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检索条件"机构=Computer Science and Engineering Nitte Meenakshi Intitute of Technology"

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Two-Dimensional Multi-Deep CNNs for Accurate and Robust Software Defect Detection: A Performance-Driven Approach 5

Two-Dimensional Multi-Deep CNNs for Accurate and Robust Soft...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Veeramachaneni, Vinod Mallick, Pradeep Kumar Rout, Subhashree Pareek, Piyush Kumar Colorado Technical University Department of Information Technology United States Kiit Deemed to be University School of Computer Engineering India Nalanda Institute of Technology Department of Computer Science and Engineering Odisha Bhubaneswar India Nitte Meenakshi Institute of Technology Yelahanka Karnataka Bengaluru560064 India

ISBN: (纸本)9798331522100

Software systems are growing in size and complexity. These properties make software defect prevention challenging. Thus, automatically predicting software module defects can help engineers conserve resources. Several ways have been presented to discover and fix these issues cheaply. However, these solutions may need a performance bump. This paper introduces the Multi-Deep Convolutional Neural Network to improve binary and multi-class software classification. Instead of one-dimensional CNNs, the proposed model captures n-gram characteristics inside and between datasets using a two-dimensional multi-scale convolutional technique. Advanced crow search optimisation algorithm (ACSOA) is used to optimally select model parameters. Besides Apache Active MQ, the study employed Eclipse data sets to evaluate the recommended deep learning method. F1-score, recall, accuracy, and precision were utilized to validate the model. The results show that the suggested strategy finds software vulnerabilities better and more reliably than current models. © 2025 IEEE.

关键词： Search engines

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NAARI- A Portable Sanitary Napkin Vending Machine 2

NAARI- A Portable Sanitary Napkin Vending Machine

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Shetty, Chinmai Malathi, S.Y. Sarojadevi, H. Ranjan Kumar, H.S. Suchetha, G. Debnath, Sanjoy Kumar Dept of Information Science and Engineering Nitte India Kle Institute of Technology Dept of Computer Science and Engineering Hubballi India Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Bangalore India Shri Madhwa Vadiraja Institute of Technology and Management Bantakal Udupi India Sahyadri College of Engineering and Management-Affiliated to Vtu Department of Information Science and Engineering Mangalore India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350368413

In an effort to address the challenges of menstrual hygiene management in developing countries, a portable sanitary pad vending machine was developed. The vending machine is powered by an Arduino Uno microcontroller and a power source that ranges between 5 to 12 volts. It features an liquid-crystal display (LCD) keypad, token acceptor with an Radio Frequency Identification (RFID) scanner for validation, and a L298d motor driver that powers two DC Servo motors to turn the spring coil system that delivers the sanitary pads or disposable bags to the user. The entire system is enclosed in a metal box for protection against damage. The LCD keypad serves as an input and output device, displaying information about the vending machine and providing instructions for its use. With the token acceptor and RFID scanner, only authorized users are granted access to the vending machine. The scanner reads the user's RFID card, which has a unique identification number, to confirm their authorization. The portable vending machine is designed to be cost-effective and easy to use, providing an innovative solution to menstrual hygiene management challenges. It is also portable and can be relocated to various locations. Overall, the vending machine serves as a critical resource in promoting menstrual hygiene and addressing the challenges that come with it. Its use ensures that individuals have access to essential sanitary products that they need to live healthy lives. © 2024 IEEE.

关键词： Liquid crystal displays

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Pneumonia Detection in Chest X-Rays Using Combined Approach of Deep Learning 2

Pneumonia Detection in Chest X-Rays Using Combined Approach ...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Aditya Pai, H. Pareek, Piyush Kumar Guruprasad, M.S. Chandrappa, S. Bhargava, Krishna Mit Art Design and Technology University Mit School of Computing Pune India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Department of Computer Science and Engineering Dehradun India School of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9798350372892

A respiratory infection like pneumonia faces a serious health challenge across the globe, especially in the poor and underdeveloped countries. The pneumonia is affected by various factors like bacteria or viruses. The reason for the affection is due to higher level of pollution, poor air quality, excess of population, poor infrastructure etc. To reduce the threat from pneumonia, there is a need for early and accurate detection of the infection by using chest X-ray images as the most effective diagnostic method. The paper proposes an automated chest X-ray analysis system by using VGG16 architecture and a weighted average ensemble method to enhance diagnostic accuracy. By integrating deep transfer learning, the system overcomes data scarcity issues, achieving a remarkable 99% accuracy on a publicly available dataset. The research underscores the potential of automated systems in improving pneumonia diagnosis, highlighting the urgent need for advanced diagnostics in resource-limited settings. © 2024 IEEE.

关键词： Air quality

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Efficient Task Scheduling for Cloud Environments with MSA: Enhancing Makespan and Resource Utilization 5

Efficient Task Scheduling for Cloud Environments with MSA: E...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

ISBN: (纸本)9798331522100

People still want the fastest and most effective answers, even as technology improves. Since the advent of cloud computing and its multiple substructures, many algorithms have been proposed to improve the presentation of each unit or structure. The massive cloud can solve any user-supplied task. Therefore, new methods are proposed to improve the process and reduce evaluation time. The task scheduler allocates jobs to virtual machines to maximize efficiency and minimize execution time in cloud computing. This paper introduces the Mantis Search Algorithm (MSA) as a scheduling solution and tests and compares it to other algorithms based on efficiency, makespan, and cost limits. Sexual cannibalism, prey pursuit, and prey assault comprised this optimization process. A multiobjective fitness purpose is created in the CC stage by reducing makespan and optimising resource utilization. There are several ways to test the strategy and evaluate the results. Simulations showed that the MSA technique outperformed alternatives in numerous situations. © 2025 IEEE.

关键词： Virtual environments

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Credit Risk Assessment using Ensemble Models and Explainable AI

Credit Risk Assessment using Ensemble Models and Explainable...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Ramya Srikanteswara Krinal Naghera Shashank Balnad Kukkaje Adarsh Kumar Shrey P Dept of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru Karnataka

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

New challenges are still there in the credit risk management in this context of the financial system, primarily because of the lack of good models for prognosis as well as low interpretability. To address this issue, this research brings together CatBoost, XGBoost, and LightGBM to improve the prospect of identifying high-risk borrowers. A minimal level of Explainable AI (XAI) is utilized to make confirmations that the model follows them and to build confidence in the outcome. Moreover, based on a difference to basic models, the obtained ROC-AUC score equals 0.9693, proving the feasibility of the proposed ensemble approach in questions of impact developing default risks. The end product is therefore a robust method with ensemble modeling and compliant with regulatory requirements in operational financial environments.

关键词： Explainable AI Computational modeling Scalability Predictive models Data models Risk management Ensemble learning Stakeholders Streams Synthetic data

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An Effective Public-key Authenticated Searchable Encryption for Protecting Sensitive data in Healthcare IoT

An Effective Public-key Authenticated Searchable Encryption ...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Shanthi, M. Navaneetha, M. Kartheek, G.C.R. Parameshachari, B.D. Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Gopalan College of Engineering and Management Department of Computer Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

The practice of conveying medical records to either a Third-Party Cloud Service (TCS) for backups has become increasingly popular in recent decades at least as the healthcare Internet of things has expanded. However, when used in many environments, a few little PAEKS schemes also have security issues. People proposed a novel, efficient, but also effective PAEKS scheme in this article. The Diffie-Hellman pow consensus concept is used in this scheme to generate a secret key known only to the receiver and to the sender. We show that our scheme is completely risk-free against inside keyword various attacks in several co-environments when the Cloudera Diffie-Hellman assumption is used. From the analysis, it shows that proposed method achieved better results in terms of accuracy (96.8%), precision (95.2%), recall (95.8%) f1-score (95.5%), memory utilization (80 MB) and execution time (0.7 sec) respectively. © 2023 IEEE.

关键词： Attribute-Based Encryption Data Preprocessing Healthcare IoT Public-Key Authenticated Searchable Encryption Query Processing Searchable Encryption

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Culture Craze Couture: Personalized Picks for Indian Elegance

Culture Craze Couture: Personalized Picks for Indian Eleganc...

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Data science and Network Security (ICDSNS), International Conference on

作者： Vijaya Shetty S Teja Kumar M Vivek Gowda V Vishal V Tarkeswar Rai P Ramesh Naidu Computer Science and Engineering Nitte Meenakshi Institue of Technology Bengaluru India

ISBN: (数字)9798350373110

ISBN: (纸本)9798350373127

Fashion recommendation systems have gained popularity, yet there's a noticeable gap in representing Indian ethnic fashion. This paper introduces an innovative Fashion Recommendation System tailored specifically for Indian clothing to address this gap. Employing both image-based and text-based recommendation techniques, the system provides personalized suggestions to users. Notably, it includes a virtual try-on feature, allowing users to preview Indian ethnic clothing items on their bodies before purchasing. This feature relies on a sophisticated body segmentation algorithm, extracting users' body shapes from photos or videos and overlaying selected clothing items onto them. Additionally, the system comprises a dedicated wardrobe section, enabling users to revisit and tryon previously purchased Indian ethnic clothing. By combining advanced technologies with a focus on Indian fashion, this system enhances the online shopping experience, boosting user satisfaction and confidence in their fashion choices.

关键词： Industries Image segmentation Shape Clothing Neural networks Mixture models Network security Feature extraction Recommender systems Videos

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COCOs Nucifera Tree Disease Prediction Using Faster Region-Convolutional Neural Network with ResNet-50 3

COCOs Nucifera Tree Disease Prediction Using Faster Region-C...

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3rd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Kavitha, M.G. Raviprakash, M.L. Alsalami, Zaid Nagendar, Y. Ramachandra, A.C. Kalpataru Institute Of Technology Department Of Computer Science And Engineering Tiptur India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq School Of Computer Science And Engineering Sr University Warangal India Nitte Meenakshi Institute Of Technology Department Of Ece Bengaluru India

ISBN: (纸本)9798350318609

Coconut diseases were predicted based on various categories such as color, shape, time frame, and changes in growth. Deep learning (DL) was applied for this purpose. However, the image dataset used was unlabelled, leading to challenges such as overfitting and complexity in computational time for prediction. The proposed method is the Faster Region-Convolutional Neural Network (R-CNN) model for detecting and classifying coconut clusters, aiming to minimize overfitting in the identification of coconut nucifera diseases. The coconut disease dataset involved performing image classification, distinguishing between healthy and unhealthy coconuts. Normalization, feature extraction, and classification techniques were utilized for this task. For feature extraction, the VGG-16 model was involved, which includes maximum pooling layers to extract features at different levels of the network enhancing the quality of feature maps and providing better region identification. Classification was performed using the R-CNN with ResNet-50. The dataset contained images of both healthy and unhealthy coconuts, and the regions were predicted to identify and classify the different diseases within the images. ResNet-50 captures high-level features and helps mitigate the vanishing gradient problem during training, making it easier to connect with the R-CNN method. The proposed Faster R-CNN with ResNet-50 achieved a performance accuracy of 98.40%. This performance was compared to existing methods such as Convolutional Neural Network (CNN), Deep-CNN (DCNN) and Long Short-Term Memory (LSTM). © 2024 IEEE.

关键词： Long short-term memory

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Deep Learning Approaches To Image-Based Species Identification

Deep Learning Approaches To Image-Based Species Identificati...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Nirmala J S Kamsala Tharun Somu Geetha Sravya Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India

This research investigates the efficacy of deep learning and transfer learning methodologies for image species recognition, crucial for biodiversity conservation. Using TensorFlow, Keras, and OpenCV libraries, diverse species images are processed and various convolutional neural network (CNN) models are trained. Data augmentation enhances model generalization, while optimization techniques mitigate overfitting. Comparison of transfer learning models identifies Xception as most effective. Fine-tuning Xception achieves high accuracy in species recognition, demonstrating the potential of deep learning in biodiversity research. Image-based species recognition is vital for biodiversity conservation and ecological studies. Deep learning and transfer learning offer promising avenues for automating this task, but their effectiveness needs thorough investigation. Utilizing TensorFlow, Keras, and OpenCV libraries, diverse species images are processed. Data augmentation enhances diversity, while EarlyStopping and learning rate reduction optimize training. Transfer learning models including ResNet50V2, ResNet152V2, InceptionV3, Xception, and DenseNet121 are compared to identify the most effective architecture. Xception emerges as the most promising model and undergoes fine-tuning on the dataset. The resultant model achieves high accuracy in species recognition, demonstrating the potential of deep learning and transfer learning in automating and enhancing species identification tasks. This study highlights the effectiveness of advanced neural network architectures and transfer learning in image-based species recognition. The findings underscore their potential in addressing ecological challenges and advancing biodiversity research and conservation efforts.

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Development and Implementation of Advanced Text Autocorrection via Probabilistic Methods Using Levenshtein Distance and Jaccard Similarity

Development and Implementation of Advanced Text Autocorrecti...

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Emerging Systems and Intelligent Computing (ESIC), International Conference on

作者： Nirmala J Saunshimath Dipankar Raj Upadhyaya Abiskar Kumar Yadav Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bangalore India

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

The paper aims at creating an advanced autocorrection system specifically for the Nepali language. This system uses sophisticated natural language processing (NLP) techniques to automatically rectify typographical errors of Nepali texts. The autocorrection system suggests the most likely corrections for misspelt words using the Levenshtein Distance and Jaccard Similarity algorithms. This paper aims to improve the efficiency and user experience across a variety of text-based applications, such as social media, document editing, and messaging, by decreasing the amount of time needed for text editing and increasing text accuracy. The groundwork for future developments in NLP applications for the Nepali language is laid by this effort.

关键词： Accuracy Social networking (online) Lexicon Linguistics Probabilistic logic Natural language processing User experience

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