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Deep learning based 3D multimodal CNN for brain tumor segmentation and detection

Iran Journal of computer Science 2024年第4期7卷 843-859页

作者： Tiwari, Aniket Kumar, Divyansh Sidh, Hanuman Sahare, Parul Diwan, Tausif Satpute, Vishal Department of Electronics and Communication Engineering Indian Institute of Information Technology Nagpur Nagpur India Department of Computer Science and Engineering Indian Institute of Information Technology Nagpur Nagpur India Department of Electronics and Communication Engineering Visvesvaraya National Institute of Technology Nagpur Nagpur India

Brain tumors present a significant challenge to healthcare professionals and can impact individuals of any age. Despite advancements in medicine, early detection and effective treatment remain challenging, often resulting in poor patient outcomes. To address this issue, a convolutional neural network (CNN) model is proposed for segmenting three-dimensional (3D) computed tomography (CT) images to predict the type of brain tumor present. This approach involves applying various pre-processing techniques to the widely used BraTS2020 and BraTS2021 datasets, which are essential in brain tumor research. The pre-processing methods aim to enhance the quality of the CT images, making them suitable for use in a 10-layer CNN architecture for brain tumor classification and prediction. By accurately identifying the type of brain tumor, the most effective treatment strategy can be determined. Extensive experiments on these datasets show that the proposed approach outperforms other contemporary 3D segmentation models, achieving superior recall, precision, Hausdorff, and F1-Score metrics, with Dice Similarity Coefficients of 89.63% and 88.96%, respectively. The proposed method demonstrates significant potential to improve the early detection and treatment of brain tumors, leading to better patient outcomes. Using a CNN model for image segmentation and classification allows healthcare professionals to identify and classify brain tumors accurately and efficiently, enabling the most effective treatment strategies for their patients.

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Amplifier-Enhanced Memristive Massive MIMO Linear Detector Circuit: An Ultra-Energy-Efficient and Robust-to-Conductance-Error Design

Amplifier-Enhanced Memristive Massive MIMO Linear Detector C...

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2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Bi, Jia-Hui Yang, Shaoshi Zhang, Ping Chen, Sheng Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China University of Southampton School of Electronics and Computer Science SouthamptonSO17 1BJ United Kingdom

ISBN: (纸本)9798350351255

The emerging analog matrix computing technology based on memristive crossbar array (MCA) constitutes a revolutionary new computational paradigm applicable to a wide range of domains. Despite the proven applicability of MCA for massive multiple-input multiple-output (MIMO) detection, existing schemes do not take into account the unique characteristics of massive MIMO channel matrix. This oversight makes their computational accuracy highly sensitive to conductance errors of memristive devices, which is unacceptable for massive MIMO receivers. In this paper, we propose an MCA-based circuit design for massive MIMO zero forcing and minimum mean-square error detectors. Unlike the existing MCA-based detectors, we decompose the channel matrix into the product of small-scale and large-scale fading coefficient matrices, thus employing an MCA-based matrix computing module and amplifier circuits to process the two matrices separately. We present two conductance mapping schemes which are crucial but have been overlooked in all prior studies on MCA-based detector circuits. The proposed detector circuit exhibits significantly superior performance to the conventional MCA-based detector circuit, while only incurring negligible additional power consumption. Our proposed detector circuit maintains its advantage in energy efficiency over traditional digital approach by tens to hundreds of times. © 2024 IEEE.

关键词： Integrated circuit design

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In-Memory Massive MIMO Linear Detector Circuit with Extremely High Energy Efficiency and Strong Memristive Conductance Deviation Robustness

In-Memory Massive MIMO Linear Detector Circuit with Extremel...

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2024 IEEE Global communications Conference, GLOBECOM 2024

ISBN: (纸本)9798350351255

The memristive crossbar array (MCA) has been successfully applied to accelerate matrix computations of signal detection in massive multiple-input multiple-output (MIMO) systems. However, the unique property of massive MIMO channel matrix makes the detection performance of existing MCA-based detectors sensitive to conductance deviations of memristive devices, and the conductance deviations are difficult to be avoided. In this paper, we propose an MCA-based detector circuit, which is robust to conductance deviations, to compute massive MIMO zero forcing and minimum mean-square error algorithms. The proposed detector circuit comprises an MCA-based matrix computing module, utilized for processing the small-scale fading coefficient matrix, and amplifier circuits based on operational amplifiers (OAs), utilized for processing the large-scale fading coefficient matrix. We investigate the impacts of the open-loop gain of OAs, conductance mapping scheme, and conductance deviation level on detection performance and demonstrate the performance superiority of the proposed detector circuit over the conventional MCA-based detector circuit. The energy efficiency of the proposed detector circuit surpasses that of a traditional digital processor by several tens to several hundreds of times. © 2024 IEEE.

关键词： Operational amplifiers

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Face Recognition using Adaboost Classifier For The Visually Impaired 8

Face Recognition using Adaboost Classifier For The Visually ...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Avinash, N.J. Krishnaraj Rao, N.S. Rama Moorthy, H. Mangalore Institute of Technology and Engineering Department of Electronics and Communication Engineering Mangalore Karnataka Moodabidre India Nmam Institute of Technology Department of Information Science and Engineering Karnataka Nitte India Nitte Institute of Professional Education Department of Computer Applications Karnataka Mangaluru India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376425

The paper suggests an approach to facial recognition of physical challenges. The most difficult challenge for blind persons is identifying impediments outside and behind them. They mostly rely on other people to help them make up for their blindness. To overcome this, they need to be supported by someone else people. In their daily lives, they face many problems when it comes to moving from one place to another. There is no guidance to help them when they need it. This study suggests a facial recognition system for those with vision impairments utilizing the Raspberry Pi to help prevent these situations. There is a camera designed for face recognition and a microphone designed for voice recognition in this case, so these components can be combined and people with visual disabilities can use it easily and behaves reasonably. In this work, a method is presented for detecting, classifying, and locating objects within complex environments. The paper applies the techniques of image processing and Adaboot classifiers to achieve an accuracy of more than 90 © 2024 IEEE.

关键词： Face recognition

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PAPR Reduction of NOMA Using Vandermonde Matrix-Particle Transmission Sequence

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computer Systems Science & engineering 2022年第10期43卷 193-201页

作者： Arun Kumar Sandeep Gupta Himanshu Sharma Mehedi Masud Department of Electronics and Communication Engineering JECRC UniversityJaipur303905India Department of Electrical Engineering JECRC UniversityJaipur303905India Department of Computer Science and Engineering JECRC UniversityJaipur303905India Department of Computer Science College of Computers and Information TechnologyTaif University11099Saudi Arabia

Non-Orthogonal Multiple Access(NOMA)is an ideal choice for 5G waveforms due to their characteristics such as high data rate,massive device connectivity,high spectral access,and effective frequency selective ***,it permits gigantic *** spectrum overlaps with NOMA,which consents several operators to segment the spectrum at the same *** features make NOMA more suitable for use beyond *** to Average Power(PAPR)is a major problem in Multi-Carrier Techniques(MCT)like NOMA and it also degrades the performance of the *** Partial Transmission Sequence(PTS)is a superior algorithm for moderating the ***,it also increases the complexity of the *** PAPR is reduced in NOMA by multiplying the NOMA signal with optimised phase vectors(P).The phase vectors are generated by using the Vandermonde Matrix(VM).In this work,we proposed a VM-PTS algorithm for the NOMA system,and the number of computations to search the optimal phase vectors for NOMA high PAPR signal is less as compared with existing *** outcomes demonstrate that the performance of the recommended PTS in terms of PAPR,Bit Error Rate(BER),and complexity is better than the conventional PTS(C-PTS).

关键词： VM-PTS NOMA PAPR 5G

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Image Processing Techniques for Detecting Steel Defects with MATLAB and Machine Learning 1

Image Processing Techniques for Detecting Steel Defects with...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Dankan Gowda, V. Mary, P. Arockia Ramanan, S.V. Kumar, N.M.G. Kumar, Pullela Svvsr Abdullah, Ibrahim Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India V.S.B. Engineering College Department of Information Technology Tamilnadu Karur India Ppg Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Coimbatore India Mohan Babu University Sree Vidyanikethan Engineering College Department of Electrical and Electronics Engineering Andhra Pradesh Tirupathi India Aditya College of Engineering and Technology Department of Computer Science & Engineering Andhra Pradesh Surampalem India Al-Turath University College Department of Computer Science Baghdad Iraq

ISBN: (纸本)9798350367720

This paper focuses on the detection of surface defects in the steel manufacturing industry since these defects inhibit the quality of the manufactured products and the efficiency of the manufacturing processes. Conventional inspection techniques require a extensive amount of work to be done at the inspection sites and perhaps associated with several kinds of errors. The current research work describes an automated system for determining the defects in the steel using image processing and integrating it with computational intelligence in MATLAB. The proposed method consists of pre-processing where the images are enhanced, feature extraction where characteristics of the defects are obtained and classification utilizing the machine learning models. The efficiency of the proposed approach is proved using experiments on the dataset of steel images and a substantial increase in the precision of the defects' recognition compared to other techniques. Thus, the' findings of the apply demonstrate that the use of image processing and machine learning can offer a strong system for real time defect identification in steel production. © 2024 IEEE.

关键词： Image enhancement

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Malicious data detection in IoT using deep learning approach

Malicious data detection in IoT using deep learning approach

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Kolli, Srinivas Srinivasan, Aravindan Manikandan, R. Prasad, Shalini Kumar, Ashok Ramesh, S. Department of Information Technology Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology Telangana Hyderabad India Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Electronics and Communication Engineering Panimalar Engineering College Tamil Nadu Chennai India Department of Electronics and Communication City Engineering College Karnataka Bangalore India Department of Computer Science Banasthali Vidyapith Rajasthan India Department of Computational Intelligence Saveetha School of Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781032669663

This article compares the efficacy of various DL intrusion detection techniques and identifies the optimal DL methodology for ID in the IoT. In this study, the DL techniques employed were artificial neural networks (ANN), long short-term memory (LSTM), and gated recurrent units (GRUs). The suggested model is assessed using a common dataset for IoT intrusion detection. Next, the experimental findings are scrutinized and contrasted with existing IoT techniques for intrusion detection. When compared to the previously used approaches, the suggested technique appeared to have the highest accuracy (99.9%). © 2024 The Author(s).

关键词： Intrusion detection

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Exploring the Role of Automated Reasoning in Big Data Analysis 15

Exploring the Role of Automated Reasoning in Big Data Analys...

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15th International Conference on Computing communication and Networking Technologies, ICCCNT 2024

作者： Neetha, S.S. Sahoo, Girija Shankar Karthigaikuar, P. Jayasudha, S. Shinde, Gitanjali R. Lone, Zeeshan Ahmad Department of Computer Science and Information Technology Karnataka Bangalore India Maharishi University of Information Technology Maharishi School of Engineering and Technology Uttar Pradesh India Karpagam College of Engineering Department of Electronics and Communication Engineering Coimbatore641032 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Technology Department of Computer Engineering Pune India Vivekananda Global University Department of Computer Science & Engineering Jaipur India

ISBN: (纸本)9798350370249

In large-scale data analysis, computer reasoning is an important role. This era can pull significant files from substantial statistics and in a well-timed manner. In this way, through automatic feel, facts scientists and researchers can pick styles out of your statistics or even locate anomalies that could not have been detected in records units. Additionally, it speeds up constructing decision-making models and stimulates artificial intelligence. Artificial intelligence permits herculean records mining and semantic analysis methods, including device learning, ontologiesystem gaining knowledge and natural language processing. Likewise, it offers a platform for creating and monitoring valuable findings, trends or patterns. This also helped to enhance the accuracy of analytic results and provides a more efficient way for statistics scientists & researcher s to analyze large amounts of data. This generation poses a chance to enhance state-of-the-art strategies by making the correct picks. © 2024 IEEE.

关键词： Aberrations Discriminations Electronic Mechanical Mindset

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Deep Learning Ensemble for Diabetes Prediction: Integrating LSTM, DCNN, and SMOTE for Enhanced Risk Assessment 7th

Deep Learning Ensemble for Diabetes Prediction: Integrating ...

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7th IFIP TC 12 International Conference on Computational Intelligence in Data Science, ICCIDS 2024

作者： Selvaraj, Ayyakkannu Satheesh Kumar, V. Bhudhwant, Dilip Tamane, Sharvari Khandelwal, Chhaya S. University Department of Information and Communication Technology MGM University Maharashtra Aurangabad India Department of Mechatronics Engineering Park College of Engineering and Technology Tamil Nadu Coimbatore India Department of Electronics and Computer Engineering Jawaharlal Nehru Engineering College MGM University Maharashtra Aurangabad India

ISBN: (纸本)9783031699856

Diabetes is a prevalent and chronic medical condition that poses a significant public health challenge worldwide. Accurate early prediction of diabetes risk can facilitate timely interventions and improve patient outcomes. In this study, we propose a comprehensive approach to diabetes risk assessment through the integration of deep learning techniques and data augmentation strategies. Our research leverages the power of deep learning ensembles by combining Long Short-Term Memory (LSTM) networks, Deep Convolutional Neural Networks (DCNN), and Synthetic Minority Over-sampling Technique (SMOTE) for addressing class imbalance. We begin by collecting and preprocessing a dataset comprising patient information, medical histories, and, where applicable, image data. The LSTM model is tailored to capture temporal dependencies in the data, while the DCNN extracts meaningful features. By integrating SMOTE, we tackle class imbalance challenges commonly encountered in medical diagnosis tasks. Our ensemble model seamlessly integrates the predictions from these diverse models, allowing for a holistic assessment of diabetes risk. Extensive evaluation and hyperparameter tuning are performed to optimize performance and ensure the model’s robustness. Cross-validation techniques are employed to gauge the model’s generalization capabilities. The proposed approach aims to not only provide accurate predictions but also to offer insights into the contributions of individual models within the ensemble. Ultimately, our research seeks to enhance diabetes risk assessment, offering a valuable tool for healthcare practitioners in making informed decisions and potentially improving the early detection and management of diabetes. This present study represents a significant step towards more reliable and comprehensive diabetes risk assessment, leveraging the potential of deep learning and ensemble techniques to advance predictive accuracy and broaden our understanding of this critical healthcare cha

关键词： Risk assessment

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Advancements in Wireless Networks: Transition from 5G to Beyond 2

Advancements in Wireless Networks: Transition from 5G to Bey...

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2nd IEEE International Conference on Emerging Research in Computational Science, ICERCS 2024

作者： Thangavel, Gunasekaran Malarvizhi, C. Banu, M. Ameena Anthonisamy, Arun Hemalatha, M. Manikandan, Raja University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Chembarambakkam India NPR College of Engineering and Technology Department of Electronics and Communication Engineering Natham India Panimalar Engineering College Department of Computer Science and Business System Tamil Nadu Chennai600123 India Nandha Arts and Science College Department of Information Technology Tamil Nadu Erode638052 India K. Ramakrishnan College of Technology Department of ECE Tamil Nadu Trichy India

ISBN: (纸本)9798331534950

Earlier generations of mobile technology experienced exponential growth in mobile customers, disruptive ecosystems like Internet of Things applications, and massive channel capacity needed to connect trillions of devices. 5G networks are a vital solution. 5G aims to provide 10 Gbps to a single user, as well as quality of service, increased spectrum efficiency, ultra-reliable communication, extended device battery life, massive machine-type communication (mMTC), and a 1000-fold capacity gain. Due to the heavy traffic volume and high resource consumption of 5G applications like augmented reality (AR), virtual reality (VR), and wireless body area networks, there are major barriers to channel capacity and potential service disruptions for other users. New strategies are needed to increase spectral efficiency and channel capacity. This paper explores many major new wireless technologies that aim to increase spectral efficiency and channel capacity to meet growing user demands and improve network performance. This paper also examines these technologies' benefits and research challenges. Massive Multi-Input Multi-Output (mMIMO) technology's effectiveness and potential distinguish it for 5G and Beyond 5G (B5G) networks, offering a variety of benefits and characteristics. This article can also assist researchers who are conducting in-depth studies, as well as wireless network operators who aim to streamline the development of 5G and B5G networks. © 2024 IEEE.

关键词： 5G mobile communication systems

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