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Multi-feature Fusion Deep Network for Skin Disease Diagnosis

Multimedia Tools and Applications 2025年第1期84卷 419-444页

作者： Gairola, Ajay Krishan Kumar, Vidit Sahoo, Ashok Kumar Diwakar, Manoj Singh, Prabhishek Garg, Deepak Department of Computer Science and Engineering Graphic Era Hill University Dehradun India Department of Computer Science and Engineering Graphic Era deemed to be university Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India School of Computer Science and Artificial Intelligence SR University Warangal Telangana Warangal506371 India

The skin acts as an important barrier between the body and the external environment, playing a vital role as an organ. The application of deep learning in the medical field to solve various health problems has generated increasing interest. The objective of this research is to create a new model using a deep network with multi-feature fusion that enables accurate identification of various skin diseases. This paper proposes a fully fused network (FFN), which includes an improved single block (ISB) and an improved fusion block (IFB) to achieve optimal performance. To implement this, a convolutional neural network-based model for multi-class recognition of skin images has been developed, in which ISB is used to segment diseases in skin images. The IFB module is designed to enhance the effectiveness of the fused network. The model is built and evaluated using our proprietary dataset (Skin_disease_v1) along with publicly available datasets, namely ISIC2016, ISIC2017, and HAM10000. The highest accuracy achieved in experiments was 86% for ISB, 90% for IFB, and 92% for FFN using HAM10000 (for ResNet101V2), HAM10000 (for Resnet50 + ResNet101V2), and HAM10000 (for Resnet50 + ResNet101V2). This shows an accuracy improvement of 9.2%, 13.2%, and 15.2% compared to the state-of-the-art ISB, IFB, and FFN approaches, respectively. Our network outperforms existing networks in terms of performance. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

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Social Media User Behavior Mining and Sentiment Analysis based on computer Big data 4

Social Media User Behavior Mining and Sentiment Analysis bas...

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Zhang, Qiang Hou, Yutong School of Artificial Intelligence and Big Data Chongqing Metropolitan College of Science and Technology Chongqing China

ISBN: (纸本)9798350352931

Faced with the rapid development of social networks and the enormous business opportunities they contain, data mining and analysis based on social networks has become an inevitable trend. By utilizing various technologies such as Visual C++6.0 and Pajek, data collection and analysis were achieved. The user ID (Identification) was added to the Sina server and relevant user information was requested through the API (Application Programming Interface). Through the data analysis module, the stored data was processed appropriately for different research objects, and network analysis tools can be used to visually display certain results. Using this model, sentiment classification was performed on Weibo samples with existing labels, and the accuracy, recall and F1 value of manual labeling were evaluated. The positive accuracy was 0.85;recall was 0.82;F1 value was 0.84. This article helps to improve user experience and explore more user value. © 2024 IEEE.

关键词： data accuracy

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Leveraging machine learning for intelligent agriculture

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Discover Internet of Things 2025年第1期5卷 1-21页

作者： Sowmya, B.J. Meeradevi, A.K. Supreeth, S. Pradeep Kumar, D. Ravi Kumar, B.N. Rohith, S. Mishra, Divyansh Koushik, Abhishek Patil, Ankit U. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Computer Science Ramaiah Institute of Technology Bengaluru560054 India Department of Information Science and Engineering BMS Institute of Technology & Management Bengaluru560119 India Department of Electronics and Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India

Agriculture, the backbone of many economies, faces challenges like lack of information, outdated practices, and limited access to technology, hindering farmer productivity. This work proposes a user-friendly, multilingual platform leveraging Generative AI to address farmers' diverse needs. The platform encompasses various features to enhance agricultural practices. An LLM-powered Government Scheme Advisor functions as a multilingual chatbot offering intelligent guidance on government agricultural schemes and subsidies. The Disease Detection module utilizes AI technology for real-time identification and treatment recommendations, minimizing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential information based on geographical data to assist farmers in optimizing soil quality. A Crop Recommendation feature employs Machine Learning algorithms to offer personalized crop recommendations, considering various factors and aiding informed decision-making. The Crop Planning Tool, with its intuitive user interface, simplifies planning planting schedules and managing resources. Additionally, the platform includes an MSP Center Locator to find nearby Minimum Support Price (MSP) centers based on location. By integrating these innovative solutions, this platform bridges the gap between conventional agricultural techniques and contemporary technology, equipping farmers with the resources and expertise essential for advancing productivity and sustainability. Multilingual support ensures accessibility for a wider audience, breaking down language barriers and promoting inclusivity in the agricultural sector. This work proposes an innovative, multilingual platform powered by Generative AI to address these issues. Key features include an LLM-driven chatbot for government scheme guidance, AI-based real-time disease detection, and location-based tools for soil testing and MSP center identification. Additionally, the platf

关键词： Smart agriculture

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Efficient Evaluation Methods for Neural Architecture Search: A Survey

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2024年第12期5卷 5990-6011页

作者： Song, Xiaotian Xie, Xiangning Lv, Zeqiong Yen, Gary G. Ding, Weiping Lv, Jiancheng Sun, Yanan Sichuan University College of Computer Science Chengdu610065 China Oklahoma State University School of Electrical and Computer Engineering StillwaterOK74078 United States Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China City University of Macau Faculty of Data Science 999078 China

Neural architecture search (NAS) has received increasing attention because of its exceptional merits in automating the design of deep neural network (DNN) architectures. However, the performance evaluation process, as a key part of NAS, often requires training a large number of DNNs. This inevitably makes NAS computationally expensive. In past years, many efficient evaluation methods (EEMs) have been proposed to address this critical issue. In this article, we comprehensively survey these EEMs published up to date, and provide a detailed analysis to motivate the further development of this research direction. Specifically, we divide the existing EEMs into four categories based on the number of DNNs trained for constructing these EEMs. The categorization can reflect the degree of efficiency in principle, which can in turn help quickly grasp the methodological features. In surveying each category, we further discuss the design principles and analyze the strengths and weaknesses to clarify the landscape of existing EEMs, thus making easily understanding the research trends of EEMs. Furthermore, we also discuss the current challenges and issues to identify future research directions in this emerging topic. In summary, this survey provides a convenient overview of EEM for interested users, and they can easily select the proper EEM method for the tasks at hand. In addition, the researchers in the NAS field could continue exploring the future directions suggested in the article. © 2020 IEEE.

关键词： Network architecture

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Deep learning-based method for detection of copy-move forgery in videos

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Neural Computing and Applications 2025年第14期37卷 8451-8464页

作者： Elbarougy, Reda Abdelfatah, Osama Behery, G.M. El-Badry, Noha M. Department of Artificial Intelligence and Data Science College of Computer Science and Engineering University of Ha’il Ha’il81481 Saudi Arabia Department of Information Technology Faculty of Computer and Artificial Intelligence Damietta University New Damietta Kafr Saad Egypt Department of Computer Science Faculty of Computer and Artificial Intelligence Damietta University New Damietta Kafr Saad Egypt Department of Mathematics Faculty of Science Damietta University New Damietta Kafr Saad Egypt

Video forgery is one of the most serious problems affecting the credibility and reliability of video content. Therefore, detecting video forgery presents a major challenge for researchers due to the diversity of forgery types, the modernity of the programs used in forgery operations, and the abundance of information and content present in videos. The seriousness of this issue arises from the widespread use of videos in vital fields that require very high accuracy with no room for doubt or error, such as courtrooms, journalism, and others. Copy-move forgery is one of the most common and dangerous types of video forgery because of the difficulty in identifying it with the naked eye and the great diversity of forgery techniques involved in this particular type. One of the biggest challenges researchers have faced in the past is the complexity of the steps required to detect forgery in videos, leading to significant computational complexity and time consumption. The proposed method also aims to achieve better results than previous methods while reducing computational operations. Ultimately, forgery is detected with great efficiency. Compared to previous methods used for detecting copy-move forgery in the Rewind dataset, the proposed method achieves the highest F1, reaching 0.86, with a significant difference of 0.13 compared to the best result of the previous methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Deep learning

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Large circuit models: opportunities and challenges

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science China(Information sciences) 2024年第10期67卷 25-66页

作者： Lei CHEN Yiqi CHEN Zhufei CHU Wenji FANG Tsung-Yi HO Ru HUANG Yu HUANG Sadaf KHAN Min LI Xingquan LI Yu LI Yun LIANG Jinwei LIU Yi LIU Yibo LIN Guojie LUO Hongyang PAN Zhengyuan SHI Guangyu SUN Dimitrios TSARAS Runsheng WANG Ziyi WANG Xinming WEI Zhiyao XIE Qiang XU Chenhao XUE Junchi YAN Jun YANG Bei YU Mingxuan YUAN Evangeline F.Y.YOUNG Xuan ZENG Haoyi ZHANG Zuodong ZHANG Yuxiang ZHAO Hui-Ling ZHEN Ziyang ZHENG Binwu ZHU Keren ZHU Sunan ZOU Huawei Noah’s Ark Lab School of Integrated Circuits Peking University Faculty of Electrical Engineering and Computer Science Ningbo University Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Department of Computer Science and Engineering The Chinese University of Hong Kong School of Integrated Circuits Southeast University Huawei HiSilicon Peng Cheng Laboratory School of Computer Science Peking University School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University School of Artificial Intelligence Shanghai Jiao Tong University

Within the electronic design automation(EDA) domain, artificial intelligence(AI)-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domains, such as vision, text, and graph analytics, applying them to circuit design without tailoring to the unique complexities of electronic circuits. Such an “AI4EDA” approach falls short of achieving a holistic design synthesis and understanding,overlooking the intricate interplay of electrical, logical, and physical facets of circuit data. This study argues for a paradigm shift from AI4EDA towards AI-rooted EDA from the ground up, integrating AI at the core of the design process. Pivotal to this vision is the development of a multimodal circuit representation learning technique, poised to provide a comprehensive understanding by harmonizing and extracting insights from varied data sources, such as functional specifications, register-transfer level(RTL) designs, circuit netlists,and physical layouts. We champion the creation of large circuit models(LCMs) that are inherently multimodal, crafted to decode and express the rich semantics and structures of circuit data, thus fostering more resilient, efficient, and inventive design methodologies. Embracing this AI-rooted philosophy, we foresee a trajectory that transcends the current innovation plateau in EDA, igniting a profound “shift-left” in electronic design methodology. The envisioned advancements herald not just an evolution of existing EDA tools but a revolution, giving rise to novel instruments of design-tools that promise to radically enhance design productivity and inaugurate a new epoch where the optimization of circuit performance, power, and area(PPA) is achieved not incrementally, but through leaps that redefine the benchmarks of electronic systems' capabilities.

关键词： AI-rooted EDA large circuit models (LCMs) multimodal circuit representation learning circuit optimization

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Multi-Sensor Fusion for State Estimation and Control of Cable-Driven Soft Robots

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Journal of Bionic Engineering 2024年第6期21卷 2792-2803页

作者： Jie Ma Jinzhou Li Yan Yang Wenjing Hu Li Zhang Zhijie Liu School of Intelligence Science and Technology University of Science and Technology BeijingBeijing100083People’s Republic of China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of EducationUniversity of Science and Technology BeijingBeijing100083People’s Republic of China School of Artificial Intelligence China University of Mining and TechnologyBeijing100083People’s Republic of China

Cable-driven soft robots exhibit complex deformations,making state estimation ***,this paper develops a multi-sensor fusion approach using a gradient descent strategy to estimate the weighting *** coefficients combine measurements from proprioceptive sensors,such as resistive flex sensors,to determine the bending ***,the fusion strategy adopted provides robust state estimates,overcoming mismatches between the flex sensors and soft robot ***,a nonlinear differentiator is introduced to filter the differentiated sensor signals to address noise and irrational values generated by the Analog-to-Digital Converter.A rational polynomial equation is also introduced to compensate for temperature drift exhibited by the resistive flex sensors,which affect the accuracy of state estimation and *** processed multi-sensor data is then utilized in an improved PD controller for closed-loop control of the soft *** controller incorporates the nonlinear differentiator and drift compensation,enhancing tracking *** results validate the effectiveness of the integrated approach,demonstrating improved tracking accuracy and robustness compared to traditional PD controllers.

关键词： Cable-driven soft robot Drift compensation Multi-sensor fusion Resistive flex sensor Closed loop control

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Expressive Voice Conversion Using Perturbation and Autoencoder Loss 12

Expressive Voice Conversion Using Perturbation and Autoencod...

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12th IEEE International Conference on Information and Communication Networks, ICICN 2024

作者： Chen, Zhe Liu, Yining Liu, Gao School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou China College of Computer Science Chongqing University Chongqing China

ISBN: (纸本)9798350355802

Expressive voice conversion is to transform voice speech with emotional expression. Modeling emotional styles in expressive voice conversion is challenging. It is essential to eliminate the interference of factors such as the identity in emotional style modeling. This paper addresses this issue using an information perturbation method. During the feature extraction phase, perturbation functions are applied to factors such as formants and pitch in the speech signal to eliminate the interference of speaker information. This allows for separate modeling and extraction of content information and emotional information. We propose a general framework for expressive voice conversion, which aims to disentangle content information, speaker information, and emotional style information from the voice signal. Experimental results demonstrate the effectiveness of our proposed framework in both subjective and objective evaluations. It can extract highly expressive and understandable features from the source speech while maintaining similarity to the target speaker. © 2024 IEEE.

关键词： Signal modulation

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Graph Neural Network Based on Spectral Parameter Estimation for Mobile Networks 10

Graph Neural Network Based on Spectral Parameter Estimation ...

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10th International Conference on Engineering and Emerging Technologies, ICEET 2024

作者： Jin, Renhong Yan, Xin School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430070 China

ISBN: (纸本)9798331532895

For online learning, Graph Neural Networks(GNNs) often require real graph data for parameter fine-tuning, but obtaining the graph data is challenging. Currently, GNNs are trained based on existing graph data. When learning in dynamic networks, spatial-domain GNNs use the adjacency matrix of entire graphs as its input, which are extremely resource-intensive in terms of computation and storage while dealing with large-scale graph data. Moreover, spectral-domain GNNs suffer from high spectral decomposition complexity. To enhance the generalization of GNN models and achieve lightweight GNN models, this paper proposes a lightweight GNN model based on spectral domain, SDGAT(Spectral-Domain Graph Attention Network). By combining spectral domain graph convolution and attention mechanism, SDGAT can effectively capture the global structure and local features of a graph, which helps to suppress noise in the graph and improve the robustness of the model. Furthermore, a distributed estimation algorithm for dynamic network spectral parameters is proposed, which significantly reduces the computational overhead when processing large-scale graph data. Finally, authors validate the effectiveness and accuracy of SDGAT through classification and regression tasks, and explore the feasibility of extending this model to other GNNs. © 2024 IEEE.

关键词： Graph neural networks

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Revolutionizing Influenza A Detection in Humans: Unleashing the Power of XGBoost Classification 2

Revolutionizing Influenza A Detection in Humans: Unleashing ...

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2nd IEEE International Conference for Women in Innovation, technology and Entrepreneurship, ICWITE 2024

作者： Nikhil, V. Sahaaya Arul Mary, S.A. Natarajan, B. Bajulunisha, A. Anuradha, R.S. Venkatraman, K. Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India VIT University School of Computer Science Engineering Chennai India M.A.M College of Engineering and Technology Department of Artificial Intelligence & Data Science Tiruchirappalli621105 India Panimalar Engineering College Department of Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798350383287

Influenza A, a zoonotic virus potentially affecting and infecting humans, poses a significant global health threat. This research paper presents a comprehensive study on predicting Influenza A outbreaks by applying the XGBoost classification algorithm. The study utilizes two substantial datasets: one from the World Health Organization (WHO) containing global Influenza A epidemiological data from 2003 to 2023 and the other encompassing Influenza A infections in New York counties from 2009 to 2019. Both datasets underwent pre-processing, with essential features selected to develop an XGBoost Classification model for forecasting Influenza A cases in humans. The models exhibited impressive accuracy and performance metrics. Specifically, the WHO dataset demonstrated an AUC of 0.996 and an accuracy score of 0.99, while the New York dataset displayed an AUC of 0.992 and an accuracy score of 0.978. These outcomes underscore the model's capability to differentiate between positive and negative cases, showcasing high precision, accuracy, and F1 scores. The implications of this research extend to various domains, including the early detection of outbreaks, informed decision-making processes, and the implementation of public health interventions. The findings emphasize the pivotal role of high-quality and comprehensive datasets in training effective predictive models. Furthermore, the study points towards potential future avenues, such as personalized predictions, geo-spatial analysis, and integrating these predictive models with healthcare systems. © 2024 IEEE.

关键词： Classification (of information)

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