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6th IEEE PhD Colloquium on Emerging Domain Innovation and Technology for Society, PhD EDITS 2024

作者： Santhosh Kumar, B.J. Sahana, S. Srilakshmi, C.S. Joshi, Pallavi School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Mysuru India

ISBN: (纸本)9798331541965

In Wireless Sensor Networks (WSNs), effective routing and obstacle avoidance are essential for enhancing communication dependability and network durability. This paper introduces a new hybrid routing approach that combines the Mountain Gazelle Optimizer (MGO) for worldwide search with the Salp Swarm Algorithm (SSA) for local optimization. Whereas SSA optimizes obstacle avoidance in real time, MGO mimics the strategic movements of gazelles in difficult situations. The MGO-SSA approach balances exploration and exploitation by reducing energy consumption, averaging 5.4 J for the MGO, 6.2 J for the SSA, and attaining an overall improvement of 20.1%. According to simulations, this strategy works better than conventional techniques, leading to increased resilience, better packet delivery rates, and increased energy efficiency, which makes it the perfect choice for dynamic WSN applications such as disaster relief and environmental monitoring. © 2024 IEEE.

关键词： Disaster prevention

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A Deep Learning-based Approach for Shrimp Quality Estimation using DenseNet 121 7

A Deep Learning-based Approach for Shrimp Quality Estimation...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Bipin Nair, B.J. Akhil, P. Shashank, M.N. Amrita Vishwa Vidyapeetham School of Computing Department of Computer Science Mysore India

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359299

Shrimp creates an immense significance in the global scale due to their nutritional and cultural importance. They provide plentiful supply of amino acids, vital nutrients, omega-3 fatty acids and many more. The extensive challenge of obtaining a dataset and efficiently classifying it based on the quality remains an ongoing area of research. The manually collected dataset was expanded to increase the generalization ability of the network. The model achieved an impressive remarkable results attaining an accuracy of 95.03% during training and 98.75% on the testing dataset, highlighting its efficiency and effectiveness. The classification was performed across three categories of shrimp dataset: Fresh, Frozen and Stale shrimp images that were manually collected consisting of 5800 images in total. This work contributes to the enhancement in quality control of shrimps, benefiting industries that depends efficient classification processes. © 2024 IEEE.

关键词： Classification (of information)

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ConfigMaster: An Interactive Solution for System Management 8

ConfigMaster: An Interactive Solution for System Management

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Ganapathi, Ananya Sivakumar, Parikshith Divya, K.V. Elango, Aditya Gupta, Hardik Amrita School of Computing Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331505462

The 'ConfigMaster: An interactive solution for system management' is a simple utility written using Bash scripting. It is designed for Unix-like systems. The tool lets users easily configure system settings and do administrative tasks without having to remember complex commands. The tool minimizes user's work by automating tasks. The tool can be used even by users with very little technical knowledge, as it has a simple menu-based interface to manage things like network settings, software packages, and wireless connections directly from the terminal. By automating common tasks, the tool makes system management more efficient and accessible. By automating common tasks, the tool makes system management more efficient and accessible. It's very useful to simplify system configuration on Unix-like systems using scripting languages like Bash. 'ConfigMaster' aims to offer a smooth user experience with the incorporation of core management principles from an operating system, such as disk scheduling, process synchronization, and memory management etc. Being flexible itself, this tool will bring further improvements to make effective responses to emerging user needs. The framework being flexible, it can be expanded to add new features to configure under different scenarios. By integrating essentials operating system operations, 'ConfigMaster' enables users to efficiently perform system configurations along with promoting ease of use. © 2024 IEEE.

关键词： Wi-Fi

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Augmenting Clinical Decisions with Deep Learning Lung Cancer Image Abnormality Segmentation 14

Augmenting Clinical Decisions with Deep Learning Lung Cancer...

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14th International Conference on Cloud computing, Data science and Engineering, Confluence 2024

作者： Venkatraman, K. Reddy, Sirigiri Naga Pavan Sathvik Amrita Vishwa Vidyapeetham School of Computing Computer Science and Engineering Chennai India

ISBN: (纸本)9798350344837

Lung cancer is a major global cause of death, highlighting the critical need for quick and accurate detection methods. The exploration of computational alternatives arose from the standard way of manually processing CT images, which is time-consuming and error-prone. In this work, we combine the advantages of Support Vector Machine (SVM) and VGG16 classifiers to provide a novel method for enhancing lung cancer diagnosis. By analyzing the 'IQ-OTH/NCCD' dataset, our hybrid model-which combines the VGG 16 and SVM algorithms-performs admirably in differentiating between aggressive, benign, and normal lung cancer cases. This combination of traditional machine learning with deep learning tackles accuracy and efficiency issues, which is a promising development over current diagnostic methods. We conduct a comprehensive comparative analysis with prominent architectures to select the optimal model based on accuracy, efficiency, and resource requirements. In addition to introducing the VGG 16+SVM model, our research provides valuable insights into deep learning architectures, with the ultimate goal of advancing precise and efficient diagnosis of lung cancer, which is crucial in combating this global health challenge in the future. © 2024 IEEE.

关键词： computerized tomography

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Forecasting Bank Nifty Close Prices: An Evaluation of Time Series and Hybrid Models 2

Forecasting Bank Nifty Close Prices: An Evaluation of Time S...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Pushpakumar, Ardra Mohan, Devaprabha Pai, Maya L. School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and IT Kochi India

ISBN: (纸本)9798350371314

In India's economy, the stock market plays a critical role. Making accurate stock price predictions is crucial for both individuals and corporations, since it helps in guiding investment decisions and managing risks effectively. This study focuses on forecasting the close price of Bank Nifty, which is an index that includes banking stocks listed on the National Stock Exchange(NSE). Multiple time series models such as Autoregressive Integrated Moving Average(ARIMA), Gated Recurrent Unit(GRU) and Prophet, also their different hybrid models are assessed for their predictive capabilities using metrics like Mean Absolute Percentage Error(MAPE), Root Mean Squared Logarithmic Error(RMSLE) and Mean Squared Logarithmic Error(MSLE). The study also highlights the efficiency of Auto ARIMA. Emphasizes that the GRUPROPHET model stands out as the precise predictor with the ARIMA-GRU model coming in a close second. The study underscores using the GRU-PROPHET model for predicting the closing price of Bank Nifty. Stresses the significance of employing advanced forecasting methods, in financial markets. © 2024 IEEE.

关键词： Errors

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Machine Learning Techniques Using Deep Instinctive Encoder-Based Feature Extraction for Optimized Breast Cancer Detection

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computers, Materials & Continua 2024年第2期78卷 2441-2468页

作者： Vaishnawi Priyadarshni Sanjay Kumar Sharma Mohammad Khalid Imam Rahmani Baijnath Kaushik Rania Almajalid Department of Computer Science Gautam Buddha UniversityGreater NoidaUttar PradeshIndia College of Computing and Informatics Saudi Electronic UniversityRiyadh11673Saudi Arabia School of Computer Science Shri Vaishno Devi UniversityKatraIndia

Breast cancer(BC)is one of the leading causes of death among women worldwide,as it has emerged as the most commonly diagnosed malignancy in *** detection and effective treatment of BC can help save women’s *** an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection *** paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography(CBIS-DDSM)data *** novelty of the proposed framework lies in the integration of various techniques,where the fusion of deep learning(DL),traditional machine learning(ML)techniques,and enhanced classification models have been deployed using the curated *** analysis outcome proves that the proposed enhanced RF(ERF),enhanced DT(EDT)and enhanced LR(ELR)models for BC detection outperformed most of the existing models with impressive results.

关键词： Autoencoder breast cancer deep neural network convolutional neural network image processing machine learning deep learning

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Classification of Seashells Using Deep Learning Models 3

Classification of Seashells Using Deep Learning Models

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Bipin Nair, B.J. Shalwin, A.S. Raju, S. School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Mysuru India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518592

Seashell classification is essential for industries, including food, jewelry, decor, pharmaceuticals, and tourism. Conventional classification techniques mainly rely on humans, which is error-prone and time-consuming, requiring manual expertise. This paper uses a deep learning-based approach for classifying seashells into four categories. Our method employs deep learning models, including SENet and InceptionNet-V3. Our study reveals that SENet achieved a good accuracy of 99.46% at a train-test ratio of 80:20 with a batch size of 8 and 60 epochs. © 2024 IEEE.

关键词： Image classification

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Enhancing pre-trained models for text summarization: a multi-objective genetic algorithm optimization approach

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Multimedia Tools and Applications 2024年 1-17页

作者： Mohan, G. Bharathi Kumar, R. Prasanna Elakkiya, R. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India Birla Institute of Technology and Science Dubai345055 United Arab Emirates

Pre-trained language models have significantly advanced text summarization by leveraging extensive pre-training data to enhance performance. Many cutting-edge models undergo an initial pre-training phase on a large corpus before being fine-tuned specifically for text summarization tasks. The challenge arises when the limited data available for these summarization tasks and the high complexity of the models lead to aggressive fine-tuning, which can cause the models to over fit the training data and fail to generalize well to new, unseen data. To address these challenges, the study proposes a multi-objective based Genetic Algorithm (MOGA) approach for optimizing pre-trained models for summarization The proposed approach first compares the performance of various pre-trained models by fine-tuning them with the CNN/Daily Mail dataset. Secondly, the proposed Multi-Objective Genetic Algorithm (MOGA) is applied to all validated pre-trained models and compared using a statistical approach. Experimental results reveal that Pre-training with Extracted Gap-sentences for Abstractive Summarization Sequence-to-sequence (PEGASUS) model performs best among the pre-trained models. Further optimization with the proposed MOGA method yields even better results, with the optimized model achieving a Recall-Oriented Understudy for Gisting Evaluation) ROUGE-1 score of 0.514, a ROUGE-2 score of 0.4, and a ROUGE-L score of 0.5. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Genetic algorithms

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Innovative Solutions for Sustainable IoT in Smart City Infrastructure 5

Innovative Solutions for Sustainable IoT in Smart City Infra...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Arul, S. Kavitha, P. Kamalakkannan, S. School of Computing Sciences Department of Computer Science Chennai Pallavaram India School of Computing Sciences Department of Computer Applications Chennai Pallavaram India School of Computing Sciences Department of Information Technology Chennai Pallavaram India

ISBN: (纸本)9798331504403

Smart cities are being created all over the world to enhance the safety and quality of life for their residents via the use of technology. Video surveillance, which includes placing cameras at key locations across the city to keep an eye on public areas and give law enforcement and other local authorities real-time access to recordings, is a crucial part of the architecture of smart cities. Cuttingedge technologies like deep learning, blockchain, edge computing, and cloud computing are used in modern video surveillance systems. This paper offers a comprehensive review of video surveillance systems for smart cities, examining both their benefits and drawbacks. In order to improve inhabitants' safety, security, and overall well-being, this article aims to show the benefits of video surveillance systems in smart cities and offers suggestions for their application. © 2024 IEEE.

关键词： Smart city

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Climacteric Fruit Skin Defect Region Segmentation by Boundary Identification Using Morphologically Enhanced Watershed

Climacteric Fruit Skin Defect Region Segmentation by Boundar...

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International Conference on computing and Machine Learning, CML 2024

作者： Sumathi, K. Mangayarkarasi, S. Department of Computer Science School of Computing Sciences VISTAS Pallavaram Chennai India

ISBN: (纸本)9789819765874

Climacteric fruits are perishable due to their ripening nature even after being harvested. Reaped fruits are to be checked before marketing to decide their self-life. Manual checking of harvested fruits consumes more time and cost. Harsh handling and manual tactile methods lead to the spoilage of more than fifty percent of fruits which affect the livelihood of the farmers. Image processing methods are used to avoid spoilage by harsh handling and to check accurately to elevate a grower’s economy. The image of the fruits is captured through a digital device and pre-processed followed by the segmentation of the defective peel to segregate the affected fruits from the marketable gives a better result than manual checking. In this proposed work a combination filtering by MBG filter followed by segmentation using Morphologically Enhanced Watershed (MEWS) boundary identification was implemented. The MBG filter removes noise and enhances the image to select texture and edge features in different orientations that improves the segmentation process. The segmentation of defective fruit peel is checked with morphologically enhanced watershed, grab cut, k-means (3 clusters), and canny methods. Even with the improper lighting while capturing the images, in other segmentation methods, shadows are taken into consideration. On the contrary, the novel approach of using Morphologically Enhanced watershed method segregates the defective region of the harvested climacteric fruits more effectively than the other methods with the segmentation accuracy of 93.9% when the images are captured in an uneven light source. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fruits

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