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Peanut maturity detection assessment using cross-layer multi-perception neural network based on hyperspectral sensory image feature observation

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 9393-9407页

作者： Balasubramaniyan, M. Navaneethan, C. School of Computer Science Engineering andInformation Systems VIT University Tamil Nadu Vellore India

Artificial intelligence has played a significant role in the expansion of the agriculture industry in recent times by evaluating data and making recommendations for better production. An automated method for determining significant information in seed quality analysis is the peanut maturity analysis in image processing through sensory images. The majority of the time, changes in picture intensity result in feature independence and precise maturity level determination. Therefore, agricultural precision in identifying essential features is low. To address this issue, we suggest employing a Cross-Layer Multi-Perception Neural Network (CLMPNN) for hyperspectral sensory image feature observation in order to determine the optimal assessment of peanut maturity in agriculture. The sensing unit first determines the angular cascade projection's (ACP) structural dependencies for the peanut pod structure. With the aid of color-intensive saturation, the entity projection of pod growth is found using the Slicing Fragment Segmentation (SFS) technique. This generates the various entity variations by integrating relational maturity and non-maturity findings with spectral values. Next, cross-layer multi-perception neural networks are trained with hyperspectral values optimized by LSTM to distinguish between mature and immature pods. In comparison to the other system, this one does exceptionally well in precision agriculture, with a 98.6 well recall rate, a 97.3% classification accuracy, and a 98.9% production accuracy. © 2024 - IOS Press. All rights reserved.

关键词： Oilseeds

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Optimized featured swarm convolutional neural network (OFSCNN) model based dialect recognition system for Bagri Rajasthani language

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International Journal of Information Technology (Singapore) 2025年第1期17卷 121-133页

作者： Kukana, Poonam Sharma, Pooja Bhardwaj, Neeru Department of Computer Science and Engineering University School of Engineering & amp Technology Rayat-Bahra University Punjab Mohali India Department of Computer Science and Engineering Chandigarh University Punjab Mohali India

The dialects of a language hold a significant place in speech processing (SP) applications. The objective of dialect identification is to categorize speech sample data into a specific dialect of a speaker's spoken language. A dialect recognition system must effectively distinguish between different dialects of a standard language, as they tend to possess many similarities. The dialect of a language is not a distinct characteristic, as it can be impacted by the utterer's birthplace. Researchers in the domain of automatic speech recognition (ASR) face difficulties in identifying the speech patterns unique to each dialect or language. The proposed work recognizes the dialects of the Bagri Rajasthani language from undefined expressions of speech. Rajasthani language is one of the eldest and most famous languages in the Indo-Aryan languages. It comprises the different dialects and for recognizing the dialects, it used dissimilar phases of acoustic and spectral characteristics of the speech signal (SS). The spectral and acoustic features of SSs are measured to design the system. As there is no specific speech dataset for Bagri dialects, the database is built, to verify the Bagri dialects of the Rajasthani language. To improve the accuracy rate, and error rate in recognizing the Bagri dialects, the acoustic and spectral characteristics of speech expressions are joined. To verify several Bagri dialects of the Rajasthani language, different simulations for classification and investigation are carried out i.e., OFSCNN model, GA-NN, etc. The outcomes are important and the accuracy of 96.95% for the OFSCNN model, 80.63% for GA-NN, and 93.45% for the SVM method is an achievement. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： CNN (convolutional neural network Dialects MFCC (Mel frequency co-efficient cepstral) OFSCNN (optimized featured swarm convolutional neural network) PSO (particle swarm optimization) Speech Dialect Recognition

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Safeguarding the Homeless Using ViTransformer in Violence Detection 3

Safeguarding the Homeless Using ViTransformer in Violence De...

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3rd IEEE International Conference on Intelligent Techniques in Control, Optimization and Signal Processing, INCOS 2024

作者： Bhuvaneswari, R. Sai Lasya, M. Natarajan, B. Tamilselvi, M. Geetha, K. Velammal, M. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India School of Computer Science and Engineering Vit University Tamilnadu Chennai India Roever Engineering College Department of Computer Science and Engineering Tamilnadu Perambalur India Karpagam College of Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798350361186

In recent years, the issue of homelessness has gained significant attention as societies grapple with finding solutions to alleviate the plight of those without stable shelter. To address this pressing concern, this research utilizes advanced video analysis techniques, specifically Vision Transformer, to detect the presence of individuals in homeless populations. By leveraging state-of-the-art technology, the research aims to not only identify individuals but also discern their actions and activities within the monitored environment. The methodology involves the initial detection of persons using Vi Transformer, an innovative deep learning model renowned for its accuracy in object recognition tasks. Once a person is successfully identified, this research further scrutinizes their activities, with a particular focus on detecting instances of conflict or fighting. This step holds immense potential or the safety and well-being of homeless individuals, as it enables the swift notification of nearby authorities or support services in cases of altercation. Crucially, this research delves into the performance metrics of both person detection and activity detection experiments, providing valuable insights into the efficacy of the Vi Transformer model in real-world applications. By assessing the system's accuracy, speed, and reliability, this study contributes to the development of proactive strategies for assisting homeless populations, emphasizing the importance of technology in addressing social challenges. © 2024 IEEE.

关键词： Object recognition

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Q-Cogni: An Integrated Causal Reinforcement Learning Framework

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6186-6195页

作者： Da Costa Cunha, Cristiano Liu, Wei French, Tim Mian, Ajmal University of Western Australia Department of Computer Science and Software Engineering PerthWA6009 Australia

We present Q-Cogni, an algorithmically integrated causal reinforcement learning framework that redesigns Q-Learning to improve the learning process with causal inference. Q-Cogni achieves improved policy quality and learning efficiency with a prelearned structural causal model of the environment, queried to guide the policy learning process with an understanding of cause-and-effect relationships in a state-action space. By doing so, we not only leverage the sample efficient techniques of reinforcement learning but also enable reasoning about a broader set of policies and bring higher degrees of interpretability to decisions made by the reinforcement learning agent. We apply Q-Cogni on vehicle routing problem (VRP) environments including a real-world dataset of taxis in New York City using the Taxi and Limousine Commission trip record data. We show Q-Cogni's capability to achieve an optimally guaranteed policy (total trip distance) in 76% of the cases when comparing to shortest-path-search methods and outperforming (shorter distances) state-of-the-art reinforcement learning algorithms in 66% of cases. Additionally, since Q-Cogni does not require a complete global map, we show that it can start efficiently routing with partial information and improve as more data is collected, such as traffic disruptions and changes in destination, making it ideal for deployment in real-world dynamic settings. © 2020 IEEE.

关键词： Adversarial machine learning

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Static video summarization with multi-objective constrained optimization

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Journal of Ambient Intelligence and Humanized Computing 2024年第4期15卷 2621-2639页

作者： Dhanushree, M. Priya, R. Aruna, P. Bhavani, R. Department of Computer Science and Engineering Annamalai University Annamalainagar Tamil Nadu Chidambaram608001 India

Video summarization is an emerging research field. In particular, static video summarization plays a major role in abstraction and indexing of video repositories. It extracts the vital events in a video such that it covers the entire content of the video. Frames having those important events are called keyframes which are eventually used in video indexing. It also helps in giving an abstract view of the video content such that the internet users are aware of the events present in the video before watching it completely. The proposed research work is focused on efficient static video summarization by extracting various visual features namely color, texture and shape features. These features are aggregated and clustered using a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. In order to produce good video summary by clustering, the parameters of DBSCAN algorithm are optimized by using a meta heuristic population based optimization called Artificial Algae Algorithm (AAA). The experimental results on two public datasets namely VSUMM and OVP dataset show that the proposed Static Video Summarization with Multi-objective Constrained Optimization (SVS_MCO) achieves better results when compared to existing methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Algae

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FLBC-IDS: a federated learning and blockchain-based intrusion detection system for secure IoT environments

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Multimedia Tools and Applications 2025年第17期84卷 17229-17251页

作者： Govindaram, Anitha A, Jegatheesan Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Thandalam Chennai India

The development of the Internet of Things (IoT) in multiple sectors has led to substantial security issues due to its decentralized structure and resource limits. To solve these problems, this paper proposes a novel technique called FLBC-IDS, which combines Horizontal Federated Learning (HFL), Hyperledger Blockchain, and EfficientNet to detect intrusions in IoT environments. The FLBC-IDS model uses HFL to enable secure and privacy-preserving model training across numerous IoT devices, resulting in decentralized data privacy and resource efficiency. The model's integration of Hyperledger Blockchain ensures tamper-resistant and transparent recording of model updates and agreements among participating IoT nodes, protecting the system's integrity. Furthermore, EfficientNet improves the model's robustness by effectively obtaining and categorizing features from network traffic data. On the CICIDS-2018 and CICIoT-2023 datasets, the FLBC-IDS model outperforms previous techniques in terms of accuracy, recall, F1-score, and precision. The FLBC-IDS model has an accuracy of 98.89%, recall of 98.044%, F1-score of 98.29%, and precision of 98.44%. The article analyzes the suggested model's merits and limitations, as well as future research directions for real-world deployment of IoT security solutions. Overall, the FLBC-IDS paradigm holds significant promise for improving IoT security and safeguarding IoT settings from infiltration attempts and cyber threats. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Blockchain

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Improving coronary heart disease prediction with real-life dataset: a stacked generalization framework with maximum clinical attributes and SMOTE balancing for imbalanced data

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Multimedia Tools and Applications 2024年第37期83卷 85139-85168页

作者： Dubey, Madhuri Tembhurne, Jitendra Makhijani, Richa Department of Computer Science and Engineering Indian Institute of Information Technology Maharashtra Nagpur441108 India

Heart disease increases the strain on the heart by reducing its ability to pump blood throughout the body, which can lead to heart attacks and strokes. Heart disease is becoming a global threat to the world due to people’s unhealthy lifestyles, prevalent stroke history, physical inactivity, and current medical background. In predictive analytics, many studies were proposed to get alerts about forthcoming heart disease based on various attributes. However, the performance metrics were good, but the model was trained with few features. This study aims to train the model with all the essential attributes for heart disease prediction on Framingham Heart Study (FHS) dataset. The dataset is pre-processed with IQR (Inter Quartile Range) outlier detection followed by data oversampling using Synthetic Minority Oversampling Technique (SMOTE). We proposed a stack generalization approach, wherein various machine learning classifiers, namely logistic regression, random forest, K-nearest neighbour, Naïve Bayes, support vector machine, XGBoost, and decision tree with optimized hyperparameter trained the model to offer the best learner for the prediction of Coronary Heart disease with improved performance. The proposed model is tested on the original imbalanced and SMOTE-balanced FHS dataset. It is observed that logistic regression on the original (imbalanced) FHS dataset provides 86.51% accuracy, while the support vector machine on the SMOTE (balanced) FHS dataset outperformed the other models with an accuracy of 93.07%. Also, the proposed approach of stacked generalization with cross-validation provided 97.2% accuracy on SMOTE (balanced) dataset, which is remarkable. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Anomaly detection

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A Novel Trustworthiness Measurement Model Based on Weight and User Feedback

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Chinese Journal of Electronics 2022年第4期31卷 612-625页

作者： ZHOU Wei MA Yanfang PAN Haiyu School of Computer Science and Technology Huaibei Normal University School of Computer Science and Information Engineering Changzhou Institute of Technology School of Computer Science and Information Security Guilin University of Electronic Technology

Software trustworthiness is an essential criterion for evaluating software quality. In componentbased software, different components play different roles and different users give different grades of trustworthiness after using the software. The two elements will both affect the trustworthiness of software. When the software quality is evaluated comprehensively, it is necessary to consider the weight of component and user feedback. According to different construction of components, the different trustworthiness measurement models are established based on the weight of components and user feedback. Algorithms of these trustworthiness measurement models are designed in order to obtain the corresponding trustworthiness measurement value automatically. The feasibility of these trustworthiness measurement models is demonstrated by a train ticket purchase system.

关键词： Component-based software Trustworthiness Weight User feedback Measurement

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Deep Learning Based Face Detection and Identification of Criminal Suspects

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computers, Materials & Continua 2023年第2期74卷 2331-2343页

作者： S.Sandhya A.Balasundaram Ayesha Shaik School of Computer Science and Engineering Vellore Institute of Technology(VIT)Chennai600127India School of Computer Science and Engineering Center for Cyber Physical SystemsVellore Institute of Technology(VIT)Chennai600127India

Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past *** of the most tedious tasks is to track a suspect once a crime is *** most of the crimes are committed by individuals who have a history of felonies,it is essential for a monitoring system that does not just detect the person’s face who has committed the crime,but also their ***,a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network(DNN)model which employs a Single Shot Multibox Detector for detection of face and an auto-encoder model in which the encoder part is used for matching the captured facial images with the criminals has been *** detection and extraction of the face in the image by face cropping,the captured face is then compared with the images in the *** comparison is performed by calculating the similarity value between each pair of images that are obtained by using the Cosine Similarity *** plotting the values in a graph to find the threshold value,we conclude that the confidence rate of the encoder model is 0.75 and above.

关键词： Deep learning opencv deep neural network single shot multi-box detector auto-encoder cosine similarity

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Ladder-Type Resonant Switched-Capacitor Converter Achieving 99.1% Peak Efficiency and 4750 w/in3Power Density for High-Current Application

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IEEE Transactions on Power Electronics 2024年第7期39卷 8040-8053页

作者： You, Dong-Ying Chiu, Huang-Jen Kuo, Shih-Hao National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan

A nonisolated fixed-ratio resonant switched-capacitor converter (RSCC) with high peak efficiency and high power density is encouraged as intermediate bus converter. With higher input voltages, stacked topologies achieve better component stress, but the relative current stress will be multiplied, e.g., ladder and Cockcroft-Walton topologies are not well suited for high-power high step-down ratio applications. Accordingly, in this article, a ladder-type RSCC is proposed to meet high current demands by combining symmetrical auxiliary resonant tanks with two interleaved phases. In addition to having a high-resolution scalability that satisfies any integer conversion ratio, the voltage stress on the flying capacitors does not increase significantly as the conversion ratio increases, making their capacitance minimally susceptible to component tolerances. Meanwhile, all mosfets are always well clamped at 1× output low-voltage level, which means that low-voltage devices are permitted to be used, resulting in a good factor of merit. By combining adaptive constant on-time with dead-time modulation, an effective duty cycle is optimized, and controlled current sharing is achieved, making multistage parallel expansion very flexible and efficient. To demonstrate the feasibility and practicality, a compact 3-to-1 hardware prototype was realized with a load current of up to 75 A, resulting in an excellent power density of 4750 W/in3. A peak efficiency of 99.1% and a full-load efficiency of 98.1% were also achieved. © 1986-2012 IEEE.

关键词： Topology

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