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Involutional Neural Network Based Approach for the Detection of Melanoma Cancer

Involutional Neural Network Based Approach for the Detection...

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2024 International Conference on computer, Electronics, Electrical engineering and their Applications, IC2E3 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India

ISBN: (纸本)9798350388534

This paper presents an extraordinary method to detected melanoma, the most lethal form of skin cancer, using Involutional Neural Networks (INNs). Melanoma's early detection is crucial for effecacius treatment, yet existing diagnostic methods often lack the requiring precision and scalability. INNs offer a solve through their advanced capability to adaptively process spatial information in images, a huge different from traditional convolutional techniques. Our reaserch applies INNs to melanoma detectionair, focusing on their unique involution operations that significant enhance image analysis for accurately classification. The performance of our model, assessed across different metrics including accurate, precision, recall, and F1-scores, demonstrates its superior diagnostic capability. This investigation not only underscores the potency of INNs in medical imagining but also opens up new avenues for their application across diverse analytical needs in healthcare. By advancing melanoma detection methods, we contribute to the broader goal of improving early cancer diagnose and treatment outcomes! © 2024 IEEE.

关键词： Lung cancer

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An Ensemble ML Model to Predict the Wastage of Food: Towards Achieving the Food Sustainability

An Ensemble ML Model to Predict the Wastage of Food: Towards...

引用

2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Masuk Shopnil, Md Masrur Husna, Asmaul Sultana, Shaheena Islam, Muhammad Nazrul Military Institute of Science and Technology Dept. of Computer Science and Engineering Dhaka Bangladesh NotreDame University Bangladesh Dept. of Computer Science and Engineering Dhaka Bangladesh

ISBN: (纸本)9798350316001

Food waste is a major issue in students' hostel or dormitories, where uneaten food often ends up in the garbage, resulting in economic losses as well as reducing the food sus-tainability. Although Machine Leaning (ML) has emerged as a promising tool for predicting future events in several files, but a very few has been focused to generate accurate predictions and reducing food waste by analyzing read data for a particular context or a country. Therefore, the objective of this research is to explore the factors of affecting food waste in students' hostel or dormitories and to develop an ensemble ML-based food waste prediction system for the student hostel or dormitories. In order to attain these objectives, this study firstly find out the 18 key features that ensure a comprehensive understanding of food waste patterns;then collected the data against these features over the last six months from various mess records of a students' hostel of an engineering university situated in Dhaka, Bangladesh. Secondly, six classification models were developed and analyzed their results and found that Random Forest and Decision Tree performed best accurate prediction (75.41%). Thirdly, the all possible combinations of the studies ML models were explored to find out the best possible ensemble combination;as such, the proposed ensemble ML model showed the highest F1-score of 86.88% that considers the Random Forest and AdaBoost;while all other possible combinations showed around 80-82% F1-score. This research thus contributes to the broader goal of reducing food waste and promoting sustainability in students' mess and other food service settings;as well as may assist the management team in making data-driven decisions about food production, purchasing, and resource allocation. © 2023 IEEE.

关键词： Forecasting

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AI-Enabled Cyber Physical System And Battery Life Estimation For Smart Grid Applications 49

AI-Enabled Cyber Physical System And Battery Life Estimation...

引用

49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： N.k, Anantha Padmanabhan Kumar, Aman Chitransh, Varun Singh, Rajeev Kumar Lal, Vivek Nandan Singh, Sanjay Kumar Dept. of Computer Science and Engineering Varanasi India Dept. of Electrical Engineering Varanasi India

ISBN: (纸本)9798350331820

In this paper, the development of an efficient three- layer Cyber Physical System (CPS) using a multi-output power electronic interface integrated with IoT-enabled modules and AI- based algorithms is presented. The power electronic interface layer of the proposed CPS eliminates the need of bulky electrolytic capacitors and reduces the switching loss. This layer facilitates DC to DC and DC to three-phase AC power conversion for battery charging and smart-grid applications respectively. AI-based algorithms are utilized for accurately predicting the Remaining Useful Life (RUL) of batteries. The performance of the proposed CPS is validated through simulation and experimental measurement results using a 700 W prototype system. The measurement results show that the proposed CPS enables real-time monitoring, accurate prediction of RUL, predictive maintenance decisions, data analytics, and optimized power flow. © 2023 IEEE.

关键词： Cyber Physical System

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An Artificial Intelligence Approach in 5G New Radio Beam Enhancement 3

An Artificial Intelligence Approach in 5G New Radio Beam Enh...

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3rd International Conference on Secure Cyber Computing and Communications, ICSCCC 2023

作者： Upadhyay, Deepak Mittal, Saksham Jain, Ayushi Sharma, Ravi Bagla, Piyush Tripathi, Neha Tiwari, Pallavi Graphic Era Hill University Dept. of Computer Science & Engineering Uttarakhand Dehradun India National Institute of Technology Computer Science & Engineering Dept. Punjab Jalandhar India Dept. of Computer Science & Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798350300710

The paper presents a novel application of artificial neural networks in the context of 5G new radio beamforming. The research leverages the intricate mechanism of ANNs to demonstrate the efficacy of this approach in optimizing the beamforming process and improving the overall performance of the 5G new radio system. The utilization of ANNs allows for real-time adaptation and decision-making, thereby mitigating the limitations of conventional beamforming techniques. The results of the study are quite promising, indicating a substantial enhancement in the accuracy and efficiency of beamforming in 5G new radio. The proposed method is expected to have a significant impact on the development of advanced wireless communication systems. © 2023 IEEE.

关键词： Beamforming

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An Ensemble Deep Learning Framework for Binary Classification of COVID-19 Cases Using the VGG16 (Visual Geometry Group 16) Architecture: A Comprehensive Analysis and Performance Evaluation 2

An Ensemble Deep Learning Framework for Binary Classificatio...

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Upadhyay, Deepak Gupta, Mridul Mishra, Somesh Mittal, Saksham Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Electronics and Communication Engineering Dehradun India Graphic Era Deemed to Be University Dept. of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350371055

This paper presents a novel approach to deep learning by putting forth a cooperative system that uses the VGG16 architecture to categorise COVID-19 examples into two groups. Our model is distinguished by its remarkable recall metrics and precision, which achieve a careful balance that is essential for accurate categorization. What's more impressive is how well the model performs in the non-COVID category, effectively differentiating between COVID and non-COVID cases. With a remarkable overall accuracy of 96%, the model successfully classifies cases from both groups, demonstrating the potential of our suggested framework as a useful diagnostic tool useful in various clinical contexts. This work clarifies the effectiveness of deep learning techniques, concentrating on the VGG16 architecture in the crucial job of binary classification for COVID-19 identification. Our results open up new avenues for investigation in the field of accurate medical diagnosis in addition to providing insights into the real-world applications of sophisticated machine learning. The study highlights the ensemble approach's encouraging benefits, showing how it may strengthen diagnostic precision and advance clinical decision-making. © 2024 IEEE.

关键词： AI Machine Learning Neural Network VGG16

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Exploring the Impact of Univariate Feature Selection Method on Machine Learning Algorithms for Heart Disease Prediction

Exploring the Impact of Univariate Feature Selection Method ...

引用

2023 International Conference on Next-Generation Computing, IoT and Machine Learning, NCIM 2023

作者： Islam, Mainul Islam, Rafiqul East West University Dept. of Computer Science and Engineering Dhaka Bangladesh Dhaka University of Engineering & Technology Dept. of Computer Science and Engineering Gazipur Bangladesh

ISBN: (纸本)9798350316001

This study compares various machine learning techniques such as logistic regression, support vector machine (SVM), random forest, Naïve Bayes, and decision tree. Later, a feature selection approach that considers the Univariate Feature Selection (UFS) method was applied to select the best features based on statistical testing. In this research, we employed the ANOVA F-test to pick the most significant features, and we used a cutting-edge dataset for experimental analysis. The ANOVA F-test was used to exploit the target variable's dependency on the continuous variables. The results show that employing the UFS approach for picking features improves the performance of the algorithms. According to the experimental results, using UFS with the logistic regression technique yields 93.3% accuracy, whereas the standalone Naïve Bayes algorithm achieved the best accuracy. © 2023 IEEE.

关键词： Feature Selection

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Channel Capacity Enhancement in Dual Branch MRC under Extreme Fading Using Quantum Computing

Channel Capacity Enhancement in Dual Branch MRC under Extrem...

引用

2024 IEEE Region 10 Symposium, TENSYMP 2024

ISBN: (纸本)9798350364866

We have investigated the potential of channel capacity and signal integrity enhancement in dual branch MRC systems even under extreme fading through classical and quantum computing. We have used the classical algorithm to gauge the performance of the quantum algorithm, including QAOA and QEC. Our simulation results show that quantum-assisted MRC work optimally by providing superior SNR and BER. Quantum algorithms not only maximizes the effective channel capacity but also minimizes end-to-end system latency and number of error rate more efficiently. The addition of Quantum Machine Learning for the dynamic parameter setting in real-time has demonstrated that it may be possible to combat severe fading effects. To the best of our knowledge, this paper laid the cornerstone to explore more opportunities in implementing quantum-enabled techniques for advanced telecommunication system development to realize more robust and efficient technologies. © 2024 IEEE.

关键词： Quantum channel

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Speech Emotion Recognition Using ML 7

Speech Emotion Recognition Using ML

引用

7th IEEE International Conference on Computational Systems and Information Technology for Sustainable Solutions, CSITSS 2023

作者： Niharika, S.M. Soumya, A. Rv College of Engineering Dept. of Computer Science & Engineering India

ISBN: (纸本)9798350343144

Due to its numerous uses in industries including human-computer interaction, healthcare, and entertainment, speech emotion recognition (SER) has attracted a lot of interest recently. The use of Convolutional Neural Networks (CNN) in combination with the Librosa library for SER is used to recognise emotions from audio data. The capacity to automatically recognise and categorise emotions from spoken words can revolutionise many applications since emotions are a crucial component of human communication. Here, CNNs-known for their skill in image analysis-are adapted to the spectrogram representation of audio, efficiently identifying distinguishing characteristics. To improve the reliability of emotion identification, Librosa, a Python library for music and audio analysis, offers crucial capabilities for preprocessing and feature extraction from audio data. The architecture of the CNN model, which includes of convolutional layers for feature extraction and pooling layers for dimensionality reduction, is required to build the model. To increase model generality, methods for data augmentation and transfer learning are employed. The two feature extraction capabilities of Librosa, chroma features and mel-frequency cepstral coefficients (MFCCs), are emphasised for their effectiveness in extracting emotional cues from audio data. It has been proved via considerable research that CNNs and Librosa together produce state-of-the-art results in a variety of emotion identification tasks, considerably improving SER accuracy. This study has broad repercussions, from enhancing the emotional intelligence of virtual assistants to helping mental health practitioners monitor patients' emotional health through speech analysis. In summary, the combination of CNNs and Librosa in SER offers a potential path for developing the discipline of emotional analysis in speech, resulting in more emotionally intelligent technology and a deeper comprehension of human emotions. © 2023 IEEE.

关键词： Feature extraction

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Online Transit Entanglement Routing in Quantum Networks: Architecture Design and Optimization

Online Transit Entanglement Routing in Quantum Networks: Arc...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ho, Wan-Ting Fang, Shing-Yan Hsieh, Wei-Chia Cheni, Li-Feng Kuo, Jian-Jhih Tsai, Ming-Jer National Tsing Hua University Dept. of Computer Science Hsinchu Taiwan National Chung Cheng University Dept. of Computer Science and Information Engineering Chiayi Taiwan

ISBN: (纸本)9798350351255

Quantum networks (QNs) gradually gain significant attention due to their higher security compared with classical networks. Conventional approaches in QN routing often aggregate multiple requests into a batch before determining their routing paths. However, this approach may overlook the limited lifetime of qubits, resulting in critical decoherence. In this paper, we present a new online entanglement routing architecture with an online optimization problem and propose a novel [1, O(log |V|)]-competitive algorithm supporting online requests with admission control, aiming to maximize the number of admitted requests. Finally, extensive simulation results show that our algorithm can outperform the existing approaches by up to 98%. © 2024 IEEE.

关键词： Quantum entanglement

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Faulty Classes Prediction in Object-Oriented Programming Using Composed Dagging Technique 3

Faulty Classes Prediction in Object-Oriented Programming Usi...

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3rd International Conference on Electrical, computer and Communication engineering, ECCE 2023

作者： Mahfuz, Nagib Shill, Pintu Chandra North Western University Dept. of Computer Science and Engineering Khulna Bangladesh Khulna University of Engineering and Technology Dept. of Computer Science and Engineering Khulna Bangladesh

ISBN: (纸本)9798350345360

Class is one of the fundamental concepts of the object-oriented paradigm and has been scrutinized since the developers moved on from procedural programming design. In software fault prediction, the legalization of software metrics is essential. As a handful of software metrics suites exist, it is a very hard task to predict the defective classes flawlessly using a particular set of metrics suites. However, it is a rational approach to use only the object-oriented metrics that are directly relatable to the class definitions in the code that helps the developers foresee the errors in defining the classes and minimize the errors as much as possible. This paper utilized twelve object-oriented metrics selected from various metrics suites. The dagging ensemble model is merged with three well-known classification algorithms (Naive Bayes, Multilayer Perceptron, J48 Decision Tree) individually and applied to twelve java projects. The study depicts that the proposed ensemble method gives improved outcomes that are statistically significant when merged with Naive Bayes and Multilayer Perceptron. The proposed ensemble method shows improvements up to 12.5% in accuracy and 15% in F-Score. © 2023 IEEE.

关键词： Object oriented programming

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