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Bioinspired Polarized Skylight Orientation Determination Artificial Neural Network

Journal of Bionic engineering 2023年第3期20卷 1141-1152页

作者： Huaju Liang Hongyang Bai Ke Hu Xinbo Lv School of Energy and Power Engineering Nanjing University of Science and TechnologyNanjing210094China Neuromorphic Computing Laboratory China Nanhu Academy of Electronics and Information TechnologyJiaxing314001China Guidance Radar General Department Xi’an Electronic Engineering Research InstituteXi’an710100China School of Automation Nanjing University of Science and TechnologyNanjing210094China

This paper proposes an artificial neural network to determine orientation using polarized skylight. This neural network has specific dilated convolution, which can extract light intensity information of different polarization directions. Then, the degree of polarization (DOP) and angle of polarization (AOP) are directly extracted in the network. In addition, the exponential function encoding of orientation is designed as the network output, which can better reflect the insect’s encoding of polarization information and improve the accuracy of orientation determination. Finally, training and testing were conducted on a public polarized skylight navigation dataset, and the experimental results proved the stability and effectiveness of the network.

关键词： Polarized light navigation Orientation determination Artificial neural network

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Analysis of Duplicating the Human Mind Suspicions in AI in Various Divisions

Analysis of Duplicating the Human Mind Suspicions in AI in V...

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

作者： Santhoshkumar, S.P. Karthika, R. Vijayakumar, N. Gajalakshmi, S. Jayanthi, K. Hariharasudhan, S. School of Computing Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India School of Computing Department of Information Technology Rathinam Technical Campus Coimbatore India Pollachi Institute of Engineering and Technology Pollachi Coimbatore India

ISBN: (纸本)9798331509828

Uncertainty in AI refers to the degree of confidence or probability associated with the accuracy or effectiveness of an AI system's output. It is a measure of how much the AI system is unsure about the correctness of its output or decision-making. Uncertainty can arise in AI systems due to a variety of factors, including the quality of input data, variations in data formats, variations in context or environment, and the complexity of the underlying algorithms. Different AI applications can have specific types of uncertainty associated with them, as we discussed in previous questions about uncertainty in AI image recognition, natural language processing, data extraction, expert systems, planning and optimization, and robotics. To mitigate uncertainty in AI systems, researchers and developers use various methods, such as incorporating more data and diverse inputs, improving the quality of input data, enhancing the algorithms, and using feedback mechanisms to learn from errors and improve the system over time. However, complete elimination of uncertainty is often challenging or impossible, and users should always be aware of the potential for errors or unexpected behaviour in AI systems. In this paper, analysis of artificial intelligence uncertainty in various fields. © 2025 IEEE.

关键词： Expert systems

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Waveform based speech coding using nonlinear predictive techniques: a systematic review

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International Journal of Speech technology 2023年第4期26卷 1031-1059页

作者： Sheferaw, Gebremichael Kibret Mwangi, Waweru Kimwele, Michael Mamuye, Adane School of Computing and Information Technology Jomo Kenyatta University of Agriculture and Technology Juja Nairobi Nairobi62000 Kenya School of Information Technology and Engineering Addis Ababa University Institute of Technology 5Kilo Addis-Ababa Addis-Ababa Ethiopia

Speech coding is a technique that compresses speech signals into a smaller digital form, making it easier to transmit or store, while still maintaining the quality and intelligibility of the speech. The review aimed to identify and analyses the most effective waveform-based nonlinear speech coding prediction techniques, including the use of neural networks and polynomial filters. The study analyzed 29 publications from 2000 to 2023 and found that neural network-based models are widely used for speech compression, with RNN topologies being favored due to their ability to introduce nonlinearity and nonstationary. While nonlinear adaptive speech prediction techniques have been explored for speech coding, further research is needed to optimize the adaptive algorithms used in these models. The review also identified a need for future research to address quality performance and computational cost, and suggested further exploration of RNN predictor models. The methodology used in this study involved a computer science approach that follows three main phases: planning, conducting, and reporting. Six different stages were followed, including determining research questions, defining research approach, study selection criteria, quality measurement criteria, data extraction strategy, and synthesizing extracted data. Overall, this study highlights the need for continued research in the development and improvement of neural network-based speech compression models. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Forecasting

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TSR: a Location Privacy Preservation Mechanism in Public Transportation Route Planning Service 25th

TSR: a Location Privacy Preservation Mechanism in Public T...

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25th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2024

作者： Wu, Wei Sang, Yingpeng Zhang, Haibo Hong, Shen School of Computer Science and Engineering Sun Yet-sen University Guangzhou China School of Computing University of Otago Dunedin New Zealand School of Engineering and Technology Central Queensland University Brisbane Australia

ISBN: (纸本)9789819642069

Location-based service (LBS) applications are increasingly popular for travelling. The public transit scenario is very common in urban areas, yet there is a lack of effective privacy protection mechanisms to safeguard users’ location privacy when using services from untrusted third-party LBS providers. This paper introduces Transportation Shift-based Routing (TSR), a privacy protection mechanism for public transportation travel scenarios. It allows users to query public transit routes through applications that comply with this mechanism without disclosing any significant details about the location, and fills the gap in privacy protection mechanisms in public transportation scenarios. The fundamental concept is to shift the query’s starting and ending points to nearby stations and repeatedly perform perturbations that satisfy geo-indistinguishability, submitting a collection of perturbed station pairs to the LBS. TSR computes the set of obtained routes to derive an optimal route plan. The best route calculated can maintain service availability and protect user location privacy, with only a minimal difference from the best route obtained by direct query. TSR also allows users to freely choose privacy parameters for different scenarios and strives to ensure service quality, offering high flexibility. We implement the TSR application and conduct experiments with real-world map services. The experimental outcomes indicate that TSR successfully strikes a balance between preserving location privacy and maintaining service usability. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Smart Road Stud: Wireless Sensor Networks for Vehicle Sensing 100

Smart Road Stud: Wireless Sensor Networks for Vehicle Sensin...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Sun, Yanli Feng, Yimeng Wang, Hua Quan, Wei Li, Hao Sun, Jiayu Cheng, Jixuan Harbin Institute of Technology School of Transportation Science and Engineering Harbin China Macquarie University School of Computing Sydney Australia Broadvision Engineering Consultants Kunming China

ISBN: (纸本)9798331517786

This study introduces the Smart Road Stud (SRS), an innovative Anisotropic Magneto-Resistive (AMR) detector strategically placed on lane markings for advanced traffic monitoring. Unlike traditional detectors, the SRS, when deployed in pairs or more, facilitates simultaneous vehicle detection. Analysis reveals that single AMR detectors often face challenges, such as interference signals and misidentification of double-vehicle signals. To address these issues, a method leveraging multiple AMR detectors is proposed. Experimental results demonstrate over 95% accuracy in identifying interference signals and distinguishing double-vehicle signals. The approach achieves a remarkable 98.7% accuracy in lane-level traffic volume estimation. The SRS outperforms conventional detectors, showcasing its potential to enhance accuracy and versatility in traffic monitoring. © 2024 IEEE.

关键词： Studs (fasteners)

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Classification and Segmentation of Brain MR Images Using Deep Neural Networks and Fuzzy C-Mean Clustering 1

Classification and Segmentation of Brain MR Images Using Dee...

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1st International Conference for Women in computing, InCoWoCo 2024

作者： Asthana, Pallavi Kamboj, Deepika School of Computing MIT Art Design and Technology University CSE Department Pune India School of Engineering The NorthCap University Multidisciplinary Engineering Department Gurugram India

ISBN: (纸本)9798331518943

Brain tumor arises mainly due to the tissue's abnormal growth inside the brain. They are dangerous as compared to other kinds of tumors because of their aggressively fast-spreading nature leading to a lower survival rate in the patients. Therefore, detecting and segmenting brain tumors from brain MR images helps radiologists in the early treatment planning of patients. The proposed work uses deep neural networks to classify and a fuzzy C-mean clustering algorithm to segment. First, classification is done to detect whether the image is normal or has a tumor. Next, segmentation is done for the MR images with tumors. The evaluated results are also compared with the recently published work. © 2024 IEEE.

关键词： Deep neural networks

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Automated Detection of Hate Speech in Hindi using Supervised Learning Models

Automated Detection of Hate Speech in Hindi using Supervised...

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2024 International Conference on Distributed Systems, Computer Networks and Cybersecurity, ICDSCNC 2024

作者： Rajak, Nikhil Phulre, Ajay Kumar Chandra Patel, Subhash Patel, Narottam Das Khekare, Ganesh Vit Bhopal University School of Computing Science Engineering and Artificial Intelligence Sehore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350375442

This study addresses the pressing issue of hate speech on online platforms. Traditional text-based analysis is no longer adequate for identifying hate speech due to the growth of internet platform data. The project aims to create scalable automated methods for web platforms to detect hate speech in real-time. To find the best algorithms for recognizing hate speech, the study compares many machine learning models using a large dataset in Hindi from Twitter. Where the study matters is in the creation of advanced content filtering systems that offer a more secure and friendly online environment. Support Vector Machine (SVM) has the best performance, with outstanding precision (0.9468) and F1-score (0.944), showcasing the top-performing ones and talking about the difficulties and ways ahead in this field. The study recommends improving Hindi hate speech detection systems. With problems such as imbalanced data sets and the dynamic nature of online language, language research using hybrid models, constant model adjustments, and language trend monitoring are all necessary. © 2024 IEEE.

关键词： Adversarial machine learning

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A crop disease severity index derived from transfer learning and feature fusion using enhanced OPTICS algorithm

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International Journal of Hybrid Intelligent Systems 2024年第3期20卷 207-221页

作者： Subbiah, Priyanga Tyagi, Amit Kumar Krishnaraj, N. Department of Networking and Communications School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chengalpattu India Department of Fashion Technology National Institute of Fashion Technology New Delhi India

The adoption of automated methods for the identification and assessment of tomato-related disorders is highly sought-after in the agriculture sector. Using this technology is crucial for reducing wasteful spending, increasing the efficiency of treatments, and ultimately growing more resilient crops by reducing losses in agricultural output and maximising the effectiveness of these processes. An automated method has been suggested for accurately identifying and classifying diseases using a single photograph. The described method for disease detection in tomato plants makes use of a computer vision-based technique. Image processing, ML, and deep learning are just a few of the methods that this strategy uses. The goal of this approach is to prevent tomato crops from being damaged by various illnesses by reducing the need of conventional procedures. Bacterial spot, early blight, late blight, leaf mould, spider mites, target spot, spotted spider mite, mosaic virus, and yellow leaf curl are all examples of these illnesses. The following ten diseases frequently strike tomato crops in India. By utilising picture segmentation in combination with the Enhanced OPTICS algorithm (EOPTICSA), the affected area of the tomato plant may be precisely detected and defined after image pre-processing procedures have been used. It may be necessary to look for certain visual signs in order to diagnose the previously mentioned illnesses. The primary goal of this study was to evaluate the efficacy of the EOPTICSA method for detecting diseases in plant leaves. To eliminate the geometric features associated with colour, texture, and leaf arrangement in the provided plant pictures, image segmentation and edge detection methods are employed. Using these methods allows us to achieve our goal. Various efficacy measures are used to assess and provide a technique recommendation. This research shows that when performance metrics are used to implement these strategies, the suggested strategy outperfor

关键词： deep learning disease detection enhanced OPTICS algorithm Image processing image segmentation infection severity tomato plant diseases transfer learning

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A Shallow Convolutional Neural Network Model for Breast Cancer Histopathology Image Classification 11th

A Shallow Convolutional Neural Network Model for Breast Canc...

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11th International Conference on Recent Trends in computing, ICRTC 2022

作者： Saxena, Shweta Shukla, Praveen Kumar Ukalkar, Yash School of Computing Science Engineering VIT Bhopal University Bhopal India School of Computing and Information Technology Manipal University Jaipur Jaipur India

ISBN: (纸本)9789811988240

Identification of malignancy using histopathology image processing is a crucial method for cancer diagnosis. A model to classify images based on deep convolutional neural networks (CNNs) attains a promising performance in the computer vision for a large dataset but creates a computational complexity for a comparatively small histopathology dataset. A shallow CNN model is proposed in this paper, which classifies the histopathology images of breast tissues. The images are classified as either malignant or benign by the model. It is tested on two publicly available, benchmark breast cancer histopathology datasets. According to the result, the proposed model achieves (95.19 ± 8.50) accuracy and requires a less no. of parameters (8, 052,062) for training as compared to the existing deep CNN models implemented on the same protocol. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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Magnetic Skyrmions Based One-Bit Comparator 10

Magnetic Skyrmions Based One-Bit Comparator

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10th IEEE International Symposium on Smart Electronic Systems, iSES 2024

作者： Shringi, Shivangi Bodapati, Srinivasu Srinivasan, Srikant School of Computing and Electrical Engineering Indian Institute of Technology Mandi Mandi India Plaksha University Chandigarh India

ISBN: (纸本)9798331533229

This paper proposes a skyrmion based 1-bit comparator implemented using a 1 × 4 demultiplexer (DMUX) and an OR-gate. A stack is composed of heavy metal (HM) and ultrathin ferromagnetic (FM) i.e., platinum (Pt) and cobalt (Co), respec-tively. The stack can exhibit a large interfacial Dzyaloshinskii-Moriya interaction (DMI), and the FM-HM interface can host stable, Néel-skyrmions at room temperature. The irradiation reduces the local anisotropy and DMI. The nonirradiated regions of the FM act as a potential barrier for skyrmions due to higher anisotropy. The proposed comparator is validated through micromagnetic simulations. © 2024 IEEE.

关键词： Anisotropy

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