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32nd ACM International Conference on Multimedia, MM 2024

作者： Zhang, Delong Peng, Yi-Xing Wu, Xiao-Ming Wu, Ancong Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangdong Province Key Laboratory of Information Security Technology Guangzhou China

ISBN: (纸本)9798400706868

Online person re-identification services face privacy breaches from potential data leakage and recovery attacks, exposing cloud-stored images to malicious attackers and triggering public concern. The privacy protection of pedestrian images is crucial. Previous privacy-preserving person re-identification methods are unable to resist recovery attacks and compromise accuracy. In this paper, we propose an iterative method (PixelFade) to optimize pedestrian images into noise-like images to resist recovery attacks. We first give an in-depth study of protected images from previous privacy methods, which reveal that the chaos of protected images can disrupt the learning of recovery models. Accordingly, Specifically, we propose Noise-guided Objective Function with the feature constraints of a specific authorization model, optimizing pedestrian images to normal-distributed noise images while preserving their original identity information as per the authorization model. To solve the above non-convex optimization problem, we propose a heuristic optimization algorithm that alternately performs the Constraint Operation and the Partial Replacement Operation. This strategy not only safeguards that original pixels are replaced with noises to protect privacy, but also guides the images towards an improved optimization direction to effectively preserve discriminative features. Extensive experiments demonstrate that our PixelFade outperforms previous methods in resisting recovery attacks and Re-ID performance. © 2024 ACM.

关键词： Differential privacy

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Classification of Healthy and Diseased Lungs by Pneumonia Using X-Rays and Gene Sequencing With Deep Learning Approaches 5th

Classification of Healthy and Diseased Lungs by Pneumonia Us...

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5th International Conference on Smart Technologies in Data Science and Communication, SMART-DSC 2022

作者： Bhattacharya, Debdatta Satyanarayana, K.V. Rao, N. Thirupathi Joshua, Eali Stephen Neal Department of Computer Science and Engineering Koneru Laksmaiah Education Foundation Andhra Pradesh Vaddeswaram Guntur522302 India Department of Computer Science and Engineering Raghu Engineering College Andhra Pradesh Visakhapatnam India Department of Computer Science and Engineering Vignan’s Institute of Information Technology Andhra Pradesh Visakhapatnam530016 India

ISBN: (纸本)9789811968792

This research work is entitled to predict the lung disease using chest X-rays by deep learning technique. Lung disease is a term that refers to improper functioning of lungs. There are many diseases which occur due to the abnormal functioning of lungs. It includes tuberculosis, pneumonia, lung cancer, and asthma. The infection can be bacterial, viral, or fungal. It causes inflammation of trachea and respiratory failure. If found earlier, it can be cured, or else it can even lead to death. This project classifies the normal and abnormal X-ray with a percentage of accuracy so that we can give the treatment to the patient accordingly by seeing the X-ray. Algorithms used are convolutional neural network (CNN) and Inception Neural Network (INN) and TensorFlow which is Google open-source algorithm. The project is helpful for finding lung disease using chest X-ray. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Bronchitis Convolutional neural networks Inception neural network Inception v3 model Pneumonia Trachea

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An Adaptive Deep Learning Framework for Prediction of Agricultural Yield 2

An Adaptive Deep Learning Framework for Prediction of Agricu...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Rao, K Rama Swapna Kumari, Manthena Eklarker, Ravindra Shaker Reddy, Pundru Chandra Muley, Kirti Burugari, Vijay Kumar Geethanjali College of Engineering and Technology Electronics and Communication Engineering Telangana Hyderabad India Hyderabad India Vardhaman College of Engineering Department of Ai &ds Telangana Hyderabad India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Bharati Vidyapeeth's Institute of Management and Information Technology Master of Computer Applications Navi Mumbai India Koneru Lakshmaiah Education Foundation Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350317558

Agriculture and its large yields are vital to human survival. Recently, machine learning and deep learning have been applied to determine agricultural output. This study estimated apple production utilizing a unique deep-learning(DL)-based technique. A study-designed convolutional neural network (CNN) classified golden and royal gala apple photos. Classifying CNN-extracted pictures with traditional machine-learning(ML) methods was a hybrid application. Datasets A is used to evaluate the performance of the proposed model. Based on Dataset A accuracy findings, the CNN-SVM hybrid model performed best. CNN-SVM and CNN-Gradient Boosting hybrid models performed best and accurately for Dataset A. The more balanced distribution of train, test, and validation sizes, the suggested hybrid CNN model, and the model's assessment made dataset C the best choice. The hybrid model was 99.70% accurate for Dataset A. Precision, recall, f-measure, and Cohen kappa were 99%. The study found that hybrid models could accurately predict apple fruit yield using golden and royal gala photos. © 2024 IEEE.

关键词： DL Ensemble-Methods ML Smart-farms Yield-Prediction

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Designing Secure Cloud Computing Environments: Challenges and Solutions 15

Designing Secure Cloud Computing Environments: Challenges an...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Patil, Nagaraj Rege, P.R. Hemelatha, S. Mishra, Abhinav Revathi, K. Jain, Romil School of Engineering and Technology Karnataka Bangalore India Computer Vishwakarma Institute of Technology Pune India Karpagam Academy of Higher Education Department of Computer Science Engineering Coimbatore641021 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Karnataka Rajpura140417 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Information Technology Chennai127 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India

ISBN: (纸本)9798350370249

Designing relaxed cloud computing environments presents many demanding situations, updating the complexity of the infrastructure, the couple of jurisdictions in which companies are living, the form of protection models that exist for diverse necessities, and the ever-changing security threats. This paper gives an outline of the demanding situations of designing relaxed cloud computing environments and discusses the ability to deal with them. It describes the exclusive layers of safety and the emerging safety models, the security as a provider version, that need to be followed for cozy cloud computing. The paper additionally critiques the safety rules that are adopted to ensure the security of cloud computing and the security risks that could be superior technologies. Moreover, the paper examines the challenges of enforcement, procurement, implementation, and collaboration even as developing comfy cloud computing environments and discusses the answers to address them. Eventually, the paper concludes with the aid of highlighting the want updated expand complete cloud security architecture and updated up to date effect strong enforcement strategies up-to-date successfully relaxed cloud computing ecosystems. © 2024 IEEE.

关键词： Cloud security

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A multiple sensitive attributes data publishing method with guaranteed information utility

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CAAI Transactions on Intelligence technology 2023年第2期8卷 288-296页

作者： Haibin Zhu Tong Yi Songtao Shang Minyong Shi Zhucheng Li Wenqian Shang Department of Computer Science and Mathematics Nipissing UniversityNorth BayOntarioCanada Key Lab of Education Blockchain and Intelligent Technology Ministry of EducationGuangxi Normal UniversityGuilinChina Guangxi Key Lab of Multi‐Source Information Mining and Security Guangxi Normal UniversityGuilinChina School of Computer and Communication Engineering Zhengzhou University of Light IndustryZhengzhouChina School of Computer Science Communication University of ChinaBeijingChina Business College Beijing Union UniversityBeijingChina

Data publishing methods can provide available information for analysis while preserving *** multiple sensitive attributes data publishing,which preserves the relationship between sensitive attributes,may keep many records from being grouped and bring in a high record suppression *** category of multiple sensitive attributes data publishing,which reduces the possibility of record suppression by breaking the relationship between sensitive attributes,cannot provide the sensitive attributes association for ***,the existing multiple sensitive attributes data publishing fails to fully account for the comprehensive information *** acquire a guaranteed information utility,this article defines comprehensive information loss that considers both the suppression of records and the relationship between sensitive attributes.A heuristic method is leveraged to discover the optimal anonymity scheme that has the lowest comprehensive information *** experimental results verify the practice of the proposed data publishing method with multiple sensitive *** proposed method can guarantee information utility when compared with previous ones.

关键词： data analysis data privacy security of data

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Enhancing IoT Data Security: Integrating Elliptic Galois Cryptography with Matrix XOR Steganography and Adaptive Firefly Optimization 8

Enhancing IoT Data Security: Integrating Elliptic Galois Cry...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Anuradha, K. Alam, Irfan Nair, Deveendra Menon Narayanan Kiran, Ajmeera Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Faculty of Information and Communication Technology Kampar Malaysia Telangana India Scai Vit Bhopal University Cyber Security and Digital Forensics Division Kothari Kalan Sehore India Mlr Institute of Technology Department of Computer Science and Engineering Hdyerabad India

ISBN: (纸本)9798350376425

Securing the uninterrupted data flow is crucial in the fast-advancing Internet of Things (IoT) market. This work investigates the incorporation of elliptic Galois cryptography and matrix XOR coding steganography to improve data security. The suggested solution employs the elliptic Galois cryptography protocol to encrypt sensitive data from diverse medical records. The private information is embedded within images through the matrix XOR encoding steganography algorithm. The Adaptive Firefly optimization approach is used to improve the efficiency of utilizing cover blocks for steganography. The effectiveness of the proposed technique is evaluated using various modern approaches. We analyze the efficiency and security of the proposed approach by comparing it with a previous hybrid method employing the New York State Department of Health dataset. The results unequivocally validate that our approach offers enhanced safety in Internet of Things (IoT) applications. © 2024 IEEE.

关键词： Steganography

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Progressive Exploration-Conformal Learning for Sparsely Annotated Object Detection in Aerial Images 38

Progressive Exploration-Conformal Learning for Sparsely Anno...

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38th Conference on Neural information Processing Systems, NeurIPS 2024

作者： Lu, Zihan Wang, Chenxu Xu, Chunyan Zheng, Xiangwei Cui, Zhen PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Shandong Normal University Jinan China

The ability to detect aerial objects with limited annotation is pivotal to the development of real-world aerial intelligence systems. In this work, we focus on a demanding but practical sparsely annotated object detection (SAOD) in aerial images, which encompasses a wider variety of aerial scenes with the same number of annotated objects. Although most existing SAOD methods rely on fixed thresholding to filter pseudo-labels for enhancing detector performance, adapting to aerial objects proves challenging due to the imbalanced probabilities/confidences associated with predicted aerial objects. To address this problem, we propose a novel Progressive Exploration-Conformal Learning (PECL) framework to address the SAOD task, which can adaptively perform the selection of high-quality pseudo-labels in aerial images. Specifically, the pseudo-label exploration can be formulated as a decision-making paradigm by adopting a conformal pseudo-label explorer and a multi-clue selection evaluator. The conformal pseudo-label explorer learns an adaptive policy by maximizing the cumulative reward, which can decide how to select these high-quality candidates by leveraging their essential characteristics and inter-instance contextual information. The multi-clue selection evaluator is designed to evaluate the explorer-guided pseudo-label selections by providing an instructive feedback for policy optimization. Finally, the explored pseudo-labels can be adopted to guide the optimization of aerial object detector in a closed-loop progressive fashion. Comprehensive evaluations on two public datasets demonstrate the superiority of our PECL when compared with other state-of-the-art methods in the sparsely annotated aerial object detection task. The code will be available at: https://***/SAOD-research/PECL. © 2024 Neural information processing systems foundation. All rights reserved.

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Opposition-based multi-objective ant colony optimization framework for sustainable retrofitting: time–cost–energy–risk trade-offs

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Asian Journal of Civil engineering 2025年第5期26卷 2223-2239页

作者： Pokkuluri, Kiran Sree Chauhan, Trupti Ravindra Macedo, Víctor Daniel Jiménez Manjunath, T.C. Bharadwaj, Manish Sethi, Krushna Chandra Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Bhimavaram India Department of Design Manipal School of Architecture and Planning Manipal Academic of Higher Education Manipal Karnataka Udupi India Mechanical Engineering Faculty Michoacan University of Saint Nicholas of Hidalgo Morelia Mexico Department of Computer Science and Engineering Rajarajeswari College of Engineering Bengaluru India Department of Management Studies Atal Bihari Vajpayee Indian Institute of Information Technology and Management Gwalior India Civil Engineering Department Centurion University of Technology and Management Gajapati India

The increasing emphasis on sustainable retrofitting of buildings has brought attention to the need for optimizing trade-offs among critical objectives, such as time, cost, energy consumption, and risk. This study proposes an opposition-based multi-objective ant colony optimization (OB-MOACO) framework to address these challenges effectively. The framework incorporates opposition-based learning to enhance solution exploration and convergence in multi-objective optimization problems, specifically tailored for sustainable retrofitting projects. The study develops a time–cost–energy–risk trade-off (TCERT) model with four objectives: minimizing retrofitting time, cost, energy consumption, and risks. The model integrates constraints related to budget, project timelines, and energy efficiency to ensure feasibility and sustainability. A detailed case study of retrofitting a mixed-use building is presented, encompassing 11 aspects such as structural reinforcement, energy efficiency enhancement, and accessibility improvements. Results demonstrate the superiority of OB-MOACO over conventional methods like NSGA-III, MODE, and MOPSO in achieving Pareto-optimal solutions. Key performance metrics, including hypervolume (HV) and inverted generational distance (IGD), highlight the model's efficiency in balancing competing objectives. The study contributes to advancing sustainable retrofitting practices by providing an innovative, cost-effective, and energy-efficient decision-making framework. Implications for practitioners and policymakers are discussed, alongside recommendations for future research. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Multi-objective optimization Opposition-based ant colony optimization Pareto optimization Sustainable retrofitting Time–cost–energy–risk trade-off

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Text-to-Speech User Interface for ChatGPT 4

Text-to-Speech User Interface for ChatGPT

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4th International Conference on Electronic and Electrical engineering and Intelligent System, ICE3IS 2024

作者： Suni, Alfa Faridh Utomo, Aryo Baskoro Fathoni, Khoiruddin Mahendra, Budiandra Yusuf Seinsiani, Izzati Gemi Hastawan, Ahmad Fashiha Universitas Negeri Semarang Computer Engineering Semarang Indonesia Universitas Negeri Semarang Electrical Engineering Semarang Indonesia Universitas Negeri Semarang Information Technology Education Semarang Indonesia Universitas Negeri Semarang English Literature Study Program Semarang Indonesia

ISBN: (纸本)9798350378368

The emergence of OpenAI's ChatGPT is paying a lot of attention to Generative AI and its impact on Academic integrity. Generative AI is a system designed to generate content or output such as text, image, audio, simulation, video and code based on their respective training data. ChatGPT itself is in the form of a chatbot where users can communicate with AI through dialogue applications via text. GPT 3 API is the technology behind the ChatGPT. However, interaction via text is not as flexible as dialogue using voice. Therefore, speech-to-text technology is needed to interface between the user and the AI. Chatbot AI powered by GPT3 with the optional voice UI powered by Google Text to Speech clearly benefits accessibility and usability. The output of dialogue between human users and AI can be done through text or sound. This research attempts to implement a Text to Speech and vice versa Speech to Text as a User Interface on a Gen-AI using the GPT 3 API so that it will be more user-friendly and accessible. This research opens many possibilities for human-AI interaction (HAX) in implementing virtual assistants. A seamless dialogue between humans and AI will be our goal in the future. © 2024 IEEE.

关键词： ChatGPT Generative AI Speech to Text Text to Speech

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3D printed-electrospun PCL/hydroxyapatite/MWCNTs scaffolds for the repair of subchondral bone

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Regenerative Biomaterials 2023年第1期10卷 294-305页

作者： Yanyan Cao Lei Sun Zixian Liu Zhizhong Shen Wendan Jia Peiyi Hou Shengbo Sang College of Information Science and Engineering Hebei North UniversityZhangjiakou 075000China Shanxi Key Laboratory of Micro Nano Sensors&Artificial Intelligence Perception College of Information and ComputerTaiyuan University of TechnologyTaiyuan 030024China Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education Taiyuan University of TechnologyTaiyuan 030024China Shanxi Research Institute of 6D Artificial Intelligence Biomedical Science Taiyuan 030031China

Osteochondral defect caused by trauma or osteoarthritis exhibits a major challenge in clinical treatment with limited symptomatic effects at *** regeneration and remodeling of subchondral bone play a positive effect on cartilage regeneration and further promotes the repair of osteochondral *** use of the strengths of each preparation method,the combination of 3D printing and electrospinning is a promising method for designing and constructing multi-scale scaffolds that mimic the complexity and hierarchical structure of subchondral bone at the microscale and nanoscale,*** this study,the 3D printed-electrospun poly(ɛ-caprolactone)/nano-hydroxyapatites/multi-walled carbon nanotubes(PCL/nHA/MWCNTs)scaffolds were successfully constructed by the combination of electrospinning and layer-by-layer 3D *** resulting dual-scale scaffold consisted of a dense layer of disordered nanospun fibers and a porous microscale 3D scaffold layer to support and promote the ingrowth of subchondral ***,the biomimetic PCL/nHA/MWCNTs scaffolds enhanced cell seeding efficiency and allowed for higher cell-cell interactions that supported the adhesion,proliferation,activity,morphology and subsequently improved the osteogenic differentiation of bone marrow mesenchymal stem cells in ***,this study elucidates that the construction of 3D printed-electrospun PCL/nHA/MWCNTs scaffolds provides an alternative strategy for the regeneration of subchondral bone and lays a foundation for subsequent in vivo studies.

关键词： additive manufacturing 3D printing electrospinning subchondral bone

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