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convolution neural network-AlexNet with gazelle optimization algorithm-based software defect prediction

KNOWLEDGE AND INFORMATION SYSTEMS 2025年 1-22页

作者： Devi, S. V. Gayetri Ranjith, V. G. Ramani, P. Kavitha, A. Aalim Muhammed Salegh Coll Engn Dept Artificial Intelligence & Data Sci Chennai Tamil Nadu India Noorul Islam Ctr Higher Educ Dept Comp Sci & Engn Kumarcoil Tamil Nadu India SRM Inst Sci & Technol Dept Elect & Commun Engn Chennai 600089 India K Ramakrishnan Coll Technol Dept Elect & Commun Engn Trichy Tamil Nadu India

Nowadays, the size and complexity of software systems have increased dramatically. Software defects are very challenging to prevent because of these characteristics. Therefore, developers may be able to better allocate their limited resources by predicting the number of defects in software modules automatically. There are various approaches presented for identifying and fixing such problems, but none of these give sufficient results. To address these, this paper proposes convolution neural network-AlexNet with gazelle optimization algorithm-based software defect prediction (SWDP-CNN-AlexNet-GAOA). Here, NASA software defect prediction dataset is used. The feature normalization ensures that all features contribute equally to the model. Without normalization, features with larger numerical values would dominate the learning process. Then, software defects are predicted using convolution neural network (CNN)-AlexNet. Finally, gazelle optimization algorithm (GAOA) is proposed to optimize the parameters of CNN-AlexNet. Simulation proves that the SWDP-CNN-AlexNet-GAOA method outperforms existing models. The proposed SWDP-CNN-AlexNet-GAOA approach attains 3.88%, 5.75%, and 4.94% better accuracy and 6.25%, 5.91%, and 11.28% better F-measure compared with the existing methods, like software defect prediction using enhanced CNN (SWDP-EN-CNN), software defect prediction using hybrid swarm intelligence and deep learning (SWDP-HS-DL), and software defect prediction under ant colony optimization (SWDP-ACO), respectively.

关键词： convolution neural network Deep learning Feature selection Gazelle optimization algorithm neural network Software testing Software defect prediction

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A combined retrieval and prediction convolution neural network for oceanic primary production

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2025年第7期46卷 2866-2887页

作者： Ping, Bo Meng, Yunshan Su, Fenzhen Xue, Cunjin Tianjin Univ Inst Surface Earth Syst Sci Sch Earth Syst Sci Tianjin Peoples R China Natl Marine Data & Informat Serv Tianjin Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Int Res Ctr Big Data Sustainable Dev Goals Beijing Peoples R China

Oceanic primary production (OPP) is a key parameter for oceanic ecosystem and global carbon cycles. Currently, satellite technique has become an effective way to acquire time-series large-scale OPP values based on various retrieval models. In addition, OPP prediction based on historical satellite observations is also a significant topic for climate change. However, retrieval and prediction are commonly deemed as two separate processes, which may introduce unnecessary errors due to error propagation. Hence, in this study, a combined OPP retrieval and prediction model based on convolution neural network called CRP-OPP is proposed and validated in two experimental regions. There are two parts in the CRP-OPP model: feature extraction and object prediction. In the former part, four cascade convolutional patches are used to extract helpful information from the input oceanic parameters;and then an encoder-decoder structure is applied in the latter part to fulfil the OPP prediction. Compared with the other three machine learning methods, the proposed model can effectively forecast the OPP values at the prediction date with higher predicted accuracies. In addition, the performance of the CRP-OPP model is less affected by the dimension of input time-series data, so when the historical data used for prediction are not sufficient, the CRP-OPP model can still acquire satisfactory accuracy. Moreover, the spatial differences of OPP values may affect the predicted errors of the CRP-OPP model. Finally, even though all input oceanic parameters contribute to the OPP prediction, sea surface temperature seems to be the most significant one in the two experimental regions.

关键词： Oceanic primary production (OPP) convolution neural network OPP retrieval OPP prediction

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Ant Colony Optimized convolution neural network-Long Short Term Memory Based Energy Conservation in Variation-Tolerant Near-Threshold Processor

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2025年第2期32卷 620-627页

作者： Thiruvengadam, C. Maashi, Mashael Alsamri, Jamal Gundaala, Raghu Anjalai Ammal Mahalingam Engn Coll Dept Elect & Commun Engn Thiruvarur 614403 Tamil Nadu India King Saud Univ Coll Comp & Informat Sci Dept Software Engn POB 103786 Riyadh 11543 Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Engn Dept Biomed Engn Riyadh Saudi Arabia Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept ECE Chennai Tamil Nadu India

When considering the reduction of profit margins and improvement of energy efficiency in processors operating at near-threshold and sub-threshold levels, exploring timing error resilience might be seen as a potentially profitable option. Near-threshold (NT) circuit design is increasingly used for building energy-efficient digital circuits. One drawback was a drop in performance due to a reduction in driving current. To maximize energy economy and minimize performance deterioration, some studies advocate nontraditional (NT) chip multiprocessors. The creation of energy-efficient artificial intelligence technologies has gained popularity recently. Graphics processing units and generalpurpose CPUs could not match the goal of efficiency and performance. A semiconductor with many processing units was created to do this. This goal was achieved by engineering the hardware with unique features. Therefore, this study constructs an ACO_CNN+LSTM (Ant Colony Optimised convolution neural network-Long Short Term Memory) neural network to achieve lower power consumption without compromising performance. In this method, Ant Colony Optimization (ACO) is used to explore and select optimal configurations of CNN and LSTM architectures, minimizing energy usage through efficient layer selection, filter size adjustment, and neuron pruning. By incorporating Dynamic Voltage and Frequency Scaling (DVFS) and operating at near-threshold voltages (NTV) - where the supply voltage is just above the transistor threshold - power consumption is further reduced, as the processor can function at lower voltage levels while tolerating variations in performance. This hybrid ACO-driven optimization enhances both CNN feature extraction and LSTM's temporal processing by selecting the most energy-efficient network parameters. As a result, the proposed ACO_CNN+LSTM achieves 12.73% of energy consumption, 9.46 sec of latency, 98.45% of normalized frequency, 22.64% of error rate and 31.67% of processor utilizatio

关键词： chip processor convolution neural network energy conservation long short term memory near-threshold optimization

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Improved convolution neural network integrating attention based deep sparse auto encoder for network intrusion detection

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APPLIED INTELLIGENCE 2025年第2期55卷 1-17页

作者： Geng, Zhiqiang Li, Xueming Ma, Bo Han, Yongming Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Minist Educ China Engn Res Ctr Intelligent PSE Beijing 100029 Peoples R China Beijing Univ Chem Technol Coll Mech & Elect Engn Beijing 100029 Peoples R China

network intrusion detection (NID) is seen as a pivotal technology in the network security which can detect malicious threats occurring in the network and lend stabilized services for expanding the network environments. However, network-based Intrusion Detection Systems (NIDSs) are not sensitive to infrequent intrusions features and tend to have high misjudgment rate on imbalanced datasets, which lead to obvious defects for detecting minority classes of intrusion. Therefore, a novel neoteric NID methodology predicated upon an optimized convolutional neural network (CNN) integrating Attention based Deep Sparse Auto Encoder (ADSAE) (ADSAE-CNN) is put forward. The data expanding method based on the ADSAE model integrates attention mechanisms with deep stacked autoencoders to expand intrusion records of minority classes in data preprocessing, so as to balance the data distribution of intrusion detection datasets, improve the sensitivity of the detection model to the intrusion of few categories, and enhance the monitoring of the intrusion by the system. Meanwhile, the ADSAE can encode and transform the intrusion data to ameliorate the feature extraction capability of convolutional layers of the proposed ADSAE-CNN for detecting and classifying different intrusions classes. Finally, the ADSAE-CNN methodology is devoted to the network intrusion detection of two experiments on UNSW-NB15 and CSE-CIC-IDS2018 datasets and achieves the total precision of detection 89.1% on UNSW-NB15 and 94.20% on CSE-CIC-IDS2018, which can lead to considerably elevate the detection rate of minority intrusions and means significant effectiveness on multi-class network intrusion detections.

关键词： Deep autoencoder convolution neural network network intrusion detection Imbalance dataset Data expanding

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Offline Efficient Signature Authentication Using Octave convolution neural network

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2025年 1-16页

作者： Gutub, Adnan Altalhi, Sahar Ghazwani, Budur Umm Al Qura Univ Coll Comp CyberSecur Dept Mecca Saudi Arabia Taif Univ Coll Comp & Informat Technol Informat Technol Dept Taif Saudi Arabia

Handwritten signature verification aims to separate the genuine signature of an individual from forgeries for security reasons. Handwritten signature verification is the process of comparing the image of the questioned signature to the reference signatures of an individual with a predefined threshold to decide if it is considered genuine or forged signature. The task could be done in two ways: online and offline systems. Offline signature verification is more challenging than online systems due to being not easy to distinguish in skilled forgeries processes, as offline confirmation involve fewer features to aid its classification model. Modern approaches include deep learning (DL) algorithms as specified machine learning is trailed to be efficient in the signature image classification task. Octave convolution (OctConv) overcame limitations and achieved better results in terms of accuracy compared to the well-known convolution neural network (CNN). In this paper, we propose to utilize OctConv to build classification model with the purpose of separating genuine and forged signatures. The study revised different architectures using CNN adjusted as baseline models to be used for fair comparisons of OctConv to others. The work different popular signature datasets used have been compared and evaluated, i.e., consisting standard CEDAR, UTSig, BHSig260-Hindi, and BHSig260-Bengali. Interestingly, our OctConv-based model achieved the best competitive results compared to all four baseline model datasets, remarking competitive results among state-of-the-art models including CNN and capsule network with accuracy of high attractive percentages. The work showed attractive modeling strategy to verify signatures needed for today's cybersecurity authentication applications.

关键词： Cyber authentication Signature verification Machine learning convolution neural network Deep learning Octave convolution

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Enhanced super-resolution generative adversarial network augmented convolution neural network for pneumonia prognosis in India: promising health policy implications

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2025年 1-13页

作者： Kumar, Tapan Ujjwal, R. L. Guru Gobind Singh Indraprastha Univ USIC&T Golf Course RdSect 16 C Delhi 110078 India

Pneumonia is one of the leading causes of death globally. The healthcare industry is revolutionizing with Artificial Intelligence (AI) and data analytics applications. However, healthcare professionals are still struggling with pneumonia due to the unavailability of efficient AI-based assessments and the lack of diverse datasets for training AI models. This problem must be addressed to forecast early and accurately detect pneumonia. Analysis of chest X-rays is one effective solution, but they require trained healthcare professionals, who are less available in rural areas in India. The objective of this research is to propose a novel approach that integrates an Enhanced Super-Resolution Generative Adversarial network with a convolution neural network for early detection and prognosis of Pneumonia. This paper will also highlight two significant gaps: the lack of high-resolution images and the unavailability of diverse datasets to train AI models. The proposed hybrid model has been applied to 11,000 Chest X-ray images collected from various radiology centres and labs in India. In this model, high-resolution images generated by the Enhanced Super-Resolution Generative Adversarial network are used as input for further classification by the convolutional neural network. The model outperformed the traditional model, and performance was evaluated based on average accuracy, validation accuracy, and validation loss, with results of 91.53 percent, 87.98 percent, and 10.25 percent, respectively. The model successfully addresses the gap identified. The collected dataset has also been evaluated against three pre-trained deep learning models, VGG16, ResNet, and DenseNet, achieving an average accuracy of 0.79, 0.62, and 0.58 without fine-tuning. The result has implications for the healthcare industry and the government of India in policy-making. It provides precise suggestions for stakeholders or researchers, which will be key for launching further research in this uncharted territ

关键词： Generative adversarial network convolution neural network Healthcare policy Pneumonia Lungs disease Radiograph

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An efficient automatic diabetic retinopathy grading using a two-way cascaded convolution neural network

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DISCOVER COMPUTING 2025年第1期28卷 1-16页

作者： Kavitha, N. S. Kasthuri, N. Kongu Engn Coll Dept Elect & Commun Engn Perundurai Tamil Nadu India Kongu Engn Coll Dept Elect & Commun Engn Perundurai India

High blood sugar levels lead to an eye disease called diabetic retinopathy. This disease causes vision loss from adults to children. Early detection and proper treatment can reduce vision loss. This research suggests computer-aided diagnosis-based early detection of diabetic retinopathy. The main goal of this study is to automatically detect non-proliferative diabetic retinopathy in any retinal imaging. The proposed model consists of two main stages namely, pre-processing and classification. The first stage is to collect the images from the dataset, after which they are pre-processed using the techniques of image enhancement, noise removal, blurring effects, etc. For classification, a two-way cascaded convolution neural network is developed. To improve the effectiveness of the classifier, the handcrafted features are extracted from the image and fused with the proposed classifier. The suggested system divides images of diabetic retinopathy into four categories such as none, mild, moderate, and severe diabetic retinopathy. The effectiveness of the suggested technique is evaluated using a variety of criteria. The diagnostic efficacy of the proposed model is demonstrated through a comparative analysis with state-of-the-art methodologies, showcasing its performance against established approaches.

关键词： Diabetic retinopathy convolution neural network Hand-crafted features Pooling layer Feature map

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Multi-Classification convolution neural network Models for Chest Disease Classification

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第2期16卷 373-380页

作者： Ayman, Noha Gadallah, Mahmoud E. A. Saeid, Mary Monir Fayoum Univ Fac Comp & Artificial Intelligence Dept Comp Sci Faiyum Egypt Modern Acad Comp Sci & Management Technol Egypt Dept Comp Sci Cairo Egypt Fayoum Univ Fac Comp & Artificial Intelligence Dept Informat Syst Faiyum Egypt

Chest diseases significantly affect public health, causing more than one million hospital admissions and approximately 50,000 deaths annually in the United States. Chest X-ray imaging technology, which is a critically important imaging technique, helps in examining, diagnosing, and managing chest conditions by providing essential insights about the presence and severity of disease. This study introduces a novel chest X-ray classification framework leveraging a fine-tuned VGG19 model (16 layers) enhanced with CLAHE for improved contrast, binary mask attention to highlight abnormalities and advanced data augmentation for better generalization. Key innovations include the use of a Probabilistic U-Net for lung segmentation to isolate critical features and weighted masks to focus on pathological regions, addressing class imbalance with computed class weights for fair learning. By achieving 95% accuracy and superior class- specific metrics, the proposed method outperforms existing deep learning approaches, providing a robust and interpretable solution for real-world healthcare applications, where a test accuracy of 94.8% is achieved using different customized models based on VGG19 without using a mask. The experimental results indicate that our proposed method surpasses current deep learning techniques in terms of overall classification accuracy for chest disease detection.

关键词： convolution neural network classification chest X-ray image preprocessing U -Net deep learning U -Net deep learning

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Automatic Recognition of Vehicle Exhaust Remote Sensing Data Based on convolution neural network Inception Algorithm

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH 2025年第1期19卷 1-13页

作者： Peng, Guili Xing, Zhihan Liu, Dewen Mai, Yongqi Jin, Denghui Wang, Shoubin Tianjin Chengjian Univ Sch Control & Mech Tianjin 300384 Peoples R China Tianjin Chengjian Univ Int Sch Engn Tianjin 300384 Peoples R China Shanghai Aerosp Control Technol Shanghai 201108 Peoples R China STECOL Corp Power Construct Corp China Tianjin 300384 Peoples R China

Vehicle exhaust emissions are a significant factor causing urban environmental pollution in China. Anomaly detection in vehicle exhaust remote sensing data presents challenges due to unreasonable interference data that are difficult to manually identify and eliminate. This study addresses the need for advanced methods to accurately and efficiently identify these anomalies. We propose an enhanced convolutional neural network (CNN) Inception algorithm for recognizing vehicle exhaust data, which converts multidimensional exhaust parameters into images for feature extraction and recognition. Utilizing measured data from Xiqing Saida Road in Tianjin, Meishan area in Sichuan, and Gucheng County in Hebei Hengshui, we established a comprehensive dataset focusing on nitrogen oxides, hydrocarbons, and carbon monoxide concentrations. Three CNN algorithms (AlexNet, VGG16, and Inception) were selected for their proven effectiveness in image recognition tasks and their capability to handle complex data patterns. These models were evaluated for their performance in vehicle exhaust recognition. Experimental results demonstrate that all three algorithms can effectively identify abnormal vehicle exhaust data, with the improved Inception CNN achieving the highest accuracy of 98.7%.The findings underscore the superior performance of the Inception network, establishing it as a robust tool for filtering unreasonable data from remote sensing datasets and efficiently recognizing vehicles with substandard exhaust emissions. This research provides a critical foundation for assessing vehicle compliance and contributes significantly to urban air quality monitoring and pollution control initiatives. This paper propose an enhanced convolutional neural network (CNN) Inception algorithm for recognizing vehicle exhaust *** algorithm has strong effectiveness and the ability to handle complex data patterns in image recognition *** algorithm has been successfully applied to recognition of

关键词： Vehicle exhaust data Remote sensing convolution neural network Inception network

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convolution neural network for fluid flow simulations in cascade with oscillating blades

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2025年 462卷

作者： Bublik, Ondrej Heidler, Vaclav Vimmr, Jan Univ West Bohemia Fac Appl Sci NTIS New Technol Informat Soc Tech 8 Plzen 30100 Czech Republic Univ West Bohemia Fac Appl Sci Dept Mech Tech 8 Plzen 30100 Czech Republic

This paper aims to design a computational model for simulating the unsteady flow field in a cascade of oscillating blades. The core of the new model is a convolutional neural network, which is trained on a simplified cascade consisting of three blades. The primary advantage lies in significantly reducing the computational cost, as the new model is several orders of magnitude faster than traditional CFD methods for evaluations, though training the model remains computationally intensive. The convolutional neural network can accurately predict the unsteady flow field, as demonstrated in validation examples. In the next step, a composition algorithm is proposed to combine several simplified cases, enabling the solution of a cascade with any number of blades.

关键词： convolution neural network Unsteady fluid flow Blade cascade Fluid-structure interaction

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