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arXiv 2022年

作者： Cao, Xiaofeng Bu, Weixin Huang, Shengjun Zhang, Minling Tsang, Ivor W. Ong, Yew Soon Kwok, James T. The School of Artificial Intelligence Jilin University Changchun130012 China The MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China The School of Computer Science and Engineering Southeast University Nanjing210096 China Ministry of Education China The Australian Artificial Intelligence Institute University of Technology SydneyNSW2008 Australia Singapore The School of Computer Science and Engineering Nanyang Technological University Nanyang Avenue Singapore639798 Singapore Artificial Intelligence Scientist of A∗STAR Singapore The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

learning on big data brings success for artificial intelligence (AI), but the annotation and training costs are expensive. In future, learning on small data that approximates the generalization ability of big data is one of the ultimate purposes of AI, which requires machines to recognize objectives and scenarios relying on small data as humans. A series of learning topics is going on this way such as active learning and few-shot learning. However, there are few theoretical guarantees for their generalization performance. Moreover, most of their settings are passive, that is, the label distribution is explicitly controlled by finite training resources from known distributions. This survey follows the agnostic active sampling theory under a PAC (Probably Approximately Correct) framework to analyze the generalization error and label complexity of learning on small data in model-agnostic supervised and unsupervised fashion. Considering multiple learning communities could produce small data representation and related topics have been well surveyed, we thus subjoin novel geometric representation perspectives for small data: the Euclidean and non-Euclidean (hyperbolic) mean, where the optimization solutions including the Euclidean gradients, non-Euclidean gradients, and Stein gradient are presented and discussed. Later, multiple learning communities that may be improved by learning on small data are summarized, which yield data-efficient representations, such as transfer learning, contrastive learning, graph representation learning. Meanwhile, we find that the meta-learning may provide effective parameter update policies for learning on small data. Then, we explore multiple challenging scenarios for small data, such as the weak supervision and multi-label. Finally, multiple data applications that may benefit from efficient small data representation are surveyed. Copyright © 2022, The Authors. All rights reserved.

关键词： Big data

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Smart Locker 2.0: Leveraging IoT and machine learning for Secure, User-Friendly Public Storage

Smart Locker 2.0: Leveraging IoT and Machine Learning for Se...

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artificial intelligence and Data engineering (AIDE), International Conference on

作者： Chandrappa S Prithviraj Aditya Pai H Rajesh I S Manjunath Sargur Krishnamurthy R Sneha Shetty R Dept. of Computer Science and Engineering JAIN (Deemed to-be University) Bengaluru India Dept. of Computer Science and Engineering Nitte (Deemed to be University) NMAM Institute of Technology Nitte India Department of CSE MIT Art Design and Technology University Pune India Dept. of AI & ML BMS Institute of Technology and Management Bengaluru India JP Morgan & Chase Co Houston USA Dept. of Artificial Intelligence and Machine Learning Nitte (Deemed to be University) NMAM Institute of Technology Nitte India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Smart locker systems are a revolutionary technology, combining advanced automation and connectivity to address increasingly demanding requirements for secure, efficient, and convenient storage solutions in numerous scenarios. These intelligent lockers use the latest technologies, such as the Internet of Things (IoT) and mobile applications, to revolutionize the way people store their belongings. Existing Smart Locker System lacks measurable results. To sustain its claims, we include key findings. For security, the system utilizes RFID authentication and biometric security, which reduces unauthorized access by 98% and blocks spoofing attacks. For convenience, a study with 50 users found 92% satisfaction, with 87% preferring it over traditional lockers due to faster access, averaging 3 seconds. For environmental benefits, the system cut power use by using sleep mechanism and saved paper usage by switching to cloud logs. Real-World tests achieved 98% user satisfaction with the university system proving its efficiency, security, and sustainability. This system expounds on multifaceted benefits and functionalities in smart lockers. First, it covers how these lockers provide better security compared to old lock-and-key systems. Using access controls reduces the risk of theft and illegal access to one's valuables and sensitive belongings by a great degree. It has an easy reservation, access, and management of personal lockers via a remote means; this proves to be seamless without needing any physical keys and contactless interactions, ideal for modern spaces considering hygiene and efficiency is top priority. Moreover, it goes on to analyze the flexibility of smart locker systems. From corporate offices to co-working spaces, universities, shopping malls, and public transportation hubs, these lockers cater to diverse user needs, creating flexible and scalable solutions adaptable to a variety of environments.

关键词： Biometrics Cloud computing Switches machine learning Mobile applications Internet of Things Sustainable development Secure storage Public transportation Radiofrequency identification

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A Comprehensive Evaluation of Fake Face Recognition Scheme using artificial intelligence Oriented learning Scheme

A Comprehensive Evaluation of Fake Face Recognition Scheme u...

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Pioneering Developments in computer Science & Digital Technologies (IC2SDT), International Conference on

作者： S. Subashree Remya Rose S A. S. Valarmathy Princy Joseph P. Jasmin Dennis S. Ravi Department of Computer Science and Engineering E.G.S. Pillay Engineering College Nagapattinam Tamil Nadu India Department of Artificial Intelligence and Machine Learning R.M.D. Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering CMR College of Engineering and Technology Hyderabad India Department of Electronics and Communication Engineering Vi Institute of Technology Chengalpattu Tamil Nadu India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350365016

ISBN: (纸本)9798350365023

Fake face recognition has emerged as a critical area of research due to the proliferation of synthetic media and its potential misuse in various domains. This study presents a thorough evaluation of a novel Fake Face Recognition Scheme utilizing artificial intelligence Oriented learning Scheme (AIOLS) coupled with Generative Adversarial Network (GAN)-GoogleNet integration. The aim is to discern authentic facial images from synthetic or tampered ones with high accuracy. In this research, we employed a dataset comprising a diverse range of authentic and synthetic facial images. The proposed scheme incorporates the power of GANs for generating synthetic facial images and leverages GoogleNet, a state-of-the-art deep convolutional neural network, for discriminative feature extraction. The integration within the AIOLS framework facilitates efficient learning and adaptation to complex patterns present in fake facial images. The evaluation of our scheme was conducted through rigorous experimentation on the dataset, employing various performance metrics. Notably, the accuracy obtained was measured at an impressive 96.7%, signifying the effectiveness of our approach in distinguishing between real and fake facial images. Additionally, other metrics such as precision, recall, and F1-score were also computed to provide a comprehensive assessment of the scheme's performance.

关键词： Measurement Accuracy Image recognition Face recognition Authentication learning (artificial intelligence) Media Generative adversarial networks Feature extraction Convolutional neural networks

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Block Chain based Malware Detection System for Smartphone Application

Block Chain based Malware Detection System for Smartphone Ap...

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International Conference on Communication and Electronics Systems (ICCES)

作者： P. Robert R. Senkamalavalli V. Vedanarayanan D. Manivannan Department of Artificial Intelligence and Machine Learning CMR Institute of Technology Bengaluru Karnataka India Department of Computer Science Engineering East Point College of Engineering and Technology Bangalore Karnataka India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science Engineering Vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology Chennai Tamil Nadu India

The quick rise of smartphone malware indicates that cybercriminals are switching from traditional personal computer systems to mobile devices. For adequate security, technology specialists are currently creating new anti-malware techniques. This study proposes the use of a block chain to identify malicious smartphone applications. Two internal and external proprietary block chains are combined to create a private dual block chain and a block chain alliance for the decision-making process in the system. While the external block chain stores the blocks for apps of existing models, the private internal block chain stores include blocks drawn from both statically and dynamically driven function removers. Additionally, the proposed system blocks the outside parties, enabling anti-malware vendors to provide more precise solutions. Because cloud resources can control loads and durability, resource monitoring may be dynamic in nature. The additional feature of the proposed system is that authentication techniques like cryptography and digital signature can be integrated. Finally, the effectiveness of the suggested structure is assessed in terms of throughput and precision rate for malware detection.

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Data-Efficient CLIP-Powered Dual-Branch Networks for Source-Free Unsupervised Domain Adaptation

arXiv

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arXiv 2024年

作者： Li, Yongguang Cao, Yueqi Li, Jindong Wang, Qi Wang, Shengsheng College of Software Jilin University Changchun130012 China College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China School of Artificial Intelligence Jilin University Changchun130012 China Engineering Research Center of Knowledge-Driven Human-Machine Intelligence Ministry of Education Jilin University Changchun130012 China

Source-free Unsupervised Domain Adaptation (SF-UDA) aims to transfer a model’s performance from a labeled source domain to an unlabeled target domain without direct access to source samples, addressing critical data privacy concerns. However, most existing SF-UDA approaches assume the availability of abundant source domain samples, which is often impractical due to the high cost of data annotation. To address the dual challenges of limited source data and privacy concerns, we introduce a data-efficient, CLIP-powered dual-branch network (CDBN). This architecture consists of a cross-domain feature transfer branch and a target-specific feature learning branch, leveraging high-confidence target domain samples to transfer text features of source domain categories while learning target-specific soft prompts. By fusing the outputs of both branches, our approach not only effectively transfers source domain category semantic information to the target domain but also reduces the negative impacts of noise and domain gaps during target training. Furthermore, we propose an unsupervised optimization strategy driven by accurate classification and diversity, preserving the classification capability learned from the source domain while generating more confident and diverse predictions in the target domain. CDBN achieves near state-of-the-art performance with far fewer source domain samples than existing methods across 31 transfer tasks on seven datasets. © 2024, CC BY.

关键词： Adversarial machine learning

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Wheat Rust Disease Segmentation from Ground Imagery

Wheat Rust Disease Segmentation from Ground Imagery

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Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Hirra Anwar Muhammad Jawad Khan Muhammad Fayyaz Ajmal Saeed Mian Faisal Shafait School of Mechanical and Manufacturing Engineering NUST Islamabad Pakistan Department of Electrical Engineering Prince Sattam Bin Abdul Aziz University Al-Kharj Saudi Arabia Crop Diseases Research Institute National Agriculture Research Center Islamabad Pakistan University of Western Australia Perth Australia School of Electrical Engineering & Computer Science NUST Islamabad Pakistan Deep Learning Laboratory National Center of Artificial Intelligence Islamabad Pakistan

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Wheat rust disease is a major agricultural threat, causing significant yield losses globally. Precision agriculture and autonomous robot systems represent pivotal advancements in modern farming and disease detection, leveraging technology to enhance crop management, optimize resource use, and improve productivity. These systems rely heavily on accurate and comprehensive data to train effectively. However, current datasets often fall short of meeting the specific needs of these advanced agricultural technologies. This paper addresses this gap by introducing a primary contribution in the form of an open-source dataset focused on wheat rust disease segmentation for precision agriculture. Open-source datasets in literature are either general plant disease datasets or wheat disease classification datasets. Our proposed wheat rust segmentation dataset, Focussed Rust Disease Images (FRDI), provides close-up views of disease manifestations on wheat leaves, capturing the natural variability and complexity of diseases in field conditions, thereby meeting the requirements of precision agriculture systems. This facilitates detailed analysis and effective training of deep learning models designed for disease detection and segmentation tasks for real-world agricultural environments. Furthermore, in our work we present an improved wheat rust disease detection and segmentation technique which is based on the concept of co-salient feature extraction for disease identification within complex and cluttered field images. By integrating localized and generic contextual information, our proposed approach effectively identifies key features and intricate patterns associated with wheat rust disease.

关键词： Precision agriculture Training Deep learning Representation learning Productivity Image segmentation Plant diseases Object detection Feature extraction Diseases

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Parallel and Streaming Wavelet Neural Networks for Classification and Regression under Apache Spark

arXiv

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arXiv 2022年

作者： Venkatesh, Eduru Harindra Vivek, Yelleti Ravi, Vadlamani Shankar, Orsu Shiva Center for Artificial Intelligence and Machine Learning Institute for Development and Research in Banking Technology Castle Hills Road #1 Masab Tank Hyderabad500076 India School of Computer science and Information Sciences University Of Hyderabad Gachibowli Hyderabad500046 India

MSC Codes 68T09, 68TxxWavelet neural networks (WNN) have been applied in many fields to solve regression as well as classification problems. After the advent of big data, as data gets generated at a brisk pace, it is imperative to analyze it as soon as it is generated owing to the fact that the nature of the data may change dramatically in short time intervals. This is necessitated by the fact that big data is all pervasive and throws computational challenges for data scientists. Therefore, in this paper, we built an efficient Scalable, Parallelized Wavelet Neural Network (SPWNN) which employs the parallel stochastic gradient algorithm (SGD) algorithm. SPWNN is designed and developed under both static and streaming environments in the horizontal parallelization framework. SPWNN is implemented by using Morlet and Gaussian functions as activation functions. This study is conducted on big datasets like gas sensor data which has more than 4 million samples and medical research data which has more than 10,000 features, which are high dimensional in nature. The experimental analysis indicates that in the static environment, SPWNN with Morlet activation function outperformed SPWNN with Gaussian on the classification datasets. However, in the case of regression, the opposite was observed. In contrast, in the streaming environment i.e., Gaussian outperformed Morlet on the classification and Morlet outperformed Gaussian on the regression datasets. Overall, the proposed SPWNN architecture achieved a speedup of 1.32-1.40. © 2022, CC BY-NC-ND.

关键词： MapReduce

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A Review on the Application of Radiomics and Deep learning for Disease Identification in Musculoskeletal Radiography

A Review on the Application of Radiomics and Deep Learning f...

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International Conference on Inventive Research in Computing Applications (ICIRCA)

作者： Shyamali Das Rama Devi C M. Gomathy Nayagam D Suganthi Fidha Thachamkode K. Manoj Senthil Department of Artificial intelligence and Machine learning CMR institute of technology Bengaluru Electrical and Electronics Engineering St.Joseph's college of Engineering Department of Computer Science and Engineering Ramco Institute of Technology Rajapalayam Department of Computer Science Saveetha College of Liberal Arts and Sciences Saveetha Institute of Medical And Technical Sciences Chennai Department of Oral Medicine and Radiology KMCT Dental college Kozhikode Kerala Electronics and Communication Engineering Kongu Engineering College Erode

Musculoskeletal disorders cause most disability worldwide. Musculoskeletal (MSK) issues are diagnosed, predicted, and treated utilizing imaging methods, magnetic resonance imaging (MRI), and ultrasound, and their precise interpretation by specialized radiologists. Radiological scanning aids metabolic health, aging, and diabetes assessment. Proposed research shows how machine learning, specifically deep learning, may be used to analyze MRI data quickly and reliably, addressing a crucial clinical need in MSK radiology. As primary care radiography services grow, so does interest in how AI, and especially deep learning, might help radiologists and primary care physicians make accurate diagnoses and enhance patient treatment. Chronic pain impairs mobility, dexterity, and function. Diagnosis requires bone X-rays. Deep learning algorithms are increasingly used in musculoskeletal radiology, with promising outcomes. The proposed method uses a calibrated ensemble of deep learners to musculoskeletal radiograph recognition. In the suggested model uses three deep neural networks, which are commonly utilized in deep learning-based solutions for this problem domain. Publicly available dataset to demonstrates the suggested strategy outperforms three individual models and a standard ensemble learner. This study strongly supports the use of a calibrated ensemble approach for identifying abnormalities in musculoskeletal X-rays, where the greatest radiologist accuracy is between 0.95 and 0.98 in Cohen's kappa statistic.

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TNPAR: Topological Neural Poisson Auto-Regressive Model for learning Granger Causal Structure from Event Sequences

arXiv

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arXiv 2023年

作者： Liu, Yuequn Cai, Ruichu Chen, Wei Qiao, Jie Yan, Yuguang Li, Zijian Zhang, Keli Hao, Zhifeng School of Computer Science Guangdong University of Technology Guangzhou China Peng Cheng Laboratory Shenzhen China Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Huawei Noah's Ark Lab Huawei Paris France College of Science Shantou University Shantou China

learning Granger causality from event sequences is a challenging but essential task across various applications. Most existing methods rely on the assumption that event sequences are independent and identically distributed (i.i.d.). However, this i.i.d. assumption is often violated due to the inherent dependencies among the event sequences. Fortunately, in practice, we find these dependencies can be modeled by a topological network, suggesting a potential solution to the non-i.i.d. problem by introducing the prior topological network into Granger causal discovery. This observation prompts us to tackle two ensuing challenges: 1) how to model the event sequences while incorporating both the prior topological network and the latent Granger causal structure, and 2) how to learn the Granger causal structure. To this end, we devise a unified topological neural Poisson auto-regressive model with two processes. In the generation process, we employ a variant of the neural Poisson process to model the event sequences, considering influences from both the topological network and the Granger causal structure. In the inference process, we formulate an amortized inference algorithm to infer the latent Granger causal structure. We encapsulate these two processes within a unified likelihood function, providing an end-to-end framework for this task. Experiments on simulated and real-world data demonstrate the effectiveness of our approach. Copyright © 2023, The Authors. All rights reserved.

关键词： Topology

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Design and Implementation of Signal Processing for 2D Scanning Radar with Multi-Reflection Detection Using 60 GHz NRD Guide Pulse Radar

Design and Implementation of Signal Processing for 2D Scanni...

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Intelligent Control, Computing and Communications (IC3), International Conference on

作者： H Pal Thethi N V Uma Reddy R S Sengar Preethi Dashyam T Mounika Tamam Ali Abd Ulabbas Abedi Geetika Sharma Lovely Professional University Phagwara India Department of Artificial Intelligence and Machine Learning New Horizon College of Engineering Bangalore India Department of Electrical & Electronics Engineering GL Bajaj Group of Institutions Mathura Department of Information Technology GRIET Hyderabad Telangana India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq Department of Electrical Electronics & Communication Engineering Galgotias university Gerater Noida Uttar Pradesh

ISBN: (数字)9798331527495

ISBN: (纸本)9798331527501

The design and implementation of a signal processing system for a two dimensional (2D) electric scanning radar using multi-reflection detection to identify short range object using 60 GHz NRD (Non Radiative Dielectric) guide pulse radar is presented in this research paper. Designed to increase detection accuracy and precision by processing multiple reflections of the transmitted signals in order to recognize objects at short distances, the radar system is the one. The signal processing algorithms are adapted for MPTS handling, and the system takes advantage of the high frequency properties of the 60 GHz NRD guide. Hardware design, simulation results, and performance evaluation regarding the detection accuracy, range resolution, and processing latency, are also discussed. However, applications like autonomous driving, indoor navigation, and industrial automation require short range, high precision radar system, and this approach offers a big advantage.

关键词： Accuracy Radar measurements Radar clutter Radar detection Radar Radar signal processing Real-time systems Reflection High frequency Signal resolution

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