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

作者： Padmakala, S. Al-Farouni, Mohammed Rao, Deepak Dasaratha Saritha, Kambhampati Puneeth, R.P. Saveetha Institute of Medical And Technical Sciences Saveetha University Saveetha School of Engineering Department of Computer Science And Engineering Chennai India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Indian Institute of Technology Patna India Gokaraju Rangaraju Institute of Engineering and Technology Department of Ai & Ml Hyderabad Bachupally India Department of Computer Science and Engineering Karnataka India

ISBN: (纸本)9798350372892

G networks represent the fifth iteration of cellular networks, thus aimed at resolving the challenges and constraints faced by the preceding 4G network. Cloud Radio Access Networks (CRAN) provides economical and EnergyEfficient (EE) solutions customized to the requirements of 5G networks. This research proposes an energy-efficient CRAN that automatically allocates Baseband Unit (BBU) resources to Remote Radio Unit (RRHs) based on traffic, increasing network throughput as well as reducing power usage. Bat Optimization Algorithm (BOA) is a Swarm Intelligence (SI) approach used to optimize Resource Allocation (RA) in CRAN by mapping BBURRHs. Through controlling the BBU's mode based on resource utilization, this research minimizes the Power Consumption (PC) in 5G CRAN. Moreover, the findings revealed that the proposed BOA-CRAN reduced the power usage upto 95% when compared to existing methods such as 5G CRAN, Green-RAN while increasing BBU efficiency. © 2024 IEEE.

关键词： 5G mobile communication systems

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Adaptive residual convolutional neural network for moiré image restoration

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International Journal of Information Technology (Singapore) 2025年第2期17卷 783-791页

作者： Malagi, Vindhya P. Naresh, E. Mithra, C. Suresh, B. V. N. V. Krishna Department of AI & amp ML Dayananda Sagar College of Engineering Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Chennai 600062 India Computer Science & amp Engineering KLEF KL University Andhra Pradesh Vaddeswaram India

Restoring Moiré images presents significant challenges due to the complex interference patterns that obscure image details. These patterns often degrade the quality of images, making accurate restoration crucial for various applications. Effective Moiré image restoration requires advanced techniques to overcome the difficulties posed by these intricate artifacts. This study introduces an Adaptive Residual Convolutional Neural Network (RCNN) for Moiré image restoration, augmented with the Zebra Optimization Algorithm (ZOA) to enhance both feature extraction and restoration accuracy. The hybrid model leverages ZOA's capability to balance exploration and exploitation, optimizing network parameters to improve learning efficiency. Tested on the DIV2K dataset, this approach demonstrates significant improvements in handling complex Moiré patterns, as evidenced by enhanced Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) values. © The Author(s) 2024.

关键词： Adaptive residual convolutional neural network Moiré images Peak signal-to-noise ratio Structural similarity index measure Zebra optimization algorithm

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Comparative Analysis of Sorting Algorithms: Time Complexity and Practical Applications

Comparative Analysis of Sorting Algorithms: Time Complexity ...

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

作者： Manish Rai Hemlata Parmar Suhani Sharma Department of Computer Science and Engineering(AI&ML) Manipal University Jaipur Jaipur India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

In the field of computer science, sorting algorithms are crucial because they facilitate the effective processing and arrangement of data in a variety of scenarios, including data analysis, searching, and optimal system operation. The objective of this study is to look at and compare different sorting algorithms to see how well they work and how useful they are in different situations. The study tests popular sorting algorithms like Quick Sort, Merge Sort, and Bubble Sort on different input sizes, types of data (sorted, reversed, and random), and how they are implemented in the C++ programming language. The study shows how they work in terms of time complexity, resource use, and real applications through a systematic analysis. The most important results show that algorithms work differently depending on the inputs. For example, Quick Sort does better in most situations, while Merge Sort stays stable in the worst ones. This work also finds situations where simpler algorithms, like Bubble Sort, are good for small data sets. This paper is unique because it gives real-life cases, suggestions for choosing the best algorithm based on specific needs, and suggestions for how to do that. The goal of these results is to help programmers, teachers, and experts pick the right sorting algorithms for their computer tasks.

关键词： Machine learning algorithms Data analysis Systematics Systems operation Instruction sets Machine learning Internet of Things Time complexity Optimization Sorting

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Transfer Learning in Endoscopic Imaging: A Machine Vision Approach to GIT Disease Identification

Transfer Learning in Endoscopic Imaging: A Machine Vision Ap...

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Communications and computer science (InCCCS), International Conference on

作者： Jayavrinda Vrindavanam Pradeep Kumar Gaurav Kamath Abhilash S Bharadwaj Department of Computer Science and Engineering(AI & ML) Dayananda Sagar University Bengaluru India

ISBN: (数字)9798350358858

ISBN: (纸本)9798350358865

In the context of rapidly rising Gastro-Intestinal tract related diseases among the population, the study explores the use of ai and ml that can enable early detection of the GI tract related abnormalities. The study after a detailed review of the existing studies, proposes on transfer learning to fine tune model weights trained on large data sets for GI-tract applications. The study has implemented models such as EfficientNet, MobileNet V3 Large, ResNet-50, DenseNet-201 and CNN (Convolutional Neural Network) on the endoscopic images. The top performing model, EfifcientNet2S, showcased superior proficiency identifying the eight distinct GIT diseases. The paper highlights the scope of applying transfer learning neural network architectures in enhancing disease diagnosis as well as selecting optimal architectures for accurate and efficient medical image classification in gastrointestinal pathology.

关键词： Pathology Transfer learning Neural networks computer architecture Gastrointestinal tract Medical diagnosis Convolutional neural networks

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CbcErDL: Classification of breast cancer from mammograms using enhance image reduction and deep learning framework

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Multimedia Tools and Applications 2025年第15期84卷 15501-15526页

作者： Agrawal, Rohit Singh, Navneet Pratap Shelke, Nitin Arvind Tripathi, Kuldeep Narayan Singh, Ranjeet Kumar Department of Computer Science and Engineering Madhav Institute of Technology & Science Race Course Road MP Gwalior474005 India School of AI Bennett University Uttar Pradesh Greater Noida201310 India School of Computer Science and Engineering Bennett University TechZone 2 UP Greater Noida201310 India

Breast cancer is a major health concern for women worldwide, and early detection is vital to improve treatment outcomes. While existing techniques in mammogram classification have demonstrated promising results, their limitations become apparent when applied to larger datasets. The decline in performance with increased dataset size highlights the need for further research and advancements in the field to enhance the scalability and generalizability of these techniques. In this study, we propose a framework to classify breast cancer from mammograms using techniques such as mammogram enhancement, discrete cosine transform (DCT) dimensionality reduction, and deep convolutional neural network (DCNN). The first step is to improve the mammogram display to improve the visibility of key features and reduce noise. For this, we use 2-stage Contrast Limited Adaptive Histogram Equalization (CLAHE). DCT is then used to enhance mammograms to reduce residual data. It can provide effective reduction while preserving important diagnostic information. In this way, we reduce the computational complexity and increase the results of subsequent classification algorithms. Finally, DCNN is used on size-reduced DCT coefficients to learn feature discrimination and classification of mammograms. DCNN architectures have been optimized with various techniques to improve their performance, including regularization and hyperparameter tuning. We perform experiments on the DDSM dataset, a large dataset containing approximately 55,000 mammogram images, and demonstrate the effectiveness of the proposed method. We assess the proposed model’s performance by computing the precision, recall, accuracy, F1-Score, and area under the receiver operating characteristic curve (AUC). We achieve Precision and Recall values of 0.929 and 0.963, respectively. The classification accuracy of the proposed models is 0.963. Moreover, the F1-Score and AUC values are 0.962 and 0.987, respectively. These results are better a

关键词： Discrete cosine transforms

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Octacleaner: Underwater Trash Detection Through YOLO 3

Octacleaner: Underwater Trash Detection Through YOLO

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3rd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Pavani, Dhanni Reddy, A Nymisha Nandini Saw, Neha Prasad, Siva Naik, Santosh Madeva Hyderabad Institute of Technology and Management Computer Science with AI & ML Hyderabad India Mechanical Engineering Hyderabad Institute of Technology and Management Hyderabad India

ISBN: (纸本)9798350317022

Million tons of trash is generated every day, which is often inadequately managed and sent to landfills. When it rains, trash along with agricultural waste and sediment, washes into water bodies, leading to underwater pollution. The heavy plastics, metals, glass, and organic waste settle at the water body's bottom, compounding the problem. To solve this,Octacleaner was developed which is an underwater trash-collecting robot equipped with a YOLO object detection model. In our study, the performance of YOLOv5 and YOLOv8 were compared. YOLOv5 boasts efficient and rapid object detection, making it suitable for real-time applications. It utilizes a streamlined architecture, making it computationally efficient while maintaining accuracy. YOLOv8, on the other hand, takes this a step further, achieving high precision of 90°/0, offering faster and highly accurate results. Its strength lies in its ability to swiftly and effectively identify objects. However, the unique challenge in underwater environments is that trash can change its shape due to water currents and decomposition. Additionally, factors like reduced sunlight and the presence of underwater mud can make the predictions more challenging. Despite this, YOLOv8 shows promise in addressing these difficulties, with its superior accuracy and speed. © 2023 IEEE.

关键词： Image Processing Object Detection Trash Underwater Pollution Underwater trash YOLOv5 YOLOv8

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Hybrid Deep Maxout-VGG-16 model for brain tumour detection and classification using MRI images

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Journal of Biotechnology 2025年 405卷 124-138页

作者： Loganayagi, T. Sravani, Meesala Maram, Balajee Rao, Telu Venkata Madhusudhana Department of Electronics and Communication Engineering Paavai Engineering College Pachal Tamilnadu Namakkal India Department of Computer Science and Engineering GMR Institute of Technology Andhra Pradesh Rajam India School of Computer Science and Artificial Intelligence SR University Telangana Warangal506371 India Department of AI&DS Vignan's Institute of Information Technology Visakhapatnam530049 India

Brain tumor detection is essential to identify tumors at an early stage, allowing for more effective treatment. The patient's chances of recovery and survival can be improved by early detection. The existing methods for detecting brain tumour have several limitations, including limited accessibility, exposure to radiation, high costs and potential for false negatives. To overcome the issues, a Deep Maxout-Visual Geometry Group-16 (DM-VGG-16) model is devised for detecting tumour in brain from Magnetic Resonance Imaging (MRI). Initially, MRI image is sent for pre-processing as input. Here, Non-Local Mean (NLM) filter performs pre-processing. The pre-processed image is subjected to segmentation stage, which is accomplished by Template–based K-means and improved Fuzzy C Means algorithm (TKFCM). Moreover, in feature extraction stage, various features, like area, cluster prominence, Hybrid PCA- Normalized GIST (NGIST) and Improved Median binary Pattern (IMBP) are extracted. Lastly, proposed DM-VGG-16 model is utilized for detection of brain tumors from extracted features. The DM-VGG-16 is the integration of Deep Maxout Network (DMN) and Visual Geometry Group-16 (VGG-16). The DM-VGG-16 outperformed superior results than conventional techniques with performance metrics, including accuracy, True Negative Rate (TNR) and True Positive Rate (TPR) of 90.76 %, 90.65 % and 90.75 % correspondingly. © 2025 Elsevier B.V.

关键词： K-means clustering

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Multilingual Transformer for Dynamic Cricket Commentary Generation

Multilingual Transformer for Dynamic Cricket Commentary Gene...

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Computing and Data science (ICCDS), International Conference on

作者： Gaurav Kamath Udayabhaskara N Department of Computer Science and Engineering (AI and ML) Dayananda Sagar University Bangalore India

ISBN: (数字)9798350365337

ISBN: (纸本)9798350365344

The sport of Cricket is growing in popularity which necessitates the use of technology to adhere to popular game standards. Since this is a data rich and most elaborate game, understanding this for someone who's new to the game had been challenging. We are aiming to solve this unavailability of the game through native commentary to audience of all major languages. To outline our entire research work we ideate a Cricket Game ai through computer vision, use it to produce text commentary of game-play and perform Machine Translation(MT) to produce native commentary for a targeted language, As afirst part of the outlined process in this work we focus on the implementation of the machine translation component of this pipeline, evaluating various methods including Seq2seq and transformer based architectures. Our method of cross feeding Seq2Seq with Transformer model results in Bilingual Evaluation Understudy (BEL U) score on Validation set of 2.08.

关键词： Computational modeling Pipelines Games Data science Transformers Real-time systems Machine translation

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Intrusion Detection System in Wireless Sensor Networks using Modified Recurrent Neural Network with Long Short-Term Memory 2

Intrusion Detection System in Wireless Sensor Networks using...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Ramkumar, K. Alzubaidi, Laith H. Malathy, V. Venkatesh, T. Kruthika, C.G. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India The Islamic University Najaf Iraq Sr University School of Engineering Department of Ece Telangana India Kpr Institute of Engineering and Technology Department of Ece Coimbatore India Nitte Meenakshi Institute of Technology Department of Ai and Ml Bengaluru India

ISBN: (纸本)9798350317558

Leveraging deep learning, Intrusion Detection Systems (IDS) for Wireless Sensor Networks (WSN) enhance security through advanced analytics, detecting and mitigating network intrusions in real-time for robust protection. Evaluations and scalability considerations are critical for improving the applicability and robustness of intrusion detection systems based on deep learning techniques. This paper introduces a novel approach: a modified Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM) for IDS in WSN classification, utilizing the NSL-KDD dataset. Rigorous performance validation demonstrates outstanding metrics, including an accuracy of 99.95%, precision at 99.93%, and a robust recall of 95.69%, with the F1-score reaching 99.80%, surpassing benchmarks set by existing methods. Notably, current approaches like Deep Neural Network (DNN), Convolutional Neural Network-LSTM (CNN-LSTM), Conditional Generative Adversarial Network (CGAN), and Stacked Autoencoders-DNN (SAE-DNN) face limitations in achieving optimal accuracy and robustness. The proposed RNN-LSTM method exhibits significant advancements in addressing these challenges for effective intrusion detection in WSN © 2024 IEEE.

关键词： Convolutional Neural Network Deep Neural Network Intrusion Detection System Long Short-Term Memory Wireless Sensor Network

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Malware Detection Using the Machine Learning Based Modified Partial Swarm Optimization Approach 1

Malware Detection Using the Machine Learning Based Modified ...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICaiSC 2023

作者： Sivakumar, S. Saminathan, S. Ranjana, R. Mohan, M. Pareek, Piyush Kumar Department of Computer Science Perambalur India Srinivasan College of Arts and Science Department of Computer Science and IT Perambalur India Panimalar Engineering College ECE Department India AP/CSE KPR Institute of Engineering and Technology India Nitte Meenakshi Institute of Technology Department of AI and ML Bengaluru India

ISBN: (纸本)9798350323795

Malware analysis includes a crucial step after malware detection called malware categorization, which classifies dangerous files. There have been many reported static and dynamic methods for classifying malware up to this point. The ml-MD strategy presented in this study uses static methods to categorise various malware families and is based on machine learning. In order to detect malware, we create a new machine learning-based framework. The characteristics from the dataset are extracted in this case using principal component analysis (PCA). In order to offer the best malware detection solutions, introduce a machine learning-based Modified Particle Swarm Optimization (MPSO) algorithm. Improved Accuracy and detection rate using ml-based MPSO technique. The effectiveness of the suggested technique in detecting malware is demonstrated by the experimental results on several benchmark data sets, which greatly outperform alternative approaches. © 2023 IEEE.

关键词： Machine learning

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