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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Soni, Priyanka Mense, Omkar Manohar Kanti Addya, Sourav National Institute of Technology Cloud and Smart System Services Lab Department of Computer Science and Engineering Karnataka India

ISBN: (纸本)9798331531195

Device connectivity has been redefined by the rapid development of the Internet of Things (IoT) technology, enabling diverse applications in areas such as smart cities, smart homes, and healthcare. These applications produce massive amounts of data, making it a significant challenge to offload tasks to cloud servers. However, the physical distance between devices often leads to latency issues for IoT systems, impacting time-sensitive applications. Fog computing tackles this issue by processing data closer to IoT devices. However, a high user density can strain the capacity of a macro base station, potentially leading to congestion. Drone Base Stations (DBSs) provide a flexible solution by acting as mobile relays to reduce latency and traffic burden. To address this issue, this work proposes a drone-assisted load distribution strategy in IoT networks. In our proposed approach, we deploy DBS efficiently using a greedy optimization technique. However, after the deployment of DBS, an optimal base station assignment framework dynamically connects user equipment to the most suitable base station, helping to minimize latency and enhance network performance. This dual-phase approach provides a scalable and practical solution for realtime IoT applications. Through extensive simulations, ensures that our proposed approach achieves a more balanced assignment compared to baseline algorithms. © 2025 IEEE.

关键词： Drones

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Diabetic retinopathy detection using a well-calibrated uncertainty aware convolutional neural network

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Multimedia Tools and Applications 2024年 1-23页

作者： Verma, Preeti Elango, Sivasankar Singh, Kunwar Department of Computer Science and Engineering National Institute of Technology Tamilnadu Tiruchirappalli620015 India KIET Group of Institution Uttar Pradesh Delhi-NCR Ghaziabad201206 India

The early detection of diabetic retinopathy is crucial in preventing irreversible vision loss, making it a critical concern in healthcare. While deep learning models have shown advancements in categorization tasks, they can sometimes exhibit overconfidence in incorrect outcomes, posing significant risks in the medical field. To tackle this challenge, a novel model is proposed, which leverages a CNN (Convolutional neural network) architecture to predict softmax probabilities. The model incorporates train time augmentation to enhance performance and subsequently calibrates the projected probabilities using multiple calibration techniques, including Platt scaling, temperature scaling, isotonic regression, histogram binning, and Platt histogram. Furthermore, Test Time Data Augmentation (TTAUG) is employed to improve prediction accuracy by presenting the same data in multiple variations. The proposed technique is rigorously evaluated on benchmark datasets, namely Kaggle and Messidor. Notably, TTAUG yields remarkable results, achieving 98% accuracy on the Kaggle dataset, a significant improvement compared to the 93% achieved without augmentation. The calibration of probabilities further enhances the performance, as indicated by the reduction of Expected Calibration Error (ECE) from 0.52 to an impressive 0.019. The results underscore the significance of quantifying uncertainty in predictions, which enhances the interpretability of the proposed method as a diagnostic aid tool. By effectively addressing overconfidence and providing calibrated probabilities, the model proves its potential in improving both accuracy and reliability in diabetic retinopathy detection. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Calibration

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Trajectory-Driven Efficiency: An Energy-Efficient Opportunistic Routing Protocol for Industrial IoT Wireless Sensor Networks 10

Trajectory-Driven Efficiency: An Energy-Efficient Opportunis...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Rajulu, G. Govinda Murugesan, M. Mahaboog Basha, S.K. Kalaiselvan, S.A. Kumar, M. Lenin Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Chennai Avadi India Tkr College of Engineering and Technology Department of Computer Science and Engineering Telangana India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vignan Institute of Technology and Science Department of Electronics and Communications Engineering Telangana India

ISBN: (纸本)9798350353068

The rapid expansion of Wireless Sensor Networks (WSNs) in the realm of Industrial Internet of Things (IIoT) demands sophisticated routing protocols to ensure energy efficiency and reliable communication. This paper introduces a novel Energy-Efficient Opportunistic Routing Protocol Based on Trajectory Prediction (EORB-TP) to address these challenges in industrial environments. The research identifies the need for dynamic adaptation to industrial dynamics and changing environmental conditions, prompting the development of trajectory prediction models using Hidden Markov Models (HMMs). EORB-TP intelligently leverages these models for dynamic routing decisions, strategically optimizing data transmission paths to minimize energy consumption. The proposed system's efficiency is demonstrated through a comprehensive experimental study, showcasing superior performance in terms of network lifetime, energy consumption, end-to-end delay, and routing cost compared to existing protocols. This innovative protocol offers a promising solution for enhancing the sustainability and efficiency of Wireless Sensor Networks in Industrial IoT applications, paving the way for resilient and resource-conscious industrial monitoring systems. © 2024 IEEE.

关键词： Routing protocols

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Federated Learning Empowered Breast Cancer Detection in Images: A YOLO and ResNet-50 Fusion Approach

Federated Learning Empowered Breast Cancer Detection in Imag...

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2024 International Conference on Computational Intelligence and Computing Applications, ICCICA 2024

作者： Trivedi, Naresh Kumar Jain, Sachin Kaswan, Suresh Jain, Vishal Chitkara University Institute Of Engineering And Technology Chitkara University Punjab India Ajay Kumar Garg Engineering College Department Of Computer Science And Engineering Ghaziabad India Sharda University Sharda School Of Engineering & Technology Department Of Computer Science & Engineering UP Greater Noida India Chandigarh University Department Of Computer Science & Engineering Punjab India

ISBN: (纸本)9798350306446

In the world, breast cancer kills more women than any other disease. An accurate forecast model was created, and the highest risk was identified by a data mining-based classification effort that used many approaches. The development of automated breast cancer diagnostic tools has been enabled by advances in data mining and machine learning. It is possible to tell benign breast lesions from malignant ones during a breast cancer diagnosis. More than that, people who have had tumors surgically removed can be given prognoses that tell them when the cancer will return. Consequently, the issues surrounding categorization include these two concerns. There are a lot of data mining techniques used to categorize patients as either "cancerous"or "non-cancerous"in this field. Breast cancer diagnostic concerns are thus encapsulated in the domain of hotly debated classification issues. This work used deep learning to diagnose breast cancer via federated learning while protecting user and hospital data. This study uses YOLO and RestNet-50 DL models. YOLO trains RestNet using client data. The server receives client-trained models for global model integration. Integration gives the client access to the global model for performance evaluation. The suggested model outperforms another state-of-the-art deep learning model in the identical scenario with 98.73% accuracy when all clients receive data equally. The federated research environment has hyper-performance elements like many clients and communication cycles. © 2024 IEEE.

关键词： Object detection

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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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Assessing Social Media's Impact on Mental Health: A Gaussian Naive Bayes Approach 4

Assessing Social Media's Impact on Mental Health: A Gaussian...

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4th International Conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Nanditha, S. Joy, Helen K. Kounte, Manjunnth R. Sridevi, R. Jayarani, A. Electa Alice Vanusha, D. Christ University Department of Computer Science Bangalore560029 India School of Electronics and Communication Engineering Reva University Bangalore India Ks Institute of Technology Department of Electronics and Communication Engineering Bangalore India Srm Institute of Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350387490

In recent times, social media has gained immense popularity. However, there is a growing concern among individuals regarding its impact on mental well-being. Extensive research has been conducted to unravel the intricate connection between the use of social media and mental health. This study thoroughly examines the existing literature by incorporating various studies that cover topics such as machine learning for analysing depression-related sentiments and the effects of social media on specific demographic groups. The aim is to develop a comprehensive understanding of the relationships between these factors. In order to gain a deeper understanding of mental health indicators, a dataset consisting of 481 records was analysed using various techniques including pre-processing, correlation analysis, data visualisation, and Gaussian Naive Bayes predictive modelling. Based on our findings, it appears that individuals who engage with social media may experience negative impacts on their mental well-being. During our investigation, we conducted tests on different machine learning models and discovered that Gaussian Naive Bayes yielded the highest level of accuracy. The presence of multicollinearity among independent variables emphasises the importance of being cautious when interpreting research findings. This study makes a valuable contribution to our understanding of the complexities involved in using social media platforms while managing mental health issues. By focusing on the intersection of technology and psychiatric illnesses, it lays the groundwork for future research and interventions in this important area. © 2024 IEEE.

关键词： Adversarial machine learning

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Sign Ease: AI-Based Gesture Recognition

Sign Ease: AI-Based Gesture Recognition

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Srivastava, Sudiksha Ali, Parvez Yadav, Shivendram Khan, Aaman Pandey, Praveen Lucknow India Babu Banarasi das Institute of Technology and Management Department of Computer Science and Engineering Lucknow India

ISBN: (纸本)9798331508685

The research aims to find how effective various models are based on deep learning, particulary - Convolutional Neural Networks (CNN), VGG-16, MobileNet V2, Inception V3, and ResNet-50 - on the ISL dataset, using transfer learning techniques with pre-trained ImageNet weights to improve model performance. We assess the models using rigorous evaluation metrics, including F1-score, accuracy, detection rate, loss, and specificity. Our results reveal that ResNet-50 outperforms the other models, achieving exceptional evaluation accuracy, specificity (98.41%), detection rate (99.27%), and F1-score (99.34%). The superior performance of ResNet-50 is attributed to its deep architecture, residual connections, and efficient handling of the vanishing gradient problem, which allow it to capture complex features more effectively. While VGG-16 shows promising results with a test accuracy of 97.37%, Inception V3 and MobileNet V2 demonstrate limited effectiveness, indicating their lower suitability for this specific task. These findings highlight the importance of model architecture in achieving high performance for the ISL dataset. © 2025 IEEE.

关键词： Convolutional neural networks

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Generative Adversarial Networks (GANs) in the Wild: Applications, Challenges, and Future Prospects 2

Generative Adversarial Networks (GANs) in the Wild: Applicat...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Kumar, Rakesh Chaudhary, Pooja Gupta, Shilpi Singhal, Manoj Gla University Department of Computer Engineering & Applications UP Mathura India UP Ghaziabad India Meerut Institute of Technology Department of Computer Science & Engineering UP Meerut India Gl Bajaj Institute of Technology & Management Department of Information Technology UP Greater Noida India

ISBN: (纸本)9798350371055

Generative adverse Networks (GANs) have become increasingly famous for a variety of tasks in picture and audio processing, especially unsupervised getting to know. GANs encompass components, a 'generator' and a 'discriminator,' that are skilled adversarial. The generator is tasked with producing a pattern of statistics, and the discriminator is tasked with figuring out whether the information is 'actual' or 'fake.' The generator 'learns' through trial and mistakes as it attempts to create samples that appear practical to the discriminator. Through the antagonistic schooling manner, the generator can create more accurate 'fake' records while the discriminator can emerge as better at distinguishing between 'actual' and 'faux' records. GANs have been applied to various duties, which include picture era, textual content to photosynthesis, photo-to-image translation, and automatic music composition. Recent tries have been made to apply GANs to extra complicated obligations in the wild, including self-reliant use and drug discovery. GANs have inspired an ever-developing quantity of industries because of their potential to generate sensible facts with minimal human input. © 2024 IEEE.

关键词： antagonistic complicated discriminator photosynthesis potential

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Room-temperature waveguide-integrated photodetector using bolometric effect for mid-infrared spectroscopy applications

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光：科学与应用（英文版） 2025年第5期14卷 1300-1311页

作者： Joonsup Shim Jinha Lim Inki Kim Jaeyong Jeong Bong Ho Kim Seong Kwang Kim Dae-Myeong Geum SangHyeon Kim School of Electrical Engineering Korea Advanced Institute of Science and Technology(KAIST)291 Daehak-RoYuseong-GuDaejeon 34141Republic of Korea Department of Electrical & Computer Engineering Inha University100Inha-roMichuhol-guIncheon 22212Republic of Korea

Waveguide-integrated mid-infrared(MIR)photodetectors are pivotal components for the development of molecular spectroscopy applications,leveraging mature photonic integrated circuit(PIC)*** various strategies,critical challenges still remain in achieving broadband photoresponse,cooling-free operation,and large-scale complementary-metal-oxide-semiconductor(CMOS)-compatible *** leap beyond these limitations,the bolometric effect-a thermal detection mechanism-is introduced into the waveguide *** importantly,we pursue a free-carrier absorption(FCA)process in germanium(Ge)to create an efficient light-absorbing medium,providing a pragmatic solution for full coverage of the MIR spectrum without incorporating exotic materials into ***,we present an uncooled waveguide-integrated photodetector based on a Ge-on-insulator(Ge-OI)PIC architecture,which exploits the bolometric effect combined with ***,our device exhibits a broadband responsivity of 28.35％/mW across 4030-4360 nm(and potentially beyond),challenging the state of the art,while achieving a noise-equivalent power of 4.03×10-7W/Hz0.5 at 4180 *** further demonstrate label-free sensing of gaseous carbon dioxide(CO2)using our integrated photodetector and sensing waveguide on a single *** approach to room-temperature waveguide-integrated MIR photodetection,harnessing bolometry with FCA in Ge,not only facilitates the realization of fully integrated lab-on-a-chip systems with wavelength flexibility but also provides a blueprint for MIR PICs with CMOS-foundry-compatibility.

关键词： applications temperature integrated room bolometric effect mid-infrared photodetector spectroscopy using

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An AI-Based Prediction Model for Climate Change Effects on Crop production using IoT

An AI-Based Prediction Model for Climate Change Effects on C...

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2023 International Telecommunications Conference, ITC-Egypt 2023

作者： Elghamrawy, Sally MISR Higher Institute for Engineering and Technology Computer Engineering Department Mansoura Egypt Egypt

ISBN: (纸本)9798350326062

Climate change influences agriculture's sustainability over different aspects. This paper proposes a prediction model grounded on Artificial intelligence techniques to predict the climate change effects on the Crop production. The proposed model is known as the Prediction Model for Climate Change Effects on Crop production (PMCCE). PMCCE utilizes the Artificial intelligence techniques along with the IoT technology to predict the crop production taking in consideration the climate change factors as: Annual Precipitation, Climate impacts per Celsius °C, Co2 concentrations, and temperature. The proposed model provides decision-makers with a comprehensive overview of the suitability of number of the most needed crops (namely, maize, wheat, and rice) for both short-term and long-term cultivation. An optimized Convolutional Neural Network(CNN) is proposed in PMCCE to predict the most appropriate crop based on climate change data and to predict the most significant factors affecting crop production. The experimental results indicate that the proposed model efficiently predicts the most suitable crop that enhances yield production and is better suited to cultivation under specific climate change scenarios. Furthermore, PMCCE outperforms other prediction models that used different methods, as the accuracy of the PMCCE model for the three targeted crops, achieved 98.2%, 98.69%, and 98.1% for Wheat, Maize, and Rice, respectively. Additionally, the PMCCE model identified the most significant factors in climate change data that affect crop production: CO2 and the average temperature were the most significant factors that affects the crops production, then climate impacts, climate scenario and adaptation type. © 2023 IEEE.

关键词： Climate change

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