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AdamW-RSO DBN: AdamW-Rat Swarm Optimized deep belief network for software vulnerability detection

Multimedia Tools and applications 2024年 1-23页

作者： Shaji, Lijin R, Suji Pramila Department of Computer Applications Noorul Islam Centre for Higher Education Tamil Nadu Thuckalay629180 India Department of Computer Science and Engineering Mar Baselios Institute of Technology and Science Kerala Kothamangalam686693 India

During the software development phase, vulnerability detection needs huge interest to make it less vulnerable and secure. Every time, the vulnerable software provokes hackers to make malicious activities and interrupts the software operation which results in millions in financial losses to the software firm. To minimize the losses, there are reliable and effectual vulnerability detection systems developed by security communities aspiring to recognize software vulnerabilities earlier in the development or testing stage. One such system is deep learning which can detect security vulnerabilities in the software. This work develops an optimized deep-learning system for detecting software vulnerabilities. Feature fusion approach is developed using canonical correlation analysis and Deep Residual Network (DRN) so that all the features in the input open-source projects are utilized without negligence. Moreover, an algorithm, AdamW- Rat Swarm Optimizer (AdamW-RSO), is proposed by combining AdamW and RSO, to train the DBN that is adopted to perform vulnerability detection. Finally, the detection performance is examined by utilizing metrics, such as recall, precision, and F-measure on a function-level vulnerable dataset. The test results reveal that the AdamW-RSO-based DBN has obtained superior detection performance. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Network intrusion

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Parasitical Disease Prediction Model - a Deep Learning Based Approach 3

Parasitical Disease Prediction Model - a Deep Learning Based...

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3rd International Conference on Advances in Computing, Communication, Embedded and Secure Systems, ACCESS 2023

作者： Sreeja, S.P. Asha, V. Saju, Binju Pooja Yadav, S. Prashant, Prabhu Chodnekar Sammit Prasad, Arpana New Horizon College of Engineering Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350346169

Female anopheles mosquitoes transmit the highly contagious parasitical diseases. Animals as well as humans are harmed by this sickness. In the worst-case scenario, this illness could result in the patient's death if it is not adequately diagnosed in the early stages. It is exceedingly difficultly in confirming 0 the presence of ailment in industry owing towards a deficiency of exceedingly methodological competence. Cutting-edge this situation, data retrieval assistance is required for accurate and quick disease identification. With the aid of IT division buzzword know-hows like Machine Learning, Deep Learning, and Non-natural Acumen, modern IT sectors are working tirelessly to combat this sickness. If appropriately applied, these technologies will continue to be the backbone of healthcare as they have been in recent years. In order to determine if an organism is infected with a parasite or not, this study applies the Convolutional Neural Network (CNN) algorithm to a minuscule carbon copy of the contaminated blood cells. 15 out of 16 random photos can be accurately predicted by our suggested model, which achieved an accuracy of 95.23 percent. © 2023 IEEE.

关键词： Diseases

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Optimizing Task Offloading Efficiency in Non-Orthogonal Multiple Access-Enabled Edge Computing Networks Using Deep Reinforcement Learning 2

Optimizing Task Offloading Efficiency in Non-Orthogonal Mult...

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Singh, Raj Sarkar, Pritam Dash, Bibhuti Bhusan Patra, Sudhansu Shekhar Gourisaria, Mahendra Kumar Bandyopadhyay, Anjan Kiit Deemed to be University School of Computer Engineering Bhubaneswar India Kiit Deemed to be University School of Computer Applications Bhubaneswar India

ISBN: (纸本)9798350388916

Edge computing shows potential, in supporting applications that require computation in wireless networks. Alongside advancements in edge computing, nonorthogonal multiple access (NOMA) has emerged as a way to improve spectrum efficiency. This study discusses an edge computing system where various edge devices utilize NOMA to coordinate their tasks with a central edge node that has computational resources for task processing. This framework involves coordinating task scheduling allocating resources and managing power distribution to minimize costs, which include delay and energy consumption. Understanding the balance between energy usage and delay is essential, in this context. It is challenging to Find the best practice for this complex combinatorial optimization problem is a real challenge. To deal with this, we offer an online knowledge-based optimization system leveraging Deep Reinforcement *** simulation results investigate and compare with the previous works on the basis of reducing the cost, resource allocation, task scheduling and proper power distribution. © 2024 IEEE.

关键词： Deep reinforcement learning

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Enhancing Security in 5G Vehicular Networks Through Machine Learning-Based Attack Detection 4

Enhancing Security in 5G Vehicular Networks Through Machine ...

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4th IEEE International Conference on Applied Electromagnetics, Signal Processing, and Communication, AESPC 2024

作者： Verma, Aarzoo Bhol, Seema Gupta Pattnaik, Prasant Kumar Panda, Bishnupriya School of Computer Engineering KIIT Deemed to Be University Bhubaneswar India School of Computer Applications KIIT Deemed to Be University Bhubaneswar India

ISBN: (纸本)9798331528171

The vehicular networks along with 5G technology is promising and has a potential to revolutionize transportation sector, making it both smarter and safer. By analyzing data from connected vehicles, ML can improve traffic flow, predict accidents, and optimize route planning. However, challenges such as accurately predicting vehicle movements, managing computational and network resources, and ensuring data security in the cloud persist. The proposed study is presenting a comprehensive approach that combines various ML techniques with a focus on edge computing and MLbased attack detection. Edge computing improves the performance, as data is being processed closer to its source, enabling real-time decision-making. Furthermore, incorporating ML-based attack detection mechanisms strengthens network security by identifying and responding to cyber threats promptly. This integrated strategy aims to refine classification algorithms used in traffic management, resulting in more accurate predictions and efficient resource use. By harnessing the advantages of both edge and cloud computing while implementing robust security measures, this study aspires to design an efficient and more secured transportation system powered by 5G technology. The ultimate goal is to develop a system that not only improves traffic flow and reduces accidents but also ensures data security and optimal resource management, paving the way for the future of smart mobility. © 2024 IEEE.

关键词： 5G mobile communication systems

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Smart Watering: Revolutionizing Irrigation with AR and IoT

Smart Watering: Revolutionizing Irrigation with AR and IoT

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International Conference on Interdisciplinary Approaches in Civil engineering for Sustainable Development, IACESD 2023

作者： Sumanth, V. Manasa, G. Pushpa, T.S. Reddy, Ch. Ram Mohan Department of Computer Applications B.M.S. College of Engineering Karnataka Bengaluru India

ISBN: (纸本)9789819700714

Drip irrigation has become a successful and sustainable method of agricultural water management, which optimizes crop productivity. In irrigation system, water is provided to plants depending on the soil type. In agriculture, two things are crucial: first is learning about the soil’s fertility and second is measuring the moisture content of the soil. This study combines the use of augmented reality (AR) and Internet of Things (IoT) technologies to further improve the accuracy and efficacy of drip irrigation systems. IoT plays a vital role in drip irrigation with its network of interconnected devices and sensors. By deploying IoT-enabled smart sensors in agricultural fields, real-time data on soil moisture, humidity, temperature and weather conditions can be collected and analyzed. AR technology provides real-time visual overlays and interactive guides that aid in the installation, monitoring and maintenance of drip irrigation systems. Using AR applications, irrigation specialists and farmers can assess water flow rates, pressure levels and soil moisture data. The integration of AR technology into automated drip irrigation system has the potential to bring significant improvements to the agriculture industry. This suggested application uses a moisture sensor to deliver information about the soil’s moisture content and water plants accordingly. Here Arduino Uno, moisture sensor and other electronic tools are used to measure moisture content of the soil. An augmented reality prototype is proposed, using Unity software to visualize the model (Chetankumar in Augmented reality—theory, design and development, 1st edn, McGraw Hill, 2020;Amit et al. in Int J Electr Electr Data Commun 10(10):38–41, 2022). The proposed integration of AR and IoT technologies provides significant benefits, including optimized water usage, enhanced crop yield and reduced operational cost. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Soil moisture

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OHMBC: Optimized Hybrid deep learning Model for Classification of Breast Cancer 4

OHMBC: Optimized Hybrid deep learning Model for Classificati...

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4th International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Saju, Binju Asha, V. Tressa, Neethu Kondikoppa, Pooja B. Pooja Pavan Kumar, V.J. Kalady India New Horiznon College of Engineering Department of Master of Computer Applications Bengaluru India

ISBN: (纸本)9798350343670

Men are much less likely than women to develop breast cancer. Breast lumps, bloody nipple discharge, and changes in the nipple's or breast's shape or texture are all indications of breast cancer. The study proposes a hybrid model to classify Diabetic retinopathy images. Initially, images are pre-processed which includes Image resizing, Gray scale conversion and noise removal using a combination of Weiner filter and Median filter. Further, images are enhanced using Gaussian Mixture based histogram equalization. Features of the images are extracted using Dense Convolutional neural network. An optimized ResNet101 model with improved Aquila optimization is used for classification. Performance of the models is compared with other studies in literature. The proposed model has obtained 96% accuracy, 98.1% sensitivity, 98.2% specificity, 98% precision, and 98.18% F1 Score. © 2024 IEEE.

关键词： Breast Cancer DCNN Gaussian Mixture IAO Resnet

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Enhanced Deep Learning Techniques to Classify Tamil Handwritten Characters 3

Enhanced Deep Learning Techniques to Classify Tamil Handwrit...

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

作者： Saravanan, T.M. Jegadeesan, M. Selvaraj, P.A. Gopika, P. Kavinesh, R. Mahashwetha, G.S. Kongu Engineering College Department of Computer Applications Tamil Nadu Perundurai638 060 India

ISBN: (纸本)9798350361186

Tamil handwritten document digitization is becoming increasingly dependent on exploit of handwritten character identification. Tamil is solitary of the mainly used traditional languages within India, especially in south. It can be exceedingly difficult to detect a non-digitized character because tamil has such a great and multipart character set. The Inception model and other convolution neural networks aid in classification of various type of objects on images. Here, symmetric and asymmetric structure blocks to facilitate the model itself include convolutions, average, max pooling, concatenations, dropouts, and completely linked layers. The batch normalization, which is used broadly in the model and is also functional to the inputs for activation of the function. SoftMax is used to compute loss. To differentiate handwritten characters for better classification, we employed Inception. In this article, we evaluate the handwritten character identification for tamil by means of ResNet, Inception v3, VGG16, and VGG19. There are 1609 test images and 3901 train images in the 156-class dataset. © 2024 IEEE.

关键词： Convolution

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Prostate Cancer Prediction Using Convolutional Neural Networks 3

Prostate Cancer Prediction Using Convolutional Neural Networ...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Sethi, Bijaya Kumar Singh, Debabrata Rout, Saroja Kumar Department of Computer Science and Engineering Bhubaneswar India Department of Computer Applications Bhubaneswar India Department of Information Technology Hyderabad India

ISBN: (纸本)9798350364569

Cancer-related mortality in men is highest among men who suffer from prostate cancer. The lack of clarity and consistency of early symptoms often makes diagnosis a challenge in the later stages (stages III and IV). Existing diagnostic techniques face challenges such as subjectivity, variability between observers, and lengthy testing processes involving biomarkers, biopsies, and imaging tests. This paper introduces a novel convolutional neural network (CNN) algorithm for prostate cancer diagnosis and prediction to overcome these drawbacks. An additional dataset of histopathology images was used to train and validate the system before it was put through its learning phase. In the study, 95.12% of cancerously derived cells were identified correctly and 93.02% were identified correctly, a remarkable accuracy of 98.07%. As a result of this study, Various challenges associated with expert evaluations by humans were successfully addressed, including higher misclassification rates, interdependencies between observers, and lengthy analysis periods. Prostate cancer diagnosis and prognosis have been made much simpler and faster by this research. Moving forward, to optimize the effectiveness of our proposed method, future investigations should explore the latest developments and innovations in this field. © 2024 IEEE.

关键词： Convolutional neural networks

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Diabetic Retinopathy Detection using Squeezenet 2

Diabetic Retinopathy Detection using Squeezenet

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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Nanthini, K. Sivabalaselvamani, D. Sharvanthika, K.S. Ojha, Shubham Kumar Siva, M. Department of Computer Applications Kongu Engineering College Perundurai Erode India

ISBN: (纸本)9798350340235

Diabetic retinopathy (DR), which causes lesions on the retina and impairs vision, is a common complication of diabetes mellitus. It can cause blindness if it is not caught in time. With early identification and treatment of DR, the risk of vision loss can be considerably decreased. Ophthalmologists must manually diagnose DR retina fundus images, which is more expensive, time-consuming, and error-prone than computer-aided diagnosis technologies. Recently, deep learning has become one of the most well-liked techniques forenhancing performance, especially in classifying and interpreting medical pictures. Deep learning models can detect specific lesions or abnormalities associated with diabetic retinopathy such as microaneurysms, hemorrhages, exudates, and neovascularization. By training CNNs on annotated retinal images with labeled lesions, these models can learn to identify and localize the presence of these abnormalities, aiding in early detection and diagnosis. The images have been converted to grayscale using an image batch processor and the images are resized are the pre-processing steps. Using deep learning algorithms like Squeezenet, diabetic retinopathy images are classified into No DR indications, mild NPDR, moderate NPDR, Severe NPDR, extremely severe NPDR, PDR, and Advanced PDR cases. Squeezenet produced an accuracy of 94.7% without augmentation and had an accuracy of 97 percent. Then the pre-processed images are undergone. © 2023 IEEE.

关键词： Classification Diabetic retinopathy Retino images Squeezenet

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Machine Learning for Identification of Immedicable Renal Disease 5

Machine Learning for Identification of Immedicable Renal Dis...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Prasad, Arpana Asha, V. Saju, Binju Manoj, S.M. Kumar, P.M. Manoj Ramesh, B. Nithya New Horizon College of Engineering Master of Computer Applications Bengaluru India

ISBN: (纸本)9781665474672

Chronic Renal Disease (CRD) is a condition that causes a progressive decrease in renal function. It refers to a clinical entity that causes Renal dysfunction. Improper disease diagnosis and treatment can result in irreversible renal disease with very short chances of survival. Artificial Intelligence (AI) technologies equips healthcare providers with a rich set of algorithms to predict the chronic development of diseases. Prior information helps to enable proactive corrective measures. This research study intends to develop an effective tool for predicting the CRD using machine learning approaches. A machine learning model is proposed and presented to address the non-uniform distribution of instances through class balancing, feature ranking and analysis, and classifier training. © 2023 IEEE.

关键词： Machine learning

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