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Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

National Science Review 2024年第2期11卷 231-263页

作者： Kyung Rok Pyun Kangkyu Kwon Myung Jin Yoo Kyun Kyu Kim Dohyeon Gong Woon-Hong Yeo Seungyong Han Seung Hwan Ko Department of Mechanical Engineering Seoul National University IEN Center for Human-Centric Interfaces and Engineering Institute for Electronics and NanotechnologyGeorgia Institute of Technology School of Electrical and Computer Engineering Georgia Institute of Technology Department of Chemical Engineering Stanford University Department of Mechanical Engineering Ajou University George W.Woodruff School of Mechanical Engineering Georgia Institute of Technology Institute of Advanced Machinery and Design(SNU-IAMD) Seoul National University

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human–machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.

关键词： wearable sensor soft electronics artificial intelligence machine learning human–machine interfaces gesture recognition

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Business Response Prediction based on Consumer Behaviour Patterns 9

Business Response Prediction based on Consumer Behaviour Pat...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Pokharkar, Vishwa Edgaonkar, Omkar Shinde, Aryajeet Nirmal, Nilesh Pune Institute of Computer Technology Electronics & Telecommunications Engineering Department Pune India

ISBN: (纸本)9798350394474

This survey paper presents a comprehensive analysis of predictive models for business response in the hospitality industry, focusing on cafes and restaurants. Leveraging data from Zomato, a popular restaurant aggregator platform, and supplemented with location data, we explore effective strategies for preprocessing and clustering based on key parameters, including location, cuisine, nearby institutions, parking, traffic, bestsellers, cost, ratings, delivery options, and establishment type (cloud kitchen/dine out). We investigate a variety of machine learning and statistical approaches tailored to distinct clusters of cafes and restaurants. Our evaluation involves training and testing models to predict business responses, considering varying customer foot traffic and preferences. The findings emphasize the importance of precise clustering and customized predictive models for improved accuracy in response forecasting. The insights derived from this survey are anticipated to significantly impact decision-making processes for businesses in the hospitality sector. © 2024 IEEE.

关键词： Forecasting

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Pilot-based channel estimation in spatial modulated OFDM systems for mobile wireless applications

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International Journal of Wireless and Mobile Computing 2023年第2期24卷 181-194页

作者： Soman, Anetha Mary Nakkeeran, R. John, Shinu Mathew Department of Electronics Engineering School of Engineering and Technology Pondicherry Central University Pondicherry India Department of Computer Science Engineering St. Thomas College of Engineering and Technology Mattannur Kerala Kannur India

Spatial modulation Orthogonal Frequency Division Multiplexing (SM OFDM) is a newly developed transmission technique that has been proposed as an alternative for multiple input multiple output (MIMO)-OFDM transmission. Channel estimation is important for coherent data detection in practical scenarios. This paper investigates estimate power, Mean Square Error (MSE) and Bit Error Rate (BER) parameter metrics with channel estimation algorithm for Additive White Gaussian Noise (AWGN) and Rayleigh fading channel on three test environments proposed by International Telecommunication Union (ITU) specified standard model. Simulation output shows that for an AWGN channel, an improvement of approximately 1 dB and 0.3 dB power is obtained with Minimum Mean Square Error (MMSE) estimate and Least Square (LS)-linear/Spline using DFT. Additionally, for various interpolations employed, MSE decreases as Signal to Noise Ratio (SNR) rises, and performance improvement in BER when compared to traditional LS/MMSE estimators also occurs. For Rayleigh fading channel also there arises an enhanced performance in the parameter metrics with MMSE-DFT compared to conventional estimators. In summary, simulation output shows that incorporating DFT algorithm can provide better estimate power, BER and MSE by eliminating the effect of noise externally the extreme channel delay length. © 2023 Inderscience Enterprises Ltd.

关键词： Interpolation

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A Machine Learning and Deep Learning-Based Web Application for Crop and Fertilizer Recommendation and Crop Disease Prediction 6th

A Machine Learning and Deep Learning-Based Web Application f...

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6th International Conference on Soft Computing and Signal Processing, ICSCSP 2023

作者： Srinidhi, Amuri Jahnavi, Veeramachinani Dholvan, Mohan Department of Electronics and Computer Engineering Sreenidhi Institute of Science and Technology Hyderabad India

ISBN: (纸本)9789819984503

India is a country that is majorly dependent on agriculture and farming for livelihood. The country’s diverse soil lets farmers produce various crops throughout the year. Depending on the characteristics of soil such as the composition of nitrogen, phosphorus, potassium and the pH of the soil and environmental factors such as rain, humidity, and temperature, it is important to know which crops should be grown and what fertilizers to use, to maximize the yield. It is also important to detect any crop diseases at an early stage to prevent major loss. This paper presents an all-in-one platform web application that performs all three tasks, and it uses ML algorithms such as KNN, Decision Tree, Gaussian Naive Bayes, SVM, Logistic Regression, and Random Forest to recommend crops and fertilizers by analyzing soil and environmental factors. For crop recommendation, Random Forest is chosen as it provided the highest accuracy of 99.09%. Random Forest is deployed for fertilizer recommendation as well, as it provided the highest accuracy of 100%. The Deep Learning model ResNet50 is used to detect crop diseases, and the accuracy for training and validation was 95.52% and 87.36%, respectively. Flask framework is used to build the backend of the web application. The web application is enabled to send an SMS of the results to user’s mobile phone using the SMS API, Twilio. The goal of the project is to provide an ideal solution to farmers or the users of the web application, via a text message, which can be accessed even in remote areas and can be understood by the majority of people. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Crops

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Network Intrusion Detection System Using Ensemble Methods and Deep Neural Network 9

Network Intrusion Detection System Using Ensemble Methods an...

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9th International Conference on Science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Bandari, Sheethal Rangdal, Aakanksha Rukmanna Manisha, Budhi Shailaja, M. Sreenidhi Institute of Science And Technology Department of Electronics And Computer Engineering Hyderabad India

ISBN: (纸本)9798350365092

Today, we find almost everybody using internet and with increase in demand of computer networking, Hackers are taking advantage of this situation and trying to intrude into the networks and disturb the networks thus by injecting malwares into it. It is very important to identify whether the network is free from malware or not in order to ensure secrecy of valuable data. Intrusion Detection Systems (IDS) are some of the most prominent technologies for administrating and looking after security issues in the network. With rapidly changing network traffic data, one classifier is not enough to fend off modern network intruders. In this work, ensemble classifiers are used to classify data based on binary and multiclass. We took this as a major challenge to increase accuracy rates of detection for individual attack types and all types of attacks. This allows us to identify attacks and specific attack categories. We've chosen to contrast other works by opting for Artificial Networks as well. Python being the most versatile programming language in recent times, we chose IDS-ML an open source code repository in order to develop IDS from public network traffic records that can protect modern networks with high efficiency and accuracy. © 2024 IEEE.

关键词： Malware

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Align before Attend: Aligning Visual and Textual Features for Multimodal Hateful Content Detection 18

Align before Attend: Aligning Visual and Textual Features fo...

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18th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2024 - Student Research Workshop, SRW 2024

作者： Hossain, Eftekhar Sharif, Omar Hoque, Mohammed Moshiul Preum, Sarah M. Department of Computer Science and Engineering Department of Computer Science Dartmouth College United States Department of Electronics and Telecommunication Engineering Chittagong University of Engineering & Technology Bangladesh

ISBN: (纸本)9798891760905

Multimodal hateful content detection is a challenging task that requires complex reasoning across visual and textual modalities. Therefore, creating a meaningful multimodal representation that effectively captures the interplay between visual and textual features through intermediate fusion is critical. Conventional fusion techniques are unable to attend to the modality-specific features effectively. Moreover, most studies exclusively concentrated on English and overlooked other low-resource languages. This paper proposes a context-aware attention framework for multimodal hateful content detection and assesses it for both English and non-English languages. The proposed approach incorporates an attention layer to meaningfully align the visual and textual features. This alignment enables selective focus on modality-specific features before fusing them. We evaluate the proposed approach on two benchmark hateful meme datasets, viz. MUTE (Bengali code-mixed) and MultiOFF (English). Evaluation results demonstrate our proposed approach’s effectiveness with F1-scores of 69.7% and 70.3% for the MUTE and MultiOFF datasets. The scores show approximately 2.5% and 3.2% performance improvement over the state-of-the-art systems on these datasets. Our implementation is available at https://***/eftekhar-hossain/Bengali-Hateful-Memes. © 2024 Association for Computational Linguistics.

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Influence of performance and emission of diesel engine with alumina nano material-based catalyst biodiesel using IoT

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International Journal of Power and Energy Conversion 2024年第4期15卷 338-351页

作者： Venkatesh, B. Khan, Mudassir Babu, J. Chinna Nagaraju, C.H. Mohan, R. Madhan Department of Mechanical Engineering Annamacharya Institute of Technology and Sciences Andhra Pradesh Rajampet India Department of Computer Science College of Computer Science Applied College Tanumah King Khalid University P.O. Box 960-61421 Abha Saudi Arabia Department of Electronics and Communication Engineering Annamacharya Institute of Technology and Sciences Andhra Pradesh Rajampet India Department of Electrical and Electronics Engineering Annamacharya Institute of Technology and Sciences India

In this study, the performance and emissions of Al2O3 alkaline cottonseed biodiesel-powered compression ignition engines were predicted. An experiment was conducted using different biodiesel blends made from cottonseed oil under various loading conditions. Biodiesel was produced using ethanol and Cao-mg/Al2O3 as catalysts through the transesterification process and blended with diesel to produce CBDN10, CBDN20, CBDN30, and CBDN40. The CBDN20 blends showed an evident reduction in fuel consumption by 8% and a 12% improvement in thermal efficiency at high static thrust. The use of these blends resulted in minimal CO, CO2, and NOx emissions from engines. These emissions were monitored by an IoT system and analysed with gas analysers, which revealed CBDN20’s ability to reduce nitrogen oxide and hydrocarbon emissions compared to diesel. Additionally, CBDN20 demonstrated improved thermal efficiency and reduced brake-specific fuel consumption compared to the other blends. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Nitrogen oxides

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Enhancing real time object detection for autonomous driving using YOLO-NAS algorithm with CLEO optimizer

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International Journal of Information technology (Singapore) 2025年第3期17卷 1321-1328页

作者： Nandal, Priyanka Pahal, Sudesh Malik, Sunesh Sehrawat, Neetu Mamta Department of Computer Science and Engineering Maharaja Surajmal Institute of Technology New Delhi India Department of Electronics and Communication Engineering Maharaja Surajmal Institute of Technology New Delhi India Department of Information Technology Maharaja Surajmal Institute of Technology New Delhi India Department of Computer Science and Engineering Panipat Institute of Engineering & Technology New Delhi India

For autonomous driving to operate in a safe and effective manner, efficient and precise object detection is essential. The efficacy of the network model is heavily challenged because of the high-speed movement of vehicles and the dynamic nature of the surrounding environment that need the detection of objects of various scales. With notable improvements over earlier iterations, the You Only Look Once-Neural Architecture Search (YOLO-NAS) algorithm is a strong contender for improving real-time object recognition in autonomous vehicles. YOLO-NAS integrated with chimpanzee leader election optimization (CLEO) has the potential to greatly enhance the safety and dependability of autonomous driving systems by utilizing its sophisticated capabilities and skill fully combining it with the vehicle’s perception system. The BDD100K dataset serves as the training and evaluation set for our suggested technique. The YOLO-NAS model with CLEO optimizer achieves mean average precision (mAP) of 83%, which is significantly higher than the mAP of 65.7% attained by the YOLO-NAS model without the optimizer. This improvement highlights the effectiveness of the CLEO optimizer in enhancing detection precision. The proposed approach was compared with existing algorithms and demonstrated remarkable performance in terms of recall, precision, and mAP. It is seen that our proposed model is better by striking equilibrium between detection speed and accuracy. Based on our findings, object detection performance for autonomous driving applications is much enhanced when utilizing our suggested approach. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Autonomous vehicles Deep learning Object detection YOLO-NAS

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Review of AI & XAI-based breast cancer diagnosis methods using various imaging modalities

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Multimedia Tools and Applications 2025年第5期84卷 2209-2260页

作者： Karthiga, R. Narasimhan, K. Thanikaiselvan, V. Hemalatha, M. Amirtharajan, Rengarajan Department of ECE SRM Institute of Science and Technology Ramapuram Chennai600089 India School of Electrical & Electronics Engineering SASTRA Deemed University Thanjavur613 401 India School of Electronics Engineering Vellore Institute of Technology Vellore India Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia

Breast cancer is a significant health concern;early detection and treatment are critical to improving patient outcomes. Artificial Intelligence has the potential to assist healthcare professionals in the diagnosis and treatment of breast cancer, and clinical decision support systems using AI algorithms can help to improve the accuracy and speed of breast cancer detection and diagnosis. By analysing large amounts of data from various imaging modalities, AI algorithms can help identify breast cancer early, increasing the chances of successful treatment. This survey aims to briefly overview the most prevalent breast cancer types, their staging, and the methods and modalities for their diagnosis to address these problems. Our investigation showed that, when compared to ML, DL techniques achieved the highest accuracy, with an approximate 2% improvement in accuracy. This review article mainly focuses on four commonly used imaging modalities: Mammogram, Thermogram, Ultrasound imaging and histopathology images. This survey critically analyzes breast cancer detection approaches and modalities, as well as a cost-to-accuracy comparison. Explainable AI (XAI) techniques and their application for breast cancer diagnosis are critically examined, as well as their advantages and limitations. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Lung cancer

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Improved Skin Lesion Detection with Double Layer Concatenated DenseNet Using Transfer Learning and Attention Modules 2

Improved Skin Lesion Detection with Double Layer Concatenate...

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2nd International Conference on Power, Electrical, electronics and Industrial Applications, PEEIACON 2024

作者： Hasib, Md Mesbahuddin Faruk, Md. Farukuzzaman Mahedy Hasan, S.M. Al Mamun, Md. Srizon, Azmain Yakin Efat, Anwar Hossain Hossain, Md. Rakib Khansur, Md. Nasif Department of Computer Science & Engineering Department of Electronics & Telecommunication Engineering Rajshahi University of Engineering & Technology Rajshahi6204 Bangladesh

ISBN: (纸本)9798331517984

Skin that becomes unnaturally elevated, discoloured, or behaves in another unusual way may indicate a more serious health issue, such as skin cancer. Lesions on the skin, on the other hand, deviate from the skin's typical appearance and may point to more serious health issues. Accurate localization of diseased areas is challenging due to the complexity and expense of diagnostic tests;however, early lesion detection is essential for cancer prevention and diagnosis. The innovative automated method described in this article relies on Transfer Learning (TL), and its fundamental component is the use of DenseNet models. We have fine-tuned these models to repeatedly concatenate three attention modules, effectively extracting salient data for lesion identification. Before using concatenation to generate final features, it is necessary to fine-tune and incorporate attention mechanisms into all DenseNet models. For each version of DenseNet, the outcome of carrying out this method is Double Layer Concatenated DenseNet (DLCD). Using the ISIC 2019 dataset as an example, the proposed model reaches a remarkable 91.61% accuracy. Using GradCAM to pinpoint the precise region for categorization is one way to enhance the explainability of the architecture. We improve the study's accuracy while advancing the use of TL and other augmentation approaches. The results of this research provide techniques to improve the identification and early detection of skin lesions, perhaps resulting in a reduction in the incidence of skin cancer. © 2024 IEEE.

关键词： Diseases

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