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EfficientNetB3-Based Lung Cancer Classification from CT Scan Images: A Deep Learning Approach 4

EfficientNetB3-Based Lung Cancer Classification from CT Scan...

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

作者： Kaur, Gurjot Sharma, Neha Chitkara University Department of Computer Science and Engineering Punjab India Chitkara University Department of Electronics and Communication Engineering Punjab India

ISBN: (纸本)9798350387490

In the field of medical diagnostics, lung cancer disease identification is a vital task. Deploying deep learning technology with EfficientNetB3 architecture as its powerhouse seeks to raise the accuracy and reliability of lung cancer classification in this study. The dataset consists of 1,187 high-resolution images showing lung cancer pathology in a variety of forms. Using the EfficientNetB3 model, 40 iterations of training have been carried out, batch size 20, and learning rate 0.001 are used. This enables the model to pick up on fractional changes that are symptomatic of different kinds of lung cancers. Model training is an arbitrary manipulation of model parameters designed to fine-tune them. The evaluation method for model performance is based on its capacity to recognize CT scan images like that from lung cancer correctly. The proposed EfficientNetB3 model, achieving an accuracy rate of 93.33%, proves remarkably successful. This result indicates the model's proficiency in distinguishing between different types of lung cancer. This enables more accurate diagnosis and treatment planning. And the findings of this recalibrate routes through which patient care is delivered. It hopes to replace conventional medical models with more efficient ones by providing diagnosis capabilities for forms of lung cancer that are both accurate and timely. Thus, misdiagnosis will decrease and targeted treatments can be pursued more effectively. Plus, the research's success illustrates the feasibility of deep learning strategies particularly EfficientNetB3 architecture for promoting medical imaging analysis and health care provision. © 2024 IEEE.

关键词： Lung cancer

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The Traffic-Aware Scatter Net Scheduling (TAAS) for Multicast Wireless Protocols 2

The Traffic-Aware Scatter Net Scheduling (TAAS) for Multicas...

引用

2nd IEEE International Conference on Distributed Computing and Electrical Circuits and electronics, ICDCECE 2023

作者： Hamsa, S. Devaraj Verma, C. Department of Electronics and Communication Engineering Bangalore India Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

The Traffic-Aware Scatter Net Scheduling (TASS) algorithm is an advanced scheduling strategy designed to reduce the latency of the data transmission in wireless networks. It is a hybrid technique that combines both the advantages of a random-access scheduling scheme and a deterministic scheduling scheme. The TASS algorithm is based on two main components: the "scatter"and the "aware."The scatter part of the algorithm randomly assigns nodes to different time slots, while the aware part of the algorithm takes into account the current network traffic. By using the aware part of the algorithm, the TASS algorithm can schedule transmissions in such a way that it reduces the latency of data transmission. The main advantages of the TASS algorithm are its ability to reduce the latency of data transmission and its flexibility in adapting to changes in the network traffic. The algorithm is also designed to be resilient to any changes in the network topology, allowing it to quickly adapt to changing network conditions. Furthermore, the algorithm is designed to be energy efficient, as it minimizes the number of control messages that need to be sent and received. © 2023 IEEE.

关键词： Wireless networks

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Fractional Sparrow Optimization- Light Sensor Positioning Enabled Semiconductor Optical Amplifier based Digital Signal Processing Model 2

Fractional Sparrow Optimization- Light Sensor Positioning En...

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

作者： Deepa, K. Singh, Ashish Pandu, N.K. Department of Electronics and Communication Engineering Karnataka Nitte574110 India Department of Computer Science and Engineering Karnataka Moodbidri574225 India

ISBN: (纸本)9798350368413

An optimized Dense wavelength-division multiplexing (DWDM) optical network based on semiconductor optical amplifiers (SOAs) plans to improve its ability of signal transmission processing to eliminate the badly negative nonlinear effects caused by high-intensity optical signals. In current models, several issues exist which include nonlinearities, issues to do with optimal performance of SOA, and compatibility with the existing systems. Therefore to conquer those existing challenges in this research develop a new model optimized fractional sparrow optimization- Light Sensor Positioning based semiconductor optical amplifier-based digital signal processing method (FSO-LSP-SOA-DSP). In this research, a new model of optimized FSO is proposed along with the placement of light sensors in a semiconductor optical amplifier-based DSP technique. This new method is to change the signal quality in the DWDM system by efficiently using the FSO for fine-tuning the parameters and the harness of the light sensors placed strategically to capture and process the signals effectively. The proposed model includes optimization of the position of the light sensor and FSO which helps to provide a fuller solution to the problems associated with the non-linearities and signal fading in the optical communication system. The results show that the models have low error rates of 1.19, 3.16, 0.5557, 0.26, 56170.00, and 1.167 during a number of channels (NOC) 50 for bit error rate (BER), error vector magnitude (EVM), generalized mutual information (GMI), mutual information (MI), symbol error rate (SER), and signal to noise ratio (SNR) during CH analysis. © 2024 IEEE.

关键词： Semiconductor optical amplifiers

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Ensembled Machine Learning Models for Antenna Optimization in Wireless communication/ Biomedical Applications 3

Ensembled Machine Learning Models for Antenna Optimization i...

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3rd International Conference on communication, Control, and Intelligent Systems, CCIS 2024

作者： Upadhyay, Nidhi Kaur, Amanpreet Singh, Ashima Pattanayak, Arnab Thapar Institute of Engineering and Technology Department of Electronics and Communication Engineering Punjab India Thapar Institute of Engineering and Technology Department of Computer Science Engineering Punjab India

ISBN: (纸本)9798331528201

In this work, the antenna optimisation process is done by using ML algorithms such as KNN, random forest, Gradient Boost, XG Boost and MLP. Then the predicted results are ensembled using Voting Regressor. A Cylindrical Dielectric Resonator Antenna (CDRA) was designed and simulated in CST MWS'23 for this purpose. Dataset is created by varying the radius of the DR using parametric sweep function of the EM simulator. A close prediction between actual values and the predicted ones is obtained using these ML models. It is observed that by ensembling these varied approaches, a model that can handle different types of relationships in the data, can be created. This algorithm can be further utilized for optimisation of other antenna parameters and thus helping in reducing time in optimising complex antenna designs. The accuracy achieved by the ensemble model is 98.6% while maintaining a small MSE of 0.001. The optimized design shows stable characteristics within the ISM band and hence can be used for biomedical as well as wireless communication applications. © 2024 IEEE.

关键词： Random forests

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An Image Processing Approach to Enhance Driver Distraction and Drowsiness Detection Algorithm 3

An Image Processing Approach to Enhance Driver Distraction a...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Paul, Aishik Shaha, Abhisri Mukherjee, Arijit Sen, Anindya Heritage Institute of Technology Electronics and Communication Engineering Kolkata India Heritage Institute of Technology Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350381931

Some of the major causes that lead to road accidents are drowsiness, fatigue, or distraction of the driver. Drowsiness has become a growing problem with long distance drivers. The primary causes of drowsiness are long shifts at work and sleep deprivation. Sometimes, transport companies do not rotate drivers regularly between shifts. Thus, due to driving for an extended period, they feel sleepy. Such cases may lead to fatal accidents that may cause many casualties (for travel companies), tragic loss to a family, loss of goods (for goods transport companies), and may also cause massive economic losses. The signs of distraction and sleepiness in a driver can be discerned through their facial posture. The most popularly used methods to detect drowsy feeling of the driver are by calculating Eye Aspect Ratio (EAR) and Yawn Aspect Ratio (YAR) using landmarks of the face. The system activates alarm once the features extracted from the facial and body landmarks exceed generalized threshold value. In addition to EAR and YAR, the system includes features like Front Tilt Ratio, Side Tilt Angle, and Head-Shoulder Ratio. These features collectively contribute to detect the steadiness of driver. Experiments show that the algorithm has 91.8% detection accuracy. Our proposed solution aims to overcome the shortcomings in drowsiness detection. © 2024 IEEE.

关键词： Aspect ratio

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Improving Cloud Data Security with Alexnet Learning Model Compare Stochastic Gradient Descent with Accuracy 3

Improving Cloud Data Security with Alexnet Learning Model Co...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Thriveni, E. Mahaveerakannan, R. Sudhakar, K. Department of Computer Science and Engineering Tamilnadu Chennai602105 India Department of Electronics and Communication Engineering Tamilnadu Chennai602105 India

ISBN: (纸本)9798350372120

The suggested method of protecting cloud data involves setting up a server to function as a third party, similar to Alexnet, and using Advanced Encryption Standard for data stored in private clouds. Stochastic Gradient Descent is used to calculate accuracy the user and the cloud in order to ensure security between the two parties. Supplies and Procedures: In this work, two groups - AlexNet and Stochastic Gradient Descent - are compared. For statistical analysis, 1300 instruction and 423 testing dataset samples from the UNSW-NB15 dataset were used. N=10, an alpha value of 0.05, a G-Power value of 0.8%, and a 95% confidence interval were employed in the ClinCalc software for each approach. Findings and Discussion: Novel Alexnet (91.081%) exhibits better accuracy than SGD (87.9396%). The significance value, as determined by looking at the Independent Sample t-Test findings, is 0.011(p © 2024 IEEE.

关键词： Cryptography

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IoT-Assisted AI Framework for Preserving Biodiversity: Monitoring Rare Trees in Dynamic Forest Ecosystems

IoT-Assisted AI Framework for Preserving Biodiversity: Monit...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Rajan, K. Palanivel Raj, J. Relin Francis Krishnan, R. Santhana Balamuralikrishnan, G. Vidyasree, G. Jothi, J. Nirmal Psna College of Engineering and Technology Department of Electrical and Electronics Engineering Dindigul India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Velammal College of Engineering and Technology Department of Computer Science and Engineering Madurai India Sri Sai Ranganathan Engineering College Department of Computer Science and Engineering Coimbatore India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Avadi India

ISBN: (纸本)9798331509828

The preservation of biodiversity, particularly the monitoring of rare tree species in dynamic forest ecosystems, presents significant challenges due to the complexities in detecting and analyzing forest changes. This research introduces a novel IoT -assisted AI framework, termed FoLiA-Net (Forest Lightweight Adaptive Network), designed to improve the efficiency and accuracy of monitoring rare trees. The proposed algorithm combines IoT sensor data with machine learning techniques to adaptively detect, classify, and track rare tree species across large and dynamically changing forest areas. The framework utilizes a lightweight neural network structure to ensure real-time processing while minimizing computational resources, making it suitable for deployment in resource-constrained environments. Simulation experiments were conducted to evaluate the performance of FoLiA-Net in comparison with existing algorithms, including Faster R-CNN (F-R-CNN), Random Forest Classifier (RFC), and Gradient Boosting Machines (GBM). The evaluation was based on several key performance metrics such as accuracy, latency and detection efficiency. The results revealed that FoLiA-Net outperformed the existing algorithms in terms of accuracy and computational efficiency, demonstrating a notable improvement in the monitoring of rare tree species in diverse forest ecosystems. The proposed framework is particularly effective in large-scale forest monitoring, providing a valuable tool for biodiversity conservation efforts © 2025 IEEE.

关键词： Biodiversity Forest Ecosystems Machine Learning Rare Trees Simulation Sustainability

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An Optimized Plant Disease Classification System Based on Resnet-50 Architecture and Transfer Learning 4

An Optimized Plant Disease Classification System Based on Re...

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4th International Conference for Emerging Technology, INCET 2023

作者： Balavani, Kola Sriram, Dasari Shankar, Malla Bhavani Charan, Devavarapu Sri Department of Electronics and Communication Engineering Mylavaram India Department of Computer Science and Engineering Mylavaram India

ISBN: (纸本)9798350335750

Automated plant disease diagnosis is a vital task in modern agriculture to ensure timely and effective management and reduce crop losses. This paper proposes a deep learning-based plant disease classification model using ResNet-50 architecture, which offers several advantages over existing methods. ResNet-50 allows accurate identification of plant diseases from images with high precision and recall, even when the diseases have similar visual characteristics. The ResNet-50 architecture has skip connections that help in reusing the learned features from previous layers, overcoming the problem of vanishing gradients and allowing it to train on deeper networks, resulting in improved accuracy compared to shallower models. Furthermore, transfer learning is utilized in the proposed model, enabling the use of pre-trained weights from a large dataset of general images, followed by fine-tuning on the plant disease dataset. This enhances the model's performance by leveraging the pre-trained weights' knowledge, leading to faster convergence, improved accuracy, and reduced training time. The model is trained and tested on a comprehensive dataset from 38 different categories of plant diseases and healthy leaves. This ensures that the model is robust and can accurately identify and classify diseases across various plant species. By leveraging the advantages of the ResNet-50 architecture and transfer learning, the proposed model can accurately and efficiently diagnose plant diseases, leading to improved agricultural productivity and reduced losses due to plant diseases. © 2023 IEEE.

关键词： Image classification

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Implementation of YOLOv9 in Agricultural AI for Enhanced Weed Detection

Implementation of YOLOv9 in Agricultural AI for Enhanced Wee...

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2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology, IC-SIT 2024

作者： Duraisamy, Premkumar Deepika, A. Niranjani, V. Jeevitha, R. Sibishree, M. Oviya, A. KPR Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India KPR Institute of Engineering and Technology Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9798350369175

Weeds significantly hinder agricultural productivity by reducing crop yields and increasing production costs. Leveraging artificial intelligence (AI) is critical for equipping farmers with early detection capabilities to implement effective weed management strategies. Among AI technologies, deep learning (DL) techniques are especially effective for analyzing agricultural field images to identify weed species. This paper reviews state-of-the-art DL methodologies, with a focus on the YOLOv9 model for weed detection. Our findings highlight YOLOv9's superior effectiveness, achieving an accuracy of $\mathbf{9 0 \%}$, due to its robust architecture and advanced feature extraction. The model's proficiency in accurately identifying weeds is evident from our thematic analysis. We also explore various image acquisition devices, including robots, drones, and mobile phones. Comparative evaluations show that YOLOv9 consistently outperforms other DL techniques, achieving remarkable accuracy and speed. This study includes a comparative analysis of several algorithms like SSD, Mask R-CNN, and Fast R-CNN, underscoring YOLOv9's superior performance. This paper serves as a valuable resource for researchers and practitioners, guiding future efforts in weed management and precision agriculture by emphasizing YOLOv9's transformative potential. © 2024 IEEE.

关键词： Agricultural productivity Deep learning Image acquisition object detection Weed detection YOLOv9 algorithm

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The Smart Development of Multichip communication Systems with The Help of Short Transmission Nodes 2

The Smart Development of Multichip Communication Systems wit...

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2nd IEEE International Conference on Distributed Computing and Electrical Circuits and electronics, ICDCECE 2023

作者： Sunil, M.P. Devaraj Verma, C. Department of Electronics and Communication Engineering Bangalore India Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350347456

Multichip communication systems offer faster and more efficient data transmission and processing than single-chip systems. Multichip communication systems are made up of multiple chips that are interconnected and communicate with each other to carry out tasks more quickly. By using multiple chips, more complex operations can be completed faster and more efficiently than if done by a single processor. Multichip communication systems are used in many different industries, from automotive to aerospace to consumer electronics. Short transmission nodes are nodes that are used to transfer data from one network to another, usually over shorter distances. They are often used in the networking of computers, as they allow data to be sent more quickly and securely than over long distances. Short transmission nodes are also frequently used in wireless networks, where they provide a more reliable connection than longer distance nodes. In general, short transmission nodes are designed to be more efficient than longer distance nodes, allowing for faster data transfer speeds and more reliable connections. They are also often used for security purposes, as they are harder to intercept than signals passing over longer distances. © 2023 IEEE.

关键词： Data transfer

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