Network-Computation in Neural Systems最新文献

This research introduces a revolutionary machinet learning algorithm-based quality estimation and grading system. The suggested work is divided into four main parts: Ppre-processing, neutroscopic model transformation, Feature Extraction, and Grading. The raw images are first pre-processed by following five major stages: read, resize, noise removal, contrast enhancement via CLAHE, and Smoothing via filtering. The pre-processed images are then converted into a neutrosophic domain for more effective mango grading. The image is processed under a new Geometric Mean based neutrosophic approach to transforming it into the neutrosophic domain. Finally, the prediction of TSS for the different chilling conditions is done by Improved Deep Belief Network (IDBN) and based on this; the grading of mango is done automatically as the model is already trained with it. Here, the prediction of TSS is carried out under the consideration of SSC, firmness, and TAC. A comparison between the proposed and traditional methods is carried out to confirm the efficacy of various metrics.

Cardiovascular diseases (CVD) represent a significant global health challenge, often remaining undetected until severe cardiac events, such as heart attacks or strokes, occur. In regions like Qatar, research focused on non-invasive CVD identification methods, such as retinal imaging and dual-energy X-ray absorptiometry (DXA), is limited. This study presents a groundbreaking system known as Multi-Modal Artificial Intelligence for Cardiovascular Disease (M2AI-CVD), designed to provide highly accurate predictions of CVD. The M2AI-CVD framework employs a four-fold methodology: First, it rigorously evaluates image quality and processes lower-quality images for further analysis. Subsequently, it uses the Entropy-based Fuzzy C Means (EnFCM) algorithm for precise image segmentation. The Multi-Modal Boltzmann Machine (MMBM) is then employed to extract relevant features from various data modalities, while the Genetic Algorithm (GA) selects the most informative features. Finally, a ZFNet Convolutional Neural Network (ZFNetCNN) classifies images, effectively distinguishing between CVD and Non-CVD cases. The research's culmination, tested across five distinct datasets, yields outstanding results, with an accuracy of 95.89%, sensitivity of 96.89%, and specificity of 98.7%. This multi-modal AI approach offers a promising solution for the accurate and early detection of cardiovascular diseases, significantly improving the prospects of timely intervention and improved patient outcomes in the realm of cardiovascular health.

This paper presents a non-parametric identification scheme for a class of uncertain switched nonlinear systems based on continuous-time neural networks. This scheme is based on a continuous neural network identifier. This adaptive identifier guaranteed the convergence of the identification errors to a small vicinity of the origin. The convergence of the identification error was determined by the Lyapunov theory supported by a practical stability variation for switched systems. The same stability analysis generated the learning laws that adjust the identifier structure. The upper bound of the convergence region was characterized in terms of uncertainties and noises affecting the switched system. A second finite-time convergence learning law was also developed to describe an alternative way of forcing the identification error's stability. The study presented in this paper described a formal technique for analysing the application of adaptive identifiers based on continuous neural networks for uncertain switched systems. The identifier was tested for two basic problems: a simple mechanical system and a switched representation of the human gait model. In both cases, accurate results for the identification problem were achieved.

This research introduces an innovative solution addressing the challenge of user authentication in cloud-based systems, emphasizing heightened security and privacy. The proposed system integrates multimodal biometrics, deep learning (Instance-based learning-based DetectNet-(IL-DN), privacy-preserving techniques, and blockchain technology. Motivated by the escalating need for robust authentication methods in the face of evolving cyber threats, the research aims to overcome the struggle between accuracy and user privacy inherent in current authentication methods. The proposed system swiftly and accurately identifies users using multimodal biometric data through IL-DN. To address privacy concerns, advanced techniques are employed to encode biometric data, ensuring user privacy. Additionally, the system utilizes blockchain technology to establish a decentralized, tamper-proof, and transparent authentication system. This is reinforced by smart contracts and an enhanced Proof of Work (PoW) mechanism. The research rigorously evaluates performance metrics, encompassing authentication accuracy, privacy preservation, security, and resource utilization, offering a comprehensive solution for secure and privacy-enhanced user authentication in cloud-based environments. This work significantly contributes to filling the existing research gap in this critical domain.

The rapid advancements in Agriculture 4.0 have led to the development of the continuous monitoring of the soil parameters and recommend crops based on soil fertility to improve crop yield. Accordingly, the soil parameters, such as pH, nitrogen, phosphorous, potassium, and soil moisture are exploited for irrigation control, followed by the crop recommendation of the agricultural field. The smart irrigation control is performed utilizing the Interactive guide optimizer-Deep Convolutional Neural Network (Interactive guide optimizer-DCNN), which supports the decision-making regarding the soil nutrients. Specifically, the Interactive guide optimizer-DCNN classifier is designed to replace the standard ADAM algorithm through the modeled interactive guide optimizer, which exhibits alertness and guiding characters from the nature-inspired dog and cat population. In addition, the data is down-sampled to reduce redundancy and preserve important information to improve computing performance. The designed model attains an accuracy of 93.11 % in predicting the minerals, pH value, and soil moisture thereby, exhibiting a higher recommendation accuracy of 97.12% when the model training is fixed at 90%. Further, the developed model attained the F-score, specificity, sensitivity, and accuracy values of 90.30%, 92.12%, 89.56%, and 86.36% with k-fold 10 in predicting the minerals that revealed the efficacy of the model.

Numerous studies have been conducted in an attempt to preserve cloud privacy, yet the majority of cutting-edge solutions fall short when it comes to handling sensitive data. This research proposes a "privacy preservation model in the cloud environment". The four stages of recommended security preservation methodology are "identification of sensitive data, generation of an optimal tuned key, suggested data sanitization, and data restoration". Initially, owner's data enters the Sensitive data identification process. The sensitive information in the input (owner's data) is identified via Augmented Dynamic Itemset Counting (ADIC) based Associative Rule Mining Model. Subsequently, the identified sensitive data are sanitized via the newly created tuned key. The generated tuned key is formulated with new fourfold objective-hybrid optimization approach-based deep learning approach. The optimally tuned key is generated with LSTM on the basis of fourfold objectives and the new hybrid MUAOA. The created keys, as well as generated sensitive rules, are fed into the deep learning model. The MUAOA technique is a conceptual blend of standard AOA and CMBO, respectively. As a result, unauthorized people will be unable to access information. Finally, comparative evaluation is undergone and proposed LSTM+MUAOA has achieved higher values on privacy about 5.21 compared to other existing models.

Effective project planning and management in the global software development landscape relies on addressing major issues like cost estimation and effort allocation. Timely estimation of software development is a critical focus in software engineering research. With the industry increasingly relying on diverse teams worldwide, accurate estimation becomes vital. Software size serves as a common measure for costs and schedules, but advanced estimation methods consider various variables, such as project purpose, personnel expertise, time and efficiency constraints, and technology requirements. Estimating software costs involve significant financial and strategic commitments, making it crucial to address complexity and versatility related to cost drivers. To achieve enhanced accuracy and convergence, we employ the cuckoo algorithm in our proposed NFDLNN (Neuro Fuzzy Logic and Deep Learning Neural Networks) model. Through extensive validation with industrial project data, using Function Point Analysis as the algorithmic models, our NFA model demonstrates high accuracy in software cost approximation, outperforming existing methods insights of MRE of 3.33, BRE of 0.13, and PI of 74.48. Our research contributes to improved project planning and decision-making processes in global software development endeavours.

The Internet of Things (IoT) is a network that connects various hardware, software, data storage, and applications. These interconnected devices provide services to businesses and can potentially serve as entry points for cyber-attacks. The privacy of IoT devices is increasingly vulnerable, particularly to threats like viruses and illegal software distribution lead to the theft of critical information. Ant Colony-Optimized Artificial Neural-Adaptive Tensorflow (ACO-ANT) technique is proposed to detect malicious software illicitly disseminated through the IoT. To emphasize the significance of each token in source duplicate data, the noise data undergoes processing using tokenization and weighted attribute techniques. Deep learning (DL) methods are then employed to identify source code duplication. Also the Multi-Objective Recurrent Neural Network (M-RNN) is used to identify suspicious activities within an IoT environment. The performance of proposed technique is examined using Loss, accuracy, F measure, precision to identify its efficiency. The experimental outcomes demonstrate that the proposed method ACO-ANT on Malimg dataset provides 12.35%, 14.75%, 11.84% higher precision and 10.95%, 15.78%, 13.89% higher f-measure compared to the existing methods. Further, leveraging block chain for malware detection is a promising direction for future research the fact that could enhance the security of IoT and identify malware threats.

Plant diseases pose a significant threat to agricultural productivity worldwide. Convolutional neural networks (CNNs) have achieved state-of-the-art performances on several plant disease detection tasks. However, the manual development of CNN models using an exhaustive approach is a resource-intensive task. Neural Architecture Search (NAS) has emerged as an innovative paradigm that seeks to automate model generation procedures without human intervention. However, the application of NAS in plant disease detection has received limited attention. In this work, we propose a two-stage meta-learning-based neural architecture search system (ML NAS) to automate the generation of CNN models for unseen plant disease detection tasks. The first stage recommends the most suitable benchmark models for unseen plant disease detection tasks based on the prior evaluations of benchmark models on existing plant disease datasets. In the second stage, the proposed NAS operators are employed to optimize the recommended model for the target task. The experimental results showed that the MLNAS system's model outperformed state-of-the-art models on the fruit disease dataset, achieving an accuracy of 99.61%. Furthermore, the MLNAS-generated model outperformed the Progressive NAS model on the 8-class plant disease dataset, achieving an accuracy of 99.8%. Hence, the proposed MLNAS system facilitates faster model development with reduced computational costs.