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Comparison of Neural Network and Random Forest Classifier Performance on Dragon Fruit Disease 23

Comparison of Neural Network and Random Forest Classifier Pe...

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23rd International Electronics Symposium, IES 2021

作者： Lado, Anita Jaquiline Sooai, Adri Gabriel Mamulak, Natalia Magdalena Rafu Nani, Paskalis Andrianus Bria, Yulianti Paula Batarius, Patrisius Aliandu, Paulina Ngaga, Emerensiana Sinlae, Alfry Aristo Jansen Mau, Sisilia Daeng Bakka Tedy, Frengky Meolbatak, Emiliana Metan Siki, Yovinia Carmeneja Hoar Gumelar, Agustinus Bimo Fanani, Nurul Zainal Yuhana, Umi Laili Universitas Katolik Widya Mandira Dept. of Computer Science Kupang85225 Indonesia Universitas Narotama Faculty of Computer Science Surabaya60117 Indonesia Dept. of Engineering Politeknik Negeri Jember Indonesia Institut Teknologi Sepuluh Nopember Dept. of Informatics Engineering Surabaya60117 Indonesia

ISBN: (纸本)9781665443463

Measuring the performance of several classifiers in the modeling process, based on datasets with certain criteria, becomes important. It used to determine which classifier is more reliable for a particular task. Neural network will be compared its performance against Random Forest using dragon fruit datasets. Consisting of 41 images of healthy and sick fruit and leaf, it is divided into four classes, both classifiers were used in two comparing experiments. The validation used is 10-fold cross-validation. The results obtained are not much different from the prediction accuracy in the range of 70% to 82.9% for both classifiers. © 2021 IEEE.

关键词： Fruits

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Reinforcement Learning for Autonomous Vehicles

Reinforcement Learning for Autonomous Vehicles

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Advances in Computing, Communication and Networking (ICAC2N), International Conference on

作者： Vinod Sharma Parwati Kumawat Pankaj Kumar S. Yaminipriya Subbarao Chamarthi Jitendra Kumar Dept of CSE SLP College Gwalior India Dept. of Commerce & Management Madhav University Pindwara India Dept. of Biomedical Engineering Dhanalakshmi Srinivasan College of Engineering Coimbatore India Dept. of Mechanical Engineering Lingayas Vidyapeeth Faridabad India Dept. of Computer Science J.S. University Shikohabad India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Reinforcement Learning (RL) has emerged as a transformative technology for autonomous vehicles, enabling sophisticated decision-making systems that enhance driving safety, efficiency, & adaptability. This paper explores the application of RL algorithms in the context of autonomous vehicle control, focusing on the development of intelligent agents capable of learning optimal driving policies through interaction with complex environments. The study reviews various RL techniques, including Deep Q-Learning, Policy Gradient methods, & Actor-Critic frameworks, evaluating their effectiveness in addressing key challenges such as real-time decision making, dynamic environment adaptation, & multi-agent interactions. Emphasis is placed on the design of reward functions, exploration strategies, & simulation environments, which are crucial for training robust & reliable autonomous driving systems. Case studies demonstrate the application of RL in scenarios such as lane-keeping, adaptive cruise control, & collision avoidance. The paper also discusses advancements in simulation platforms & hardware acceleration that facilitate scalable RL experiments. By integrating theoretical insights with practical implementations, this work provides a comprehensive overview of current developments in RL for autonomous vehicles & identifies future research directions aimed at overcoming limitations & achieving safer, more efficient.

关键词： Training Reviews Decision making Velocity control Transportation Reliability theory Safety Security Vehicle dynamics Autonomous vehicles

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PatchBMI-Net: Lightweight Facial Patch-based Ensemble for BMI Prediction

PatchBMI-Net: Lightweight Facial Patch-based Ensemble for BM...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Parshuram N. Aarotale Twyla Hill Ajita Rattani Dept. of Biomedical Engineering Wichita State University Kansas USA Dept. of Sociology Wichita State University Kansas USA Dept. of Computer Science and Engineering Uni. of North Texas at Denton Texas USA

Due to an alarming trend related to obesity affecting 93.3 million adults in the United States alone, body mass index (BMI) and body weight have drawn significant interest in various health monitoring applications. Consequently, several studies have proposed self-diagnostic facial image-based BMI prediction methods for healthy weight monitoring. These methods have mostly used convolutional neural network (CNN) based regression baselines, such as VGG19, ResNet50, and EfficientNetB0, for BMI prediction from facial images. However, the high computational requirement of these heavy-weight CNN models limits their deployment to resource-constrained mobile devices, thus deterring weight monitoring using smartphones. This paper aims to develop a lightweight facial patch-based ensemble (PatchBMI-Net) for BMI prediction to facilitate the deployment and weight monitoring using smartphones. Extensive experiments on BMI-annotated facial image datasets suggest that our proposed PatchBMI-Net model can obtain Mean Absolute Error (MAE) in the range [3.58, 6.51] with a size of about 3.3 million parameters. On cross-comparison with heavyweight models, such as ResNet-50 and Xception, trained for BMI prediction from facial images, our proposed PatchBMI-Net obtains equivalent MAE along with the model size reduction of about 5.4× and the average inference time reduction of about 3× when deployed on Apple-14 smartphone. Thus, demonstrating performance efficiency as well as low latency for on-device deployment and weight monitoring using smartphone applications.

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SegQNAS: Quantum-inspired Neural Architecture Search applied to Medical Image Semantic Segmentation

SegQNAS: Quantum-inspired Neural Architecture Search applied...

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International Joint Conference on Neural Networks (IJCNN)

作者： Guilherme Carlos Karla Figueiredo Abir Hussain Marley Vellasco Dept. of Electrical Engineering PUC-Rio Rio de Janeiro Brazil Dept. of Informatics and Computer Science UERJ Rio de Janeiro Brazil Dept. of Electrical Engineering University of Sharjah Sharjah United Arab Emirates

Semantic segmentation can be applied to a wide range of applications. One of the most interesting is medical image analysis. Applying semantic segmentation techniques in that field has great potential to assist physicians in diagnosing and analysing medical scans for the patient's benefit. Traditionally, several techniques have been applied in order to perform semantic segmentation. However, with the development of Deep Learning methods, there was a paradigm shift. Deep learning techniques are able to achieve great results, comparable to human-level performance. In order to achieve state-of-the-art results, researchers have to lean on the task of designing novel deep neural network architectures. That process is very time-consuming and heavily relies on experience and expert knowledge. Neural architecture search is the process of automatising the search for new deep neural network architectures. Quantum-Inspired Neural Architecture Search is a neural architecture search algorithm that leverages the benefits of quantum-inspired computing to search for neural network architectures efficiently. In this work, we adapt this to search for semantic segmentation neural networks and apply it to medical image analysis. The Spleen and Prostate datasets from the Medical Segmentation Decathlon challenge were used. Results show that our work was able to find better-performing semantic segmentation architectures for both datasets: $0.9583\pm 0.006$ in comparison to ResU-Net $0.9525\pm 0.008$ for the spleen dataset, and $0.6887\pm 0.067$ in comparison to $0.6529\pm 0.070$ for the prostate dataset.

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Non-Invasive Device for Hearing Rehabilitation

Non-Invasive Device for Hearing Rehabilitation

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E-Health and Bioengineering Conference (EHB)

作者： Daniela Goldan Mihai Aron Teofil Ilie Ursache Gladiola Petroiu Cristian Rotariu Dept. of Biomedical Sciences Faculty of Medical Bioengineering Grigore T. Popa University of Medicine and Pharmacy of Iasi Iași Romania Dept. of Automatic Control and Applied Informatics Faculty of Automatic Control and Computer Engineering Gheorghe Asachi Technical University Iași Romania

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

Hearing loss impacts over 5% of the world's population, a percent rising due to aging populations, noise exposure, and chronic health conditions. To address this, we developed a non-invasive hearing device designed for individuals with hearing challenges that limit the use of hearing aids. The proposed device integrates a set of Bluetooth headphones with a smartphone, that process the ambient sound in real time through a software application and delivers to the hear impaired patient via bone conduction, bypassing traditional auditory pathways. Laboratory tests confirmed that the proposed device effectively replicates auditory perception. The developed device offers an accessible and user-friendly alternative to patients who cannot use traditional hearing aids.

关键词： Irrigation Noise Auditory system Ear Receivers Hearing aids Propagation losses Real-time systems Software Testing

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Intrusion detection using deep learning

Intrusion detection using deep learning

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2021 IEEE International Conference on Electrical, computer, Communications and Mechatronics Engineering, ICECCME 2021

作者： Anwer, Misbah Ahmed, Ghufran Akhunzada, Adnan Siddiqui, Shahbaz National University of Computer Emerging Sciences Dept. of Computer Science Karachi Pakistan University Malaysia Sabah Faculty of Comp Informatics Kota Kinabalu88400 Malaysia

ISBN: (纸本)9781665412629

Advancement in network attacks requires strong and growing security mechanisms. Internet of Things (IoT) is the evolving technology connected billion of devices right now and it builds on a set of network devices therefore it is under serious threat concern. Identifying attacks is crucial and critical task. The authors propose a hybrid DL driven approach to detect the attacks, one is Cuda Deep Neural Network Long Short-Term Memory (CuDNNLSTM) and another is Long Short-Term Memory (LSTM) on kitsune dataset. There is alarming situation for protection of all the smart systems in terms of security. In this paper we implemented LSTM and cuDNN LSTM networks to identify attack. Results show that our technique cuDNNLSTM outperforms in comparison of deep learning technique LSTMs that shows 99.79% accuracy on 6GB dataset approx. (2S0lac) records. © 2021 IEEE.

关键词： Long short-term memory

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Cardiac Condition Detection Model Using Alexnet and Time-Frequency Representation of ECG Signals

Cardiac Condition Detection Model Using Alexnet and Time-Fre...

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E-Health and Bioengineering Conference (EHB)

作者： Mihai Aron Teofil Ilie Ursache Gladiola Petroiu Cristian Rotariu Dept. of Biomedical Sciences Grigore T. Popa University of Medicine and Pharmacy of Iasi Faculty of Medical Bioengineering Iaşi Romania Dept. of Automatic Control and Applied Informatics Gheorghe Asachi Technical University Faculty of Automatic Control and Computer Engineering Iaşi Romania

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

Cardiac conditions are one of the leading causes of death worldwide. To address this, we designed a model that can detect cardiac conditions from a patient's electrocardiogram (ECG). This will convert the 1D ECG signal into a 2D scalogram using wavelet transforms with Morlet wavelets. With the help of a network based on the AlexNet convolutional neural network, it distinguishes between a normal ECG signal and one indicative of arrhythmia or congestive heart failure. The resulting model performs well, achieving a classification accuracy of 92.20% and providing a robust approach for detecting cardiac conditions.

关键词： Wavelet transforms Training Time-frequency analysis Accuracy Biological system modeling Electrocardiography Predictive models Congestive heart failure Convolutional neural networks biomedical engineering

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Effect of Interest Variation and Prefix Length on Pending Interest Table (PIT) Performance in Named Data Networking (NDN) 9

Effect of Interest Variation and Prefix Length on Pending In...

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9th International Conference on Wireless and Telematics, ICWT 2023

作者： Khaderani, Helmi Ahmad Muhammadi, Ishlah Muhammad, Lutfi Razan Sucipto, Adi Kusumastuti, Luthfi Restu Nurhayati, Ade Ahdan, Syaiful Syambas, Nana Rachmana Bandung Institute of Technology Dept. of Telecommunication Engineering Bandung40132 Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung40132 Indonesia Universitas Teknokrat Indonesia Faculty of Engineering & Computer Science Lampung35132 Indonesia Telkom University Faculty of Engineering Jakarta Barat11710 Indonesia

ISBN: (纸本)9798350305029

Pending interest table (PIT) is one of the data structures on each named data network router. The speed of delivery of interest as well as the interest served at PIT are several important parameters for measuring PIT performance. This research was conducted to analyze the performance of PIT by measuring the round trip time (RTT) of sending data and sending interests from consumers to producers on the Palapa Ring Sumatra topology. Testing is carried out using miniNDN by varying the length of the prefix and the number of interests sent. From the simulation and results of the analysis that has been carried out, it is known that the correlation between the length of the prefix and RTT is directly proportional. The longer the prefix of an interest packet, the higher the RTT value. Apart from the simulation results, the number of interest packets sent is the same as those received at each node. © 2023 IEEE.

关键词： Topology

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Prediction of essential genes in G20 using machine learning model

Prediction of essential genes in G20 using machine learning ...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Nembot, Thierry Kongne Fotseu, Ernest Basile Fotseu Sani, Rajesh K. Etienne Gnimpieba, Z. Lushbough, Carol Bomgni, Alain Bertrand University of Dschang Dept. of Computer Science Dschang Cameroon Dept. of Chemical and Biological Engineering Rapid city United States University of South Dakota Dept. of Biomedical Engineering Vermillion United States

ISBN: (纸本)9781665401265

Despite the exponential growth in bioscience data, one of the key challenges for machine learning engineers remains the incompleteness of bioscience dataset (biodata). For a specific bioscience problem such as (e.g. biofilm formation, drug response, organism survival), it is very difficult to find a good consistent dataset capturing the numerous variables involved in each of these processes. Each systems biology data point is measured with different protocols in different settings, making their integration hard and not reliable. This paper focuses on using machine learning (ML) models and data mining (DM) workflow to perform gene essential prediction in G20. Actually, developing next-generation and nano-scale coatings to control biofilm formation on technologically relevant materials is a great challenge today. This can help to control microbial corrosion on material or engineer better relevant material. To tackle this relevant problem, a detailed understanding of the bacterial survival mechanisms is crucial. Computational methods for predicting essential genes can make it easier and faster to obtain reliable results. Method: The main hypothesis of our work is that a minimal information-driven specific Machine Learning model can outperform an interesting prediction score. To reach our goal, we set up first a completed data mining workflow to extract gene features from G20. We then derive 10192 features from gene sequence and protein sequence divided into 25 relevant subgroups. From each subgroup, we build a couple of interesting machine learning models. Result: We identified 69 relevant subgroups of features using our features selection algorithm. We tested the model performance on each of these subgroups and our predictive result achieved up to 98% accuracy score. These subgroups of features can be used to assist researchers to select good variables for their respective experiments. © 2021 IEEE.

关键词： Forecasting

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Borneo Smart Forest Information System for Management of Dipterocarp Plants in Kalimantan Rainforest

Borneo Smart Forest Information System for Management of Dip...

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Information Technology International Seminar (ITIS)

作者： Masna Wati Novianti Puspitasari Ummul Hairah Joan Angelina Widians Anindita Septiarini Ade Fiqri Tjikoa Dept. of Informatics Faculty of Engineering Universitas Mulawarman Samarinda Indonesia Dept. of Informatics Universitas Mulawarman Doctoral Program Dept. of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9798350398205

Kalimantan’s tropical rainforest is home to indigenous plants of the Dipterocarpaceae family (tribe), which is renowned for having the most endemic species. Dipterocarp is a family of pantropical plants, many of which are used in the wood industry. This study aims to provide information on the potential of Dipterocarp in Kalimantan forests. Education and conservation efforts for Dipterocarp trees, a prominent component of the forest ecosystem, are crucial in light of the significant harm done to Kalimantan’s rainforests. The authors develop the Borneo Smart Forest information system. This system digitally organizes data on the diversity of Dipterocarp in the Kalimantan rainforest based on Web and QR-Code technology. Borneo Smart Forest (BSF) system for monitoring plant inventory and plant markers can be read by smartphones using QR codes. It gives visitors a comprehensive overview of the plants. A QR code is a printed, two-dimensional barcode with limited space for data storage. This technology allows the plant to show earlier administrator-made additions to the inventory information system. The QR code will direct the user to the BSF website, which has data about the plants. The development of this system is one of the efforts to support the achievement of Dipterocarp conservation.

关键词： Seminars Memory Forestry QR codes Vegetation Wood industry Information technology

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