and connects them with the MSBs of individual pixels using pair matching,enabling the storage of 6 bits in one pixel by modifying a maximum of three *** proposed technique is evaluated using embedding capacity and Peak Signal-to-Noise Ratio(PSNR)score,we compared our work with the Zakariya scheme the results showed a significant increase in data concealment *** achieved results of ourwork showthat our algorithmdemonstrates an improvement in hiding capacity from11%to 22%for different data samples while maintaining a minimumPeak Signal-to-Noise Ratio(PSNR)of 37 *** findings highlight the effectiveness and trustworthiness of the proposed algorithm in securing the communication process and maintaining visual integrity.
Smart home automation is protective and preventive measures that are taken to monitor elderly people in a non-intrusive manner using simple and pervasive sensors termed Ambient Assistive Living. The smart home produce...
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