Recent studies have revealed the vulnerability of Deep Neural Network (DNN) models to backdoor attacks. However, existing backdoor attacks arbitrarily set the trigger mask or use a randomly selected trigger, which res...
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LLMs have boosted progress in many AI applications. Recently, there were attempts to distill the vast knowledge of LLMs into information retrieval systems. Those distillation methods mostly use output probabilities of...
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Keyloggers are malicious software programs that record keystrokes of users without their consent or knowledge. They can steal sensitive information like credit card numbers and passwords. They pose a significant threa...
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DNA sequencing is a critical tool in genetics, helping to identify disease-causing mutations, predict disease risks, screen for genetic diseases, and monitor disease progression. By determining the order of nucleotide...
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ISBN:
(数字)9798331530389
ISBN:
(纸本)9798331530396
DNA sequencing is a critical tool in genetics, helping to identify disease-causing mutations, predict disease risks, screen for genetic diseases, and monitor disease progression. By determining the order of nucleotides that make up DNA, DNA sequencing allows for the identification of genetic information in specific DNA fragments, entire genomes, or complex micro-biomes. It has transformed disease diagnosis and treatment and has applications in biotechnology, forensic science, and evolutionary biology. Comparing DNA sequences helps researchers identify genetic disorders, cancers, and antibody repertoires, leading to tailored treatments for individual patients. However, the diagnosis of diseases using DNA requires an efficient algorithm due to the large number of nucleotides in DNA sequences. The hash-based approach, an optimal string pattern-matching algorithm, is used to identify relevant patterns in DNA sequences. This approach has shown significant improvement in speed compared to the single pattern matching approach for finding multiple string patterns and a parallelized approach for hash based using GPU is also proposed.
Pothole detection is the dominant research domain that aids in achieving less traffic as well as safe journeys. Multiple researches were conducted in this domain to attain improved research efficacy, but the research ...
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ISBN:
(数字)9798331522667
ISBN:
(纸本)9798331522674
Pothole detection is the dominant research domain that aids in achieving less traffic as well as safe journeys. Multiple researches were conducted in this domain to attain improved research efficacy, but the research models ended with certain drawbacks. The limitations addressed by conventional methods are high false positive rates, misclassification, and improper feature extraction. Thus, to address the described challenges and to attain high efficiency in detecting potholes, Fractional Taylored Crocuta Optimization enabled Deep Convolutional Neural Network (FTayCO-DCN) is proposed in the research. The research model integrates the FTayCO algorithm that works to attain optimal outcomes among all detected outcomes through the DCN. In addition, the algorithm ignores convergence as well as optimization issues, which in turn results in high detection accuracy. Moreover, the incorporation of feature extraction mechanisms elevates the research model to retain high performance. The experimental results demonstrate that the proposed FTayCO-DCN approach achieved superior performance reported in terms of metrics attaining an accuracy of 99.06%, sensitivity of 99.09%, and specificity of 99.04%, outperforming the other existing techniques.
Malicious domains pose a significant threat to internet security, with cyber-criminals exploiting the Domain Name System (DNS) to deceive users and host malicious content. The DNS services are very significant, and he...
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Food classification plays a vital role in areas such as food recognition, nutritional tracking, and culinary exploration. This study evaluates the effectiveness of three deep learning models—YOLOv8n, VGG19, and Incep...
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ISBN:
(数字)9798331515683
ISBN:
(纸本)9798331515690
Food classification plays a vital role in areas such as food recognition, nutritional tracking, and culinary exploration. This study evaluates the effectiveness of three deep learning models—YOLOv8n, VGG19, and InceptionV3—utilizing the In-dian Food Classification Dataset and Food-11. Metrics like accuracy, precision, recall, and F1 score indicate that InceptionV3 consistently delivers the highest accuracy, whereas YOLOv8n stands out for its real-time efficiency, making it well-suited for mobile or embedded systems. VGG19 provides a balanced performance but is limited by its larger model size and higher computational requirements. Statistical analysis (p ¡ 0.05) shows significant differences among the models, with confusion matrices revealing difficulties in classifying visually similar foods. The study suggests using InceptionV3 for tasks requiring precision, YOLOv8n for applications where speed is critical, and VGG19 for scenarios needing a balanced approach, while also proposing future research in cross-cultural food recognition, AI-driven food interaction, and personalized nutrition systems.
作者：
Lokesh, K.Baskar, M.Department of Computer Science and Engineering
School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamilnadu Chennai606203 India Department of Computing Technologies
School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamilnadu Chennai606203 India
Video surveillance continues to have difficulties with identifying the anomalies such as illegal activities and crimes despite the development of interactive multimedia anomaly detection systems. To address this issue...
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Video surveillance continues to have difficulties with identifying the anomalies such as illegal activities and crimes despite the development of interactive multimedia anomaly detection systems. To address this issue, an Optimized Interpretable Generalized Additive Neural Networks based Malicious Activity Detection with Video Surveillance (IGANN-MAD-VS-EOSSOA) is proposed in this paper. Initially, the input videos are collected from UCF-Crime and ShanghaiTech dataset. The collected video is fed to pre-processing for improving the quality of video, removing the noise and enhancing the clarity of image using Multiple Local Particle Filtering (MLPF). The pre-processed video is fed to the segmentation process. Here, the input videos are segmented into image using Maximum Entropy Scaled Super-pixels Segmentation (MESPS). Then the feature extraction is done by Synchro-Transient-Extracting Transform (STET) to extract the features, like color, texture, size, shape, and orientation. The extracted features are provided to the Interpretable Generalized Additive Neural Networks (IGANN) for classifying malicious activity, like Normal, Assault, Fighting, Shooting, Vandalism, Abuse and Accident. In general, IGANN does not adapt any optimization techniques for determining the optimal parameters to assure appropriate categorization. Hence, Elite opposite Sparrow Search Optimization Algorithm (EOSSOA) is proposed to enhance the weight parameter of IGANN for the detection of malicious activity with video surveillance. The proposed IGANN-MAD-VS-EOSSOA method is implemented in Python. The proposed technique attains 26.36%, 20.69% and 30.29% higher accuracy, 19.12%, 28.32%, and 27.84% higher precision when compared with the existing methods: Video anomaly detection scheme with deep convolutional and recurrent techniques (AD-CNN-VS), Toward trustworthy human suspicious activity detection from surveillance videos with deep learning (HSAD-SV-RNN), Deep learning-based real-world object dete
This research study proposes a unique deep learning classifier using palm hand’s principle lines extraction approach for the palmprint recognition system. A Deep Convolution Multifractal Analysis Model for Palmprint ...
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Certain rod-shaped bacteria may move over surfaces without the assistance of external appendages like flagella, cilia, or pili thanks to a process known as gliding motility. In this work, the dynamics of an undulating...
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