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A deep learning approach using modified Xception net for oral malignancy detection using histopathological images of oral mucosa

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International Journal of Applied Decision sciences 2025年第2期18卷 168-188页

作者： Das, Madhusmita Dash, Rasmita Department of Computer Application Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India

The early detection of oral malignancy by physicians is a strenuous task. The analysis of histopathological oral malignancy images using image processing and deep learning techniques can be an add-on facility for doctors to diagnose oral cancer. In this work, a deep learning model is used, designing a modified Xception net with swish activation function and generalised mean pool for the detection of oral malignancy. To prove the superiority of the model, three stages of comparative analysis are carried out. In the first stage, the model is compared with a few advanced models explicitly Alexnet, Resnet50, Resnet101, VGG16, VGG19, Inception net and original Xception. In the second stage, loss and accuracy graphs analysis is done and in the third stage, the proposed model’s accuracy is compared with other model’s accuracy available in the literature. It is found that the modified Xception net got upgraded performance by an accuracy of 98.97%. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Lung cancer

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An Ingenious IoT Based Crop Prediction System Using ML and EL

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computers, Materials & Continua 2024年第4期79卷 183-199页

作者： Shabana Ramzan Yazeed Yasin Ghadi Hanan Aljuaid Aqsa Mahmood Basharat Ali Department of Computer Science&IT Government Sadiq CollegeWomen UniversityBahawalpur63100Pakistan Department of Computer Science/Software Engineering Al Ain UniversityAl Ain64141UAE Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)Riyadh11671Saudi Arabia Agronomic Research Station Bahawalpur63100Pakistan

Traditional farming procedures are time-consuming and expensive as based on manual labor. Farmers haveno proper knowledge to select which crop is suitable to grow according to the environmental factors and soilcharacteristics. This is the main reason for the low yield of crops and the economic crisis in the agricultural sectorof the different countries. The use of modern technologies such as the Internet of Things (IoT), machine learning,and ensemble learning can facilitate farmers to observe different factors such as soil electrical conductivity (EC),and environmental factors like temperature to improve crop yield. These parameters play a vital role in suggestinga suitable crop to cope the food scarcity. This paper proposes a systemcomprised of twomodules, first module usesstatic data and the second module takes hybrid data collection (IoT-based real-time data and manual data) withmachine learning and ensemble learning algorithms to suggest the suitable crop in the farm to maximize the *** is used to train the model that predicts the crop. This system proposed an intelligent and low-cost solutionfor the farmers to process the data and predict the suitable *** implemented the proposed system in the *** efficiency and accuracy of the proposed system are confirmed by the generated results to predict the crop.

关键词： Machine learning Internet of Things sensors ensemble learning

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SFI-Swin: symmetric face inpainting with swin transformer by distinctly learning face components distributions

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Multimedia Tools and Applications 2025年第17期84卷 17581-17595页

作者： Givkashi, MohammadHossein Naderi, MohammadReza Karimi, Nader Shirani, Shahram Samavi, Shadrokh Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan84156-83111 Iran Department of Electrical and Computer Engineering McMaster University HamiltonL8S 4L8 Canada Computer Science Department Seattle University Seattle98122 United States

Image inpainting consists of filling holes or missing parts of an image. Inpainting face images with symmetric characteristics is more challenging than inpainting a natural scene. None of the powerful existing models can fill out the missing parts of an image while considering the symmetry and homogeneity of the picture. Moreover, the metrics that assess a repaired face image quality cannot measure the preservation of symmetry between the rebuilt and existing parts of a face. In this paper, we intend to solve the symmetry problem in the face inpainting task by using multiple discriminators that check each face organ’s reality separately and a transformer-based network. We also propose "symmetry concentration score" as a new metric for measuring the symmetry of a repaired face image. The quantitative and qualitative results show the superiority of our proposed method compared to some of the recently proposed algorithms in terms of the reality, symmetry, and homogeneity of the inpainted parts. The code for the proposed method is available at https://***/mohammadrezanaderi4/SFI-Swin. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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AR-Light: Enabling Fast and Lightweight Multi-User Augmented Reality via Semantic Segmentation and Collaborative View Synchronization

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IEEE Transactions on computers 2025年第6期74卷 2073-2086页

作者： Wen, Yu Shah, Aamir Bader Cao, Ruizhi Zhang, Chen Chen, Jiefu Wu, Xuqing Xie, Chenhao Fu, Xin University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States Beihang University School of Computer Science and Engineering Beijing100191 China University of Houston Department of Information Science Technology HoustonTX77204 United States

Multi-user Augmented Reality (MuAR) allows multiple users to interact with shared virtual objects, facilitated by exchanging environment information. Current MuAR systems rely on 3D point clouds for real-world analysis, view synchronization, object rendering, and movement tracking. However, the complexity of 3D point clouds leads to significant processing delays, with approximately 80% of overhead in commercial frameworks. This hampers usability and degrades user experience. Our analysis reveals that maintaining the facing side of the real-world scene in a stable environment provides sufficient information for virtual object placement and rendering. To address this, we introduce a lightweight quadtree structure, representing 2D scenes through semantic segmentation and geometry, as an alternative to 3D point clouds. Additionally, we propose a novel correction method to handle potential shifts in virtual object placement during view synchronization among users. Combining all designs, we implement a fast and lightweight MuAR framework named AR-Light and test our framework on commercial AR devices. The evaluation results on real-world applications demonstrate that AR-Light can achieve high performance in various real-world scenes while maintaining a comparable virtual object placement accuracy. © 1968-2012 IEEE.

关键词： Semantic Segmentation

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Resource Profiling for Virtualized Radio Access Networks 18

Resource Profiling for Virtualized Radio Access Networks

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Tella, Rajashekhar Reddy Gupta, Utkarsh Venkateswarlu, Gudepu Chintapalli, Venkatarami Reddy Tamma, Bheemarjuna Reddy Kondepu, Koteswararao IIT Dharwad Department of Computer Science and Engineering Dharwad India National Institute of Technology Department of Computer Science and Engineering Kerala Calicut India IIT Hyderabad Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350391725

The mobile networks are rapidly evolving, transitioning from specialized hardware to fully virtualized platforms that run on commercial off-the-shelf (COTS) hardware powered by general-purpose processors. Mobile operators are increasingly adopting Network Functions Virtualization (NFV) to take advantage of virtualization benefits, such as ease of deployment, flexibility, and cost savings. A crucial component of this transformation is the implementation of virtualized Radio Access Network (vRAN) solutions, which provide a cost-effective way to deploy the 5G and beyond RAN as a containerized network function (CNF) on COTS hardware. vRAN is more efficient than traditional RAN, as it allows multiple base station workloads to be multiplexed on the same hardware, potentially co-located with other workloads. However, ensuring consistent performance of vRAN workloads in this consolidated environment is challenging due to the high variability and unpredictability arising from contentions for shared system resources such as CPU cores, Last Level Cache (LLC), and Memory Bandwidth. Thus, there is a strong need to understand the vRAN components if they are affected by any system resource. This paper presents an understanding of how the next-generation scalable processors can be used to run the vRAN components and presents a detailed analysis of the controllable knobs such as CPU, LLC, Memory Bandwidth, and energy consumption. The proposed vRAN profiling shows that the observed throughput varies significantly based on the number of pinned CPU cores (e.g., up to 250 {Mbits} / {sec}), and also similar performance observed with other system resources - LLC allocation and Memory Bandwidth. © 2024 IEEE.

关键词： Radio access networks

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A Review: DDoS Attack Detection Using Clustering Algorithms 5

A Review: DDoS Attack Detection Using Clustering Algorithms

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5th International Conference on Artificial Intelligence and Data sciences, AiDAS 2024

作者： Mishra, Amit Kumar Thapliyal, Siddhant Siddiqui, Junedh Bhatt, Rohit Naithani, Keshav Joshi, Ankit Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798331528553

Distributed Denial of Service (DDoS) attacks pose a substantial threat to the stability and security of networked systems by disrupting server replies and services. Their popularity arises from their ease of setup and the obstacles connected with detection. Various approaches have been utilized to identify DDoS attacks in network traffic, with machine learning methodologies proven to be the most effective. This research focuses on establishing an innovative approach for DDoS detection, leveraging the K-Means clustering algorithm as a semi-supervised technique. © 2024 IEEE.

关键词： K-means clustering

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A Systematic Review on Federated Learning in Edge-Cloud Continuum

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SN computer science 2024年第7期5卷 1-21页

作者： Mishra, Sambit Kumar Sahoo, Subham Kumar Swain, Chinmaya Kumar Department of Computer Science and Engineering SRM University-AP Andhra Pradesh Amaravati India Department of Computer Science and Engineering IMIT BPUT Odisha Cuttack India

Federated learning (FL) is a cutting-edge machine learning platform that protects user privacy while enabling collaborative learning across various devices. It is particularly relevant in the current environment when massive volumes of data are generated at the edge of networks by developing technologies like social networking, cloud computing, edge computing, and the Internet of Things. FL reduces the possibility of unauthorized access by third parties by allowing data to stay on local devices, hence mitigating any privacy breaches. The integration of FL in Cloud, Edge, and hybrid Edge-Cloud settings are some of the computing paradigms that this study investigates. We highlight the salient features of FL, go over the main obstacles to its implementation and use, and make recommendations for future study directions. Furthermore, we assess how FL, by facilitating safe and cooperative data sharing among vehicles, can improve service quality in the Internet of Vehicles (IoV). Our study findings are intended to offer practical insights and suggestions that may have an impact on a variety of computing technology research topics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cloud computing Computing technology Edge computing Federated learning IoT IoV

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Non Plant Specific Smart Greenhouse with Convective Drying Unit 2

Non Plant Specific Smart Greenhouse with Convective Drying U...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Mukherjee, Jhilam Mukherjee, Dibyasree Upadhyay, Sneha Basu, Ariyan Shaw, Lipakshi Singh, Debiya Manna, Sumana Adamas University Department of Computer Science Engineering Barasat India Adamas University Department of Computer Science and Engineering Barasat India

ISBN: (纸本)9798350384277

This paper presents a thorough analysis of the design and execution of a smart greenhouse system that makes use of machine learning and IoT technology. By automating critical procedures, improving monitoring capabilities, and providing predictive analytics for optimal plant growth and disease detection, the system seeks to completely transform conventional greenhouse agriculture. The greenhouse atmosphere is dynamically maintained by the integration of IoT-enabled actuators controlled by a WiFi module (ESP32). Fans, servo motors, mist sprays, lights, and pumps are automated to maintain ideal conditions. Real-time access to environmental factors is facilitated by cloud-based data storage and remote monitoring. Furthermore, plant growth and disease detection can be predicted using machine learning techniques on Raspberry Pis. A CNN model has been customised to identify specific plant diseases. Additionally, energy efficiency and environmental control are improved by the use of a vortex tube in a new drying and cooling system. Accurate soil moisture measurements are possible using a moisture metre based on microclimate conditions, which improves irrigation management. By addressing the issues with traditional greenhouse agriculture, this comprehensive strategy uses cutting-edge technologies to open the door for productive and sustainable crop production. © 2024 IEEE.

关键词： Moisture meters

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Forecasting Bitcoin Prices Using Deep Learning for Consumer-Centric Industrial Applications

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1351-1358页

作者： Roy, Pradeep Kumar Kumar, Abhinav Singh, Ashish Sangaiah, Arun Kumar Indian Institute of Information Technology Surat Department of Computer Science and Engineering Surat394190 India Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Prayagraj211004 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar751024 India National Yunlin University of Science and Technology International Graduate Institute of AI Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102-2801 Lebanon

As cryptocurrencies become more popular as investment vehicles, bitcoin draws interest from businesses, consumers, and computer scientists all across the world. Bitcoin is a computer file stored in digital wallet applications where each transaction is secured using strong cryptographic algorithms. It was challenging to forecast the future price of bitcoin due to its nonlinearity and extreme volatility. Several recent classic parametric models have been found with limited accuracy. To address the limitations and fill the existing research gaps, there is a need for a good prediction model which will provide the desired accuracy in the case of uncertainty and dynamism. This research suggested a deep learning-based framework for predicting and forecasting Bitcoin price. The research will be helpful for worldwide consumers and industries to take their decision on whether to invest or not. The research utilizes Yahoo! finance dataset for the period of 01-03-2016 to 26-02-2021 having 1828 samples. The experimental outcomes of the proposed Long Short-Term Memory (LSTM) model outperformed similar deep learning models by securing minimum loss and confirming that it can be used for future price prediction of the cryptocurrencies, which is helpful for the buyer to take their decision. © 1975-2011 IEEE.

关键词： Hidden Markov models

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A lightweight approach for intrusion detection in WSNs based on DCGAN

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International Journal of Information Technology (Singapore) 2025年第2期17卷 951-957页

作者： Devi, Manu Nandal, Priyanka Sehrawat, Harkesh Department of Computer Science and Engineering University Institute of Engineering and Technology Maharshi Dayanand University Haryana India Department of Computer Science and Engineering Maharaja Surajmal Institute of Technology New Delhi India

Ensuring strong security procedures is crucial in the rapidly advancing realm of wireless sensor networks (WSNs) in order to protect sensitive data and preserve network integrity. The resource limitations and unpredictable environments that characterize WSNs frequently provide challenges for traditional intrusion detection systems (IDS). To overcome these issues, this research introduces a unique lightweight intrusion detection system that utilizes Deep Convolutional Generative Adversarial Networks (DCGAN). The proposed system employs wireless sensor network blackhole flooding selective forwarding (WSN-BFSF) dataset for training and evaluation. The suggested approach improves the IDS’s detection capabilities in addition to reducing computing overhead. The findings show that the lightweight DCGAN-based IDS provides an improved defense against new security threats, making it a good fit for real-time deployment in WSNs. Here, the performance metrics like receiver operating characteristic (ROC) is evaluated to check the efficiency of the model. The proposed model is assessed with the existing studies based on metrics such as accuracy, precision, recall and f1-score. The results show that the proposed approach attains the highest accuracy (94%) thus outperforming the existing methods and studies. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Deep Learning (DL) Generative Adversarial Networks (GAN’s) Intrusion Detection System (IDS) Lightweight DCGAN Wireless Sensor Networks (WSN’s)

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