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Software Defect Prediction Based Ensemble Approach

computer Systems science & engineering 2023年第6期45卷 2313-2331页

作者： J.Harikiran B.Sai Chandana B.Srinivasarao B.Raviteja Tatireddy Subba Reddy School of Computer Science and Engineering VIT-AP UniversityAmaravathi522237India Department of Computer Science and Engineering GITAM Deemed to be UniversityTelangana502329India Department of Computer Science and Engineering B.V.Raju Institute of Technology NarsapurMedakTelangana502313India

Software systems have grown significantly and in *** a result of these qualities,preventing software faults is extremely *** defect prediction(SDP)can assist developers in finding potential bugs and reducing maintenance *** it comes to lowering software costs and assuring software quality,SDP plays a critical role in software *** a result,automatically forecasting the number of errors in software modules is important,and it may assist developers in allocating limited resources more *** methods for detecting and addressing such flaws at a low cost have been *** approaches,on the other hand,need to be significantly improved in terms of *** in this paper,two deep learning(DL)models Multilayer preceptor(MLP)and deep neural network(DNN)are *** proposed approaches combine the newly established Whale optimization algorithm(WOA)with the complementary Firefly algorithm(FA)to establish the emphasized metaheuristic search EMWS algorithm,which selects fewer but closely related representative *** find the best-implemented classifier in terms of prediction achievement measurement factor,classifiers were applied to five PROMISE repository *** compared to existing methods,the proposed technique for SDP outperforms,with 0.91%for the JM1 dataset,0.98%accuracy for the KC2 dataset,0.91%accuracy for the PC1 dataset,0.93%accuracy for the MC2 dataset,and 0.92%accuracy for KC3.

关键词： Prediction of a software defect deep learning models enhancedWOA firefly algorithm

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Evaluation of CatBoost for Diabetes Prevention in Comparison to XGBoost: To avert mortality by developing an AI model capable of predicting the onset of diabetes

Evaluation of CatBoost for Diabetes Prevention in Comparison...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Gifta Jerith, G. Jagadeesh, S. Ponmathi Jeba Kiruba, P. Mahesh, C. Anantha Babu, S. Samsudeen Shaffi, S. Hyderabad India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha Engineering College Department of Computer Science and Engineering Chennai India Panimalar Engineering College Department of Information Technology Chennai India GITAM University GITAM School of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331522988

Diabetes is a metabolic condition defined by consistently elevated blood glucose levels. Hyperglycemia is characterized by heightened hunger, thirst, and urination. Numerous consequences may arise from untreated diabetes. Diabetic ketoacidosis, hyperosmolarity, hyperglycemia, and even mortality may occur as acute consequences. Heart disease, stroke, chronic renal failure, foot ulcers, and eye impairment are some of the worst long-term consequences. A total of 463 million individuals were found to have diabetes in 2019, according to the worldwide diabetes organisation. Assuming predictions come true, that number will soar to 700 million by 2045, up 578 million by 2030. It has modern medical applications, allowing for the rapid and precise diagnosis of illness. Thus, we may use machine learning to avert mortality by developing an AI model capable of predicting the onset of diabetes;to determine which algorithm is most suited for this task, we will compare the CatBoost and XGBoost methods. In the end, the research compared two diabetes prediction models, XGBoost and CatBoost, using a variety of health indicators from the dataset. Using a real-world dataset obtained from Kaggle, our suggested system is trained and built using Python. Performance assessment criteria such as precision, recall, F1score and accuracy measurements are used to thoroughly analyse our methods. In comparison to XGBoost's 88.33% accuracy rate, 85.25 percent precision, 85.63 percent recall, and an F1 Score of 86.50. CatBoost's 91.08% percent accuracy, 85.25 percent precision, 85.63 percent recall, and 87.38 F1 Score. CatBoost marks it as the most successful ensemble approach. © 2024 IEEE.

关键词： Prediction models

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Towards an Efficient DDoS Attack Detection in SDN: An Approach with CNN-GRU Fusion 4

Towards an Efficient DDoS Attack Detection in SDN: An Approa...

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

作者： Goud, Konda Srikar Rao, Giduturi Srinivasa Gitam University Gitam School of Technology Department of Computer Science and Engineering AP Visakhapatnam India Bvrit Hyderabad College of Engineering for Women Telangana Hyderabad India

ISBN: (纸本)9798350343670

The emergence of Distributed Denial of Service (DDoS) attacks presents a significant risk to Software-Defined Networking (SDN) services. This study focuses on developing an effective method for detecting DDoS attacks in SDN by leveraging Deep Learning (DL) techniques, precisely combining Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). This research proposes a novel approach that utilizes CNN-GRU-based DL models to detect DDoS attacks in SDN accurately. This approach learns intricate patterns and temporal dependencies associated with DDoS attacks by analyzing network traffic data and detecting such threats. In order to assess the effectiveness of our model, we conduct extensive experiments using diverse datasets comprising both normal network traffic and various types of DDoS attacks. We present a comparative analysis against traditional ML algorithms and existing methodologies. To assess the effectiveness of our approach, we used various performance metrics. The experimental results prove that the CNN-GRU-based DL model outperforms other techniques, exhibiting superior accuracy and overall performance in detecting DDoS attacks in SDN. © 2024 IEEE.

关键词： Convolutional Neural Networks DDoS attacks Deep Learning Gated Recurrent Units SDN

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A Novel Ensemble Methodology to Validate Fuzzy Clusters of Big Data 4th

A Novel Ensemble Methodology to Validate Fuzzy Clusters of B...

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4th International Conference on Trends in Cognitive Computation engineering, TCCE 2022

作者： Sardar, Tanvir Habib Sarkar, Rashel Ahmed, Sheik Jamil Bandyopadhyay, Anjan Assistant Professor Department of CSE GITAM School of Technology GITAM University Bangalore India Associate Professor Department of Computer Science University of Science and Technology Meghalaya Ri-Bhoi India Assistant Professor School of Computer Science and Engineering & Information Science Presidency University Bangalore India Assistant Professor Kalinga Institute of Industrial Technology Odisha Bhubaneswar India

ISBN: (纸本)9789811994821

The clustering of datasets is a widely used technique in unsupervised machine learning. The cluster quality evaluation is a tricky problem because external validation is usually not possible for clustering. This happens due to the unavailability of external proof. Although there are many methods developed and experimented with to validate the results obtained from clustering, it is always preferred to use more than a few cluster validity measures. The big data usually contain a high percentage of noise in it, making it necessary to use some additional techniques to incorporate while clustering big data. In this work, we have clustered document big data using fuzzy logic-based enhancement of traditional K-Means and K-Medoids. This work has suggested the Ensembling of seven different cluster quality validity measures to determine the best quality of fuzzy clusters. The Reuters standard document dataset is clustered using different cluster numbers, and the proposed ensemble methodology is proven to determine optimal numbers of clusters. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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SMART AGRICULTURE: INTEGRATING AIR QUALITY MONITORING WITH DEEP LEARNING FOR PROCESS OPTIMIZATION*

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Scalable Computing 2025年第3期26卷 1005-1016页

作者： Shobana, J. Subramanian, A Venkata Balamurugan, P. Perumal, Sivakumar Sankari, V. Eldho, K.J. Nareshkumar, R. Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India Department of Computer Science and Engineering GITAM University Bengaluru India Department of Computer Science and Engineering J N N Institute of Enginering India Saveetha university Chennai India Department of Computer Science and Engineering Sasi Institute of Technology and Engineering Andhra Pradesh Tadepalligudem India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Kariyamanickam Rd Tamil Nadu Samayapuram India PG and Research Department of Computer Science Mary Matha Arts and Science College Kerala Mananthavady India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur India

Modernization and intense industrialization have led to a substantial improvement in people’s quality of life. However, the aspiration for achieving an improved quality of life results in environmental contamination. A primary consequence of environmental degradation is air pollution, resulting from rising levels of poisonous chemicals in the atmosphere, which may induce detrimental health conditions in humans. It is harmful to both humans and agriculture. Given that the effects of air pollution on plants may not be readily apparent, it is important to analyse the necessary data and compute the outcomes. Farmers prioritise on pests and plant diseases, frequently neglecting the detrimental impacts of air pollution. Some plant species can withstand high amounts of pollution from suspended particulate matter and accumulated gases, while others are more susceptible to harm. Therefore, plants’ reaction to air pollution is influenced by the kind of harmful compounds, their levels, and the plant’s susceptibility to them. The LSTM +CNN Proposed Ensemble method may be used to analyse the impact of air pollution on agriculture by examining trends in crop production over time and predicting which crop is more resistant based on the pollution data. The initiative created for this aim may assist farmers in determining the most suitable crop to cultivate in their fields to minimize the impact of air pollution on agricultural yield. The findings show deep learning algorithms correctly predict hourly pollutant concentrations such as carbon monoxide, sulphur dioxide, nitrogen dioxide, ground-level ozone, and particulate matter 2.5, along with the hourly Air Quality Index (AQI) for California. A proposed model used test RMSE values as a measure to evaluate prediction performance, achieving the best possible results. © 2025 SCPE.

关键词： Nitrogen oxides

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The Implication of Multimedia in the Information Revolution 2

The Implication of Multimedia in the Information Revolution

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Kamidi, Deepthi Mirona, G. Gudipati, Sri Sowmya Rani T, Jhansi Govathoti, Sudeepthi Polamuri, Subba Rao Vignan Institute of Technology and Science Department of Computer Science and Engineering India Malla Reddy Engineering College for Women Secunderabad India Gitam Deemed-to-be-University Dept of Cse Gitam School of Technology Telangana Hyderabad India Gitam Deemed-to-be-University Dept of Cse Gitam School of Technology Hyderabad India Aditya University Department of Computer Science and Engineering Andhra Pradesh Surampalem India

ISBN: (纸本)9798350364699

The Information Revolution has been greatly influenced by advancements in the integrated use of various media, including documents, images, and audiovisual content transmitted via radio waves and television. Multimedia enhances ideas by making facts more charming and accessible, aiding better understanding and memory. The unification of the combined use of several media into digital terraces has transformed using what interacts with accompanying facts, making it an indispensable aspect of the new information countryside. This research explores the importance of multimedia, an advanced technology that is present in people's lives regularly. Explore the whole article to get a deeper understanding of the role and influence of multimedia in educational settings, professional environments, and other contexts. This study investigates and broadens the understanding of the importance of multimedia by using primary data collection methods, online journals, and publications. The advancement of technology in the modern world has shown the powerful use of multimedia. The internet and social media have been major catalysts for the advancements in multimedia. The use of multimedia is advantageous in converting images and other types of unorganised data into practical information, given the exponential growth of such data. This article serves as a valuable resource for anyone who has an interest in expanding their knowledge of multimedia and its significant impacts on both business and academics. The research demonstrates that external stakeholders may use technology innovation to stimulate and guide innovation inside firms. © 2024 IEEE.

关键词： data collecting growing exponentially Information revolution Multimedia Primary data

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Diverse Geographical Regions Based Biodiversity Conservation by LiDAR Image with Deep Learning Model

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 738-749页

作者： Babu C. N, Gireesh Reddy, Chilukala Mahender Kumar, Mungara Kiran Nagaraju, Komatigunta Maheswari, G. Uma Kiran, Ajmeera Department of Computer Science and Engineering BMS Institute of Technology & amp Management Karnataka Bengaluru India Department of Artificial Intelligence and Data Science GITAM (Deemed to be University) Hyderabad India Department of CSE School of Technology GITAM (Deemed to Be University) Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Dist A.P India Department of Computer Science and Engineering RMK College of Engineering and Technology Pudhuvoyal Chennai India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Reliable data on the composition and structure of forests at various spatial scales is necessary for the conservation and monitoring of forest biodiversity. However, because field sampling techniques can be challenging, comprehensive data regarding the features of forest habitat over wide areas are frequently lacking. In order to get over this restriction, we created variables that describe the structure of the forest landscape over a wide environmental gradient using a nationally accessible light detection and ranging (LiDAR) remote sensing dataset. This research proposes a novel technique in forest management based on biodiversity analysis using LiDAR image processing by deep learning techniques. Here, the input LiDAR image has been collected as temperature pattern dataset in major deforestation regions and processed for noise removal and normalization. This image feature has been extracted using recurrent graph learning-based SegNet component analysis. Then, this extracted feature has been classified using Gaussian deep belief region-based adversarial neural networks. The experimental analysis has been carried out for various temperature pattern datasets in terms of random accuracy, average precision, recall, NSE, and AUC. The results of this study will help determine policy objectives for the restoration of mountain areas and enhance the effectiveness of monitoring damaged mountain forests. With the help of the suggested method, 96% random accuracy, 94% average precision, 49% NSE, 93% recall, and 95% AUC were achieved. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Adversarial neural networks Deep learning techniques Deforestation rate LiDAR image processing Temperature rise analysis

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CyberGuard: Detecting Adversarial DDoS Attacks in SDN using WGAN-CNN-GRU 4

CyberGuard: Detecting Adversarial DDoS Attacks in SDN using ...

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

作者： Goud, Konda Srikar Rao Giduturi, Srinivasa Gitam University Gitam School of Technology Department of Computer Science and Engineering AP Visakhapatnam India Bvrit Hyderabad College of Engineering for Women Telangana Hyderabad India

ISBN: (纸本)9798350343670

Detecting Distributed Denial of Service (DDoS) attacks in Software-Defined Networks (SDN) has become a critical challenge in ensuring network security and availability. Adversarial attacks, which aim to evade traditional detection methods, have emerged as a formidable threat. In this study, we present a novel approach for detecting adversarial DDoS attacks within an SDN environment by leveraging the combined power of Wasserstein Generative Adversarial Networks (WGAN) and Convolutional Neural Networks with Gated Recurrent Units (CNN-GRU). Our proposed methodology capitalizes on the strengths of both WGAN and CNN-GRU to effectively identify subtle adversarial attack patterns. Firstly, we utilize WGAN to synthesize adversarial DDoS attack traffic, exploiting its capability to generate data that mimics legitimate network traffic distribution. Subsequently, we employ a hybrid model of CNN-GRU to process the generated traffic. CNN extracts spatial features from traffic data, while GRU captures temporal dependencies in the network flows. The experimental results demonstrate the effectiveness of our approach in differentiating adversarial DDoS traffic from legitimate traffic. Our model achieves a high detection accuracy by training on synthesized adversarial traffic and real-world network data, even against sophisticated adversarial attack strategies. Furthermore, our approach showcases robustness against unseen adversarial patterns, highlighting its potential for real-world deployment. © 2024 IEEE.

关键词： Adversarial DDoS attacks BOTNET CNN DDoS Deep Learning GRU WGAN

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A comprehensive survey of text-based semantic similarity with potential applications

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International Journal of Intelligent Information and Database Systems 2025年第2期17卷 143-185页

作者： Sharma, Puneet Rao, K. Yogeswara Kavitha, P. Sakthivel, T. Unitedworld Institute of Technology Karnavati University Gujarat Gandhinagar India Department of Computer Science and Engineering GITAM School of Technology GITAM Rushikonda Visakhapatnam India Department of CSE PES University Karnataka Bengaluru India Firstsoft Technologies Pvt. Ltd. Chennai India

Text-based semantic similarity has recently become a popular research topic, and estimating the semantic similarity between entities for learning and decision-making is a challenging research problem. Semantic similarity quantitatively measures the informativeness between the data objects based on their properties and relationships. Due to the vast availability of measures developed by domain experts from different research fields, choosing an appropriate semantic similarity measure that suits a specific application context is challenging. The primary objective of this systematic literature survey is to offer a comprehensive view of semantic similarity and potential applications that emphasise the enormous diversity of research contributions in a wide range of fields. In this context, it examines various categories of semantic similarity based on the underlying principles of the state-of-the-art approaches with pros and cons. Notably, the prospective application areas are explored extensively by highlighting their significance. Finally, the survey concludes by summarising valuable future research directions. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Latent semantic analysis

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Predicting the success rate of a start-up using LSTM with a swish activation function

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Journal of Control and Decision 2022年第3期9卷 355-363页

作者： Ramakrishna Allu Venkata Nageswara Rao Padmanabhuni Department of Computer Science and Engineering GITAM(Deemed to be university)VisakhapatnamAndhra PradeshIndia

The researchers emphasised more small-scale start-ups which required improvement in the economy decreased the failure rate by avoiding the valuable *** resources were required to be used without wasting them lead to the development of the economy and reduces the rate of ***,to minimise resource wastage and to avoid the risk of failure the researchers considered certain factors that affected the failure and success of smallscale *** present research uses the Crunch Base dataset that predicts the success or failure of a start-up by using the Long Short Term Memory(LSTM).The LSTM unit Recurrent Neural Network(RNN)uses the Swish activation function in Feed Forward Neural(FFN)Network for the *** proposed LSTM obtained better accuracy of 71.64%when compared with existing methods such as RNN that attained 65.67%and Artificial Neural Network(ANN)of 69.7%.

关键词： Crunch base dataset gross domestic product long short term memory recurrent neural network start-up companies

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