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Autism spectrum disorder based on squeezenet with fractional tasmanian rat swarm optimization

Multimedia Tools and Applications 2024年第41期83卷 89029-89054页

作者： Muppidi, Satish Anuradha, G. Valarmathi, K. Department of Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Rajam India Computer Science and Engineering Velagapudi Ramakrishna Siddhartha Engineering College Vijayawada Kanuru India Department of Computer Science and Engineering Panimalar Engineering College Bangalore Trunk Road Chennai Pooñamallee600123 India

A complicated neuro-developmental disorder called Autism Spectrum Disorder (ASD) is abnormal activities related to brain development. ASD generally affects the physical impression of the face as well as the growth of the brain in children. An early and proper medical diagnosis is essential for ASD affected children to enhance their quality of life. However, the clinical detection of ASD is a difficult task and time-consuming, hence it is essential to design an ASD detection approach for precise diagnosis of ASD children. In this research, an algorithmic approach called Fractional Tasmanian Rat Swarm Optimization driven SqueezeNet (FTRSO-SqueezeNet) is designed for the detection of ASD. The median filter and Region of Interest (RoI) extraction are used to de-noise the input image initially and extract a particular region from the filtered image. Later, the nub regions are extracted by choosing the optimal grid from the pre-processed image and using different feature extractors the features from the input images are determined. Finally, the detection of ASD is done by using SqueezeNet, which is trained using the FTRSO approach. The performance of the method is estimated, where the designed model achieved higher performance with an accuracy of 94.55%, sensitivity of 92.53%, and specificity of 95.22% than other prevailing approaches. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Rats

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Integrating AI and computer Vision for Effective Foreign Object Detection on Airport Runways 3

Integrating AI and Computer Vision for Effective Foreign Obj...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Annie Silviya, S.H. Kingsley, S. Agitha, W. Sriman, B. Priyadarshan, V.J. Mohana, V. Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Easwari Engineering College Department of Computer Science and Engineering Chennai India P.B. College of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350322842

The FOD-R Dataset is a collection of images that depict common types of foreign object debris (FOD) that can be found on runways or taxiways. The dataset has primarily been annotated using bounding boxes to facilitate object detection. However, the FOD-xR Dataset includes an additional feature - color codes - that provide information on the ease of removing the debris. Lightweight objects are marked in green, while heavy objects are marked in red. This centralized system simplifies coordination efforts, making it easier for organizations to manage FOD removal effectively. This, in turn, can have a positive impact on an organization's bottom line by minimizing delays and damage caused by FOD incidents. Overall, the FOD-R and FOD-xR Datasets are valuable resources for organizations looking to improve their FOD detection and removal processes. © 2023 IEEE.

关键词： Object detection

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Surface Defect Detection Algorithm for Strip Steel Based on Improved YOLOv7 Model

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第3期51卷 308-316页

作者： Wang, Zhu Liu, Weisheng the College of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China the College of Computer Science and Software Engineering the University of Science and Technology Liaoning CO Anshan114051 China

This research proposes a refined deep learning framework aimed at boosting the precision and efficacy of detecting surface imperfections in strip steel. This method integrates enhancement and simplification techniques inspired by the You Only Look Once version 7 (YOLOv7) detection method, resulting in significant enhancements in the model's accuracy, speed, and flexibility. The substitution of ELAN with Bottleneck Transformer 3 (BoT3) leads to improved accuracy and mean Average Precision (mAP) values, while also introducing a more lightweight network architecture. The incorporation of the Involution mechanism enhances the model's feature extraction capabilities, thereby improving its ability to recognize small targets through the utilization of local perceptual fields. The ASPP_CA architecture leverages a multi-scale feature fusion technique along with an attention mechanism to reduce model parameters and enhance inference speed. Furthermore, it extends the model's receptive field, allowing it to capture additional visual information. The enhanced algorithm, denoted as YOLOv7-IBA, demonstrates empirical results that underscore its superiority over the three current state-of-the-art detection techniques in identifying surface flaws on strip steel. The accuracy has been improved to 82.9%, representing a significant increase of 7.2% compared to the previous performance. Furthermore, the mean mAP value has experienced a 3.2% increase, reaching a total of 79.9%. Moreover, there has been a remarkable 8.8% improvement in efficiency. The adoption of this approach holds the potential to enhance both the precision and productivity of strip surface flaw detection, while also providing valuable methodological support for the advancement of other related disciplines. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Object detection

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An optimized deep network-based intermediate code generation for the mathematical expression

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Multimedia Tools and Applications 2025年第17期84卷 18327-18350页

作者： Sharada, H.N. Anami, Basavaraj Allagi, Shridhar Department of Computer Science & Engineering SDM College of Engineering and Technology Karnataka Dharwad580002 India Department of Computer Science & Engineering KLE Institute of Technology Karnataka Hubli580030 India

In recent times, the system's mathematical expression and operation have gained greater reach in engineering and mathematics. It is vital to solving more complex expressions and equations in a short time. The most existing methodology was degraded in the recognition performance and provided inaccurate output. Therefore, a novel Squirrel-based Spiking Neural Model (SbSNM) was presented for the intermediate code generation from the mathematical expression relation data. Initially, the input ME relation data was preprocessed to remove noise and tokenized by the spiking neural features. The tokenized expressions were arranged into a syntax tree structure, and then intermediate code was generated by the squirrel functions. Finally, the differentiation was calculated and given as output. Subsequently, the performance parameters were evaluated for the presented SbSNM. The overall accuracy obtained by the presented model is 99.25% which is higher than the other existing techniques and also brought a higher precision and recall rate and f1-score. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Neural networks

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Bridging the gap: combining expert guidance and self-learned knowledge for efficient and robust autonomous navigation in complex settings

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Journal of Ambient Intelligence and Humanized Computing 2025年第2期16卷 425-441页

作者： Kumar, Dharmendra Computer Science and Engineering United College of Engineering and Research Naini Uttar Pradesh Prayagraj211010 India

Autonomous navigation in dynamic environments poses significant challenges due to complex agent–environment interactions, real-time decision-making, and adaptability requirements. We propose the Reinforcement-Imitation Learning for Autonomous Navigation (RILAN) algorithm, which integrates reinforcement learning, imitation learning, and uncertainty estimation for robust and adaptable navigation. RILAN combines the strengths of expert demonstrations and self-learned knowledge, overcoming individual limitations. The modular RILAN architecture comprises an imitation learning module, reinforcement learning module, and an uncertainty-aware policy selector, enabling efficient information processing and dynamic policy selection. Extensive experiments compare RILAN to state-of-the-art algorithms, including E2E-IL, CIL, DQN, and PPO. Results show RILAN outperforms these algorithms in success rate, average time to goal, collision rate, and off-road rate, demonstrating the proposed approach’s efficacy for robust autonomous navigation in applications like self-driving vehicles and robotics. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Contrastive Learning

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An improved multi-scale convolutional neural network with gated recurrent neural network model for protein secondary structure prediction

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Neural Computing and Applications 2024年第24期36卷 15063-15074页

作者： Bongirwar, Vrushali Mokhade, A.S. Department of Computer Science and Engineering Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Computer Science and Engineering Visvesvaraya National Institute of Technology Nagpur India

Protein structure prediction is one of the main research areas in the field of Bio-informatics. The importance of proteins in drug design attracts researchers for finding the accurate tertiary structure of the protein which is dependent on its secondary structure. In this paper, we focus on improving the accuracy of protein secondary structure prediction. To do so, a Multi-scale convolutional neural network with a Gated recurrent neural network (MCNN-GRNN) is proposed. The novel amino acid encoding method along with layered convolutional neural network and Gated recurrent neural network blocks helps to retrieve local and global relationships between features, which in turn effectively classify the input protein sequence into 3 and 8 states. We have evaluated our algorithm on CullPDB, CB513, PDB25, CASP10, CASP11, CASP12, CASP13, and CASP14 datasets. We have compared our algorithm with different state-of-the-art algorithms like DCNN-SS, DCRNN, MUFOLD-SS, DLBLS_SS, and CGAN-PSSP. The Q3 accuracy of the proposed algorithm is 82–87% and Q8 accuracy is 69–77% on different datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Recurrent neural networks

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Impact of oxymoron features and deep learning techniques in the detection of sarcastic contents

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9197-9207页

作者： Seethappan, K. Premalatha, K. Department of Computer Science and Engineering University College of Engineering Tamilnadu Ramanathapuram India Department of Computer Science and Engineering Bannari Amman Institute of Technology Tamilnadu Sathyamangalam India

Even though various features have been investigated in the detection of figurative language, oxymoron features have not been considered in the classification of sarcastic content. The main objective of this work is to present a system that can automatically classify sarcastic phrases in multi-domain data. This multi-domain dataset consisting of 67850 sarcastic and non-sarcastic data is collected from various websites to identify sarcastic or non-sarcastic utterances. Multiple approaches are examined in this work to improve sarcasm identification: 1. A Combination of fasttext embedding, syntactic, semantic, lexical n-gram, and oxymoron features 2. TF-IDF feature weighting scheme 3. Three machine learning algorithms (SVM, Multinomial Naïve Bayes, and Random Forest), three deep learning algorithms (CNN, LSTM, MLP), and one ensemble model (CNN + LSTM) The CNN + LSTM model achieves a Precision of 91.32%, Recall of 92.85%, F-Score of 92.08%, accuracy of 92.01%, and Kappa of 0.84 by combining the fasttext embedding, bigram, syntactic, semantic, and oxymoron features with TF-IDF method. These experimental results show CNN + LSTM with a combination of all features outperforms the other algorithms in classifying the sarcasm in both datasets. The sarcasm classification performance of our dataset and another sarcasm news dataset was compared while applying the above model. © 2024 - IOS Press. All rights reserved.

关键词： Syntactics

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Efficient Heuristic Replication Techniques for High Data Availability in Cloud

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computer Systems science & engineering 2023年第6期45卷 3151-3164页

作者： H.L.Chandrakala R.Loganathan Department of Computer Science and Engineering School of EngineeringPresidency UniversityIndia Department of Computer Science and Engineering HKBK College of EngineeringIndia

Most social networks allow connections amongst many people based on shared *** networks have to offer shared data like videos,photos with minimum latency to the group,which could be challenging as the storage cost has to be minimized and hence entire data replication is not a *** replication of data across a network of read-intensive can potentially lead to increased savings in cost and energy and reduce the end-user’s response *** simple and adaptive replication strategies exist,the solution is non-deter-ministic;the replicas of the data need to be optimized to the data usability,perfor-mance,and stability of the application *** resolve the non-deterministic issue of replication,metaheuristics are *** this work,Harmony Search and Tabu Search algorithms are used optimizing the replication process.A novel Har-mony-Tabu search is proposed for effective placement and replication of *** on large datasets show the effectiveness of the proposed *** is seen that the bandwidth saving for proposed harmony-Tabu replication per-forms better in the range of 3.57%to 18.18%for varying number of cloud data-centers when compared to simple replication,Tabu replication and Harmony replication algorithm.

关键词： Cloud computing data replication bandwidth saving Tabu search Harmony search hybrid Harmony-Tabu search

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Self-distillation framework for improving fake speech detection in the domain variability scenario

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Neural Computing and Applications 2025年第5期37卷 3111-3127页

作者： Samhita, V. Viju, Vaishnav Bharathi, B. Department of Computer Science Engineering Sri Sivasubramaniya Nadar College of Engineering Tamil Nadu Chennai India

Robust fake speech detection systems are crucial in an era where audio recordings can be easily altered or developed due to advancements in technology. The potential impact of this technology could be devastating due to its susceptibility to misuse. It can lead to political and economic instability, severe financial repercussions, the spread of misinformation, defamation, and security breaches in ASV systems and facilitate theft and fraud. Further, existing detection systems face a key issue;most state-of-the-art systems only provide results on the corpus they are trained on but fail in the domain variability scenario. The goal of this study is to enable a system to generalize across various domains, enhancing its reliability in real-world scenarios and thereby solidifying the authenticity of speech, making speech-based systems more dependable. All three audio spoofing scenarios are considered, logical access-based (LA) attacks including TTS and voice conversion (VC) techniques, spoofing attacks generated in real physical space, namely physical access (PA) attacks (replay, mimicry) and advanced deepfake technologies. The proposed system put forth in this study achieves commendable performance on three diverse test datasets. Notably, EER of 0.286, 0.337 and 0.371 is achieved on the In-The-Wild dataset with the proposed system implemented on ResNet, ECANet and SENet. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Audio recordings

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REAL-TIME GLUCOSE MONITORING SYSTEM AND DIETARY RECOMMENDATIONS FOR DIABETES MELLITUS USING FOG COMPUTING

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2025年第5期84卷 55-67页

作者： Sathish, N. Elangovan, D. Nagalakshmi, R. Suresh, G. Chitra, Devi D. Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Information Technology Saveetha Engineering College Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Artificial Intelligence and Data Science Kings Engineering College Sriperumbudur India Department of Computer Science And Engineering S.A. Engineering College Chennai India

This systematic review gave special attention to diabetes and the advancements in food and nutrition needed to prevent or manage diabetes in all its forms. There are two main forms of diabetes mellitus: Type 1 (T1D) and Type 2 (T2D). The loss of beta cells brought on by an autoimmune reaction causes the pancreas to be unable to produce enough insulin, which leads to Type 1 diabetes. A deficiency of insulin could arise as the illness worsens. The most frequent cause is a combination of excessive body weight and inadequate exercise. Currently, 90–95% of patients have Type 2 diabetes mellitus (T2DM). Age-specific normal blood sugar ranges exist and knowing them is crucial for determining a target blood sugar level that is both healthy and appropriate. The main takeaway is that diet regimens ought to be tailored to the patient’s requirements and should account for their capacity for change. Carbohydrates are the nutrients that have the biggest impact on the rise in blood glucose. In this paper, we have proposed a novel method for glucose monitoring and diet recommendations for diabetic patients using fog computing. A paradigm known as fog computing makes use of the benefits that come from using both cloud and edge devices to support modern computing systems by offering high-quality services, cutting latency, facilitating mobility, supporting multi-tenancy, and many other features. © 2025 by Begell House, Inc.

关键词： Fog computing

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