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International Journal of Systematic Innovation 2024年第1期8卷 1-10页

作者： Ch, Ravikumar Radha, Marepalli Mahendar, Maragoni Manasa, Pinnapureddy Department of Artificial Intelligence &Data Science Chaitanya Bharathi Institute of Technology Hyderabad 500075 India Department of Computer Science and Engineering CVR College of Engineering Hyderabad 501510 India Department of Computer Science and Engineering Neil Gogte Institute of Technology Hyderabad 500039 India

The detection of counterfeit photographs is critical in the digital age because of the widespread development of digital media and its significant impact on social networks. The legitimacy of digital content is being threatened by the growing sophistication of picture counterfeiting. With the help of pre-trained VGG-16 models and deep learning techniques that integrate Error Level Analysis (ELA) and Convolutional Neural Networks (CNNs), this study presents a fresh solution to this problem. The study thoroughly assesses and contrasts these models with a dataset that has been carefully chosen to bring the presented findings into perspective. To ensure a reliable evaluation of each model's performance 5000 experiments were carried out in total. With an accuracy rate of 99.87% and an accurate identification rate of 99% of hidden forgeries, the results demonstrate the exceptional effectiveness of the ELA-CNN model. However, despite its robustness, the VGG-16 model only achieves a significantly lower accuracy rate of 97.93% and a validation rate of 75.87%. This study clarifies the relevance of deep learning in the identification of image forgeries and highlights the practical ramifications of various models. Moreover, the research recognizes its constraints, especially for highly advanced counterfeits, and proposes possible paths for enhancing the accuracy and scope of detection algorithms. In the ever-changing world of digital media, the thorough comparative analysis provided in this study offers insightful information that can direct the creation of accurate forgery detection tools, protecting digital content integrity and reducing the effects of image manipulation. © (2023), (Society of Sytematic Innovation). All Rights Reserved.

关键词： Counterfeit images deep learning ELA-CNN Image forgery detection VGG-16 model

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Comparative Study of Machine Learning Algorithms for Feature Extraction and Prediction of Bacterial Leaf Blight Disease in Paddy Fields 8

Comparative Study of Machine Learning Algorithms for Feature...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Krishna, Penugonda Seetha Rama Nagarajan, S. Annamalai University Dept. of Computer Science Engineering Tamilnadu Chidambaram India Government College of Engineering Srirangam Department of Computer Science and Engineering Trichy620012 India

ISBN: (纸本)9798350386578

Paddy farming plays a critical role in global food security, yet diseases like Bacterial Leaf Blight (BLB) pose significant threats to crop yield and quality. Early detection and accurate diagnosis are essential for effective disease management. This research study investigates machine learning methods for feature extraction and prediction of BLB in paddy fields. Utilizing a comprehensive dataset of paddy leaf images, several machine learning models including Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbours (kNN), and Gradient Boosting Machine (GBM) were trained and evaluated. Performance metrics such as accuracy, precision, recall, and F1-score were employed to assess the effectiveness of each model. The comparative analysis discusses the strengths and limitations of each method, focusing on those that demonstrate superior capability in early and accurate detection of BLB. These findings provide valuable insights for agricultural scientists and practitioners to enhance disease management strategies using modern machine learning techniques. By leveraging this knowledge, specialists can optimize interventions and improve agricultural practices to mitigate the impact of diseases like BLB on paddy farming and ensure global food security. © 2024 IEEE.

关键词： Adversarial machine learning

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Semantic segmentation of brain tumor images using attention-based residual light u-net model

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Multimedia Tools and Applications 2025年第10期84卷 7425-7441页

作者： Chakrapani Kumar, Sunil Department of Information Technology ABV-IIITM MP Gwalior474015 India Department of Computer Science and Engineering ABV-IIITM MP Gwalior474015 India

Accurate segmentation of brain tumor regions in MRI images is essential for monitoring tumor growth. In view of this, several automated brain tumor segmentation models are proposed. U-Net is one of the most popular models widely used for image segmentation. Based on the U-Net architecture, complex models are proposed for brain tumor segmentation. However, existing models mainly focus on increasing the depth of the model for better accuracy without much bothering the transmission of spatial information. Attention-based U-Net model can help to extract salient local-level features that can be passed to the decoder part of the network. Considering the above issues, we propose a variant of the U-Net model called the Attention-based Residual Light U-Net model. The proposed model is effective in the sense that it has comparatively less model complexity, a better attention mechanism, and a combined loss function that can handle class imbalance problems. Our experiments on one of the latest BraTS-2021 datasets show that the proposed method can achieve a mean Intersection over Union of 0.8874, 0.8863, 0.8905, and dice score of 0.9415, 0.9387, 0.9115 on the whole tumor, tumor core, and enhancing tumor, respectively. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Tumors

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TotalCT-SAM: A Whole-Body CT Segment Anything Model with Memorizing Transformer 27th

TotalCT-SAM: A Whole-Body CT Segment Anything Model with Mem...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Zhiwei Shen, Yiqing School of Computer Engineering and Science Shanghai University Shanghai China Department of Computer Science Johns Hopkins University BaltimoreMD United States

ISBN: (纸本)9783031781971

Whole-body computed tomography (CT) is a crucial medical imaging modality that provides a comprehensive view of tissue anatomy, facilitating the detection and diagnosis of various conditions, including cancer, trauma-related injuries, vascular abnormalities, infectious diseases, and organ pathologies. However, existing deep learning methods for whole-body CT segmentation, such as nnU-Net, often suffer from limited generalization capabilities, hindering their adaptability to diverse clinical needs and real-world scenarios. To address this issue, we propose a novel interactive semantic segmentation method based on the recently introduced Segment Anything Model (SAM), which employs a foundation model approach to enable flexible instance segmentation. Our method extends SAM to overcome its limitation of losing important semantic information during the segmentation process, making it specifically tailored for whole-body CT anatomy segmentation. The proposed approach allows for both prompt-free and prompt-guided segmentation, accommodating different use cases and providing enhanced flexibility. Furthermore, we introduce a memory bank module that expands the context of the self-attention mechanism through approximate k-nearest neighbor (KNN) lookup, enabling the model to capture long-range dependencies and attend to distant relevant features, thereby improving its ability to handle the complexities of whole-body CT data. Experimental results demonstrate that our method achieves competitive performance compared to other state-of-the-art approaches while preserving rich semantic information at the pixel level. Our code is publicly available at https://***/13482108753/Totalct-SAM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5026-5039页

作者： Li, Mengke Cheung, Yiu-Ming Lu, Yang Hu, Zhikai Lan, Weichao Huang, Hui Shenzhen518132 China Hong Kong Baptist University Department of Computer Science 999077 Hong Kong Xiamen University Fujian Key Laboratory of Sensing and Computing for Smart City School of Informatics Xiamen361005 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

It is not uncommon that real-world data are distributed with a long tail. For such data, the learning of deep neural networks becomes challenging because it is hard to classify tail classes correctly. In the literature, several existing methods have addressed this problem by reducing classifier bias, provided that the features obtained with long-tailed data are representative enough. However, we find that training directly on long-tailed data leads to uneven embedding space. That is, the embedding space of head classes severely compresses that of tail classes, which is not conducive to subsequent classifier learning. This article therefore studies the problem of long-tailed visual recognition from the perspective of feature level. We introduce feature augmentation to balance the embedding distribution. The features of different classes are perturbed with varying amplitudes in Gaussian form. Based on these perturbed features, two novel logit adjustment methods are proposed to improve model performance at a modest computational overhead. Subsequently, the distorted embedding spaces of all classes can be calibrated. In such balanced-distributed embedding spaces, the biased classifier can be eliminated by simply retraining the classifier with class-balanced sampling data. Extensive experiments conducted on benchmark datasets demonstrate the superior performance of the proposed method over the state-of-the-art ones. © 2020 IEEE.

关键词： Benchmarking

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Advanced Water Quality Assessment through Integrated Machine Learning models for Classification 8

Advanced Water Quality Assessment through Integrated Machine...

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8th IEEE International Conference on Computing, Communication, Control and Automation, ICCUBEA 2024

作者： Ladani, Dhruv Bhesaniya, Nency Shekhaliya, Dhyey Swain, Debabrata Bhilare, Amol Pandit Deendayal Energy University Department of Computer Science and Engineering Gandhinagar India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798350391770

Water, the basic necessity of any living organisms, constitutes a fundamental human right yet remains tragically inaccessible and unsafe for millions. With nearly 2.5 million annual deaths attributed to waterborne diseases, ensuring access to pollution-free water transcends mere necessity, becoming a potent catalyst for improved global health. Freshwater scarcity is a global problem that is being exacerbated by overexploitation of freshwater sources and climate change impacts. This research delves into machine learning algorithms for analyzing and classifying safe water quality. This research study conducts a comparative analysis of water quality classification models utilizing a series of machine learning algorithms. A series of data preprocessing steps were implemented on this dataset with a view to construct a generic model. This set of steps includes balancing of data using the balancing algorithm and the feature selection. Out of all these applied learning techniques, Random Forest (RF) and XGBoost with hyper parameter (HP) tuning provides the highest accuracy equal to 94.62% and 95.39%. © 2024 IEEE.

关键词： Adversarial machine learning

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The Multi-Way Path Over the Blockchain: The Development of the Security Algorithm RB-24-1 6

The Multi-Way Path Over the Blockchain: The Development of t...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Rajaprakash, S. Bagath Basha, C. Rathish Babu, T.K.S. Karthik, S. Subapriya, V. Kavitha, S. Department of Computer Science & Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science & Engineering Kommuri Pratap Reddy Institute of Technology Telagana Hyderabad India Department of Computer Science & Engineering SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai India Department of Computer Science & Engineering Aarupadai Veedu Institute of Technology Vinayaka Missions Research Foundation Tamil Nadu Chennai India Department of Computer Science & Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522667

In the current world, a lot of new technologies are becoming famous. The Block Chain is one of these technologies. A very high amount of safety is offered by the technology in question;it is extremely robust. Users are not privy to a great deal of information regarding Block Chain;however, Protecting the data that is sent in multiple ways is what its security features are for. The "SalSa and RBJ25 algorithms" were still being used by this person. We are going to talk about the new "security mechanism" we have named RB-24-1. This method is comprised of these four different techniques. The procedure begins with the "Latin Alphabet code" being assigned to T. This is the first step in the process. To proceed to the next step, use Equation (1) to perform four times the multiplication of the number by itself. "Exchanging the values of the cells" is what the third step does., and it is carried out with the help of encrypted data. Putting the "SalSa" algorithm into action within the matrix constitutes the fourth step throughout the process. Finally, the plain text is converted into the encrypted text format. This has been a long time coming. There is a higher level of security offered by the method that has been suggested in compare and contrast with the method that is being used at the moment. © 2025 IEEE.

关键词： Encryption algorithms

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CycleDRUMS: automatic drum arrangement for bass lines using CycleGAN

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Discover Artificial Intelligence 2023年第1期3卷 4页

作者： Barnabò, Giorgio Trappolini, Giovanni Lastilla, Lorenzo Campagnano, Cesare Fan, Angela Petroni, Fabio Silvestri, Fabrizio Department of Computer Engineering Sapienza University of Rome Rome Italy Department of Computer Science Sapienza University of Rome Rome Italy FAIR META London United Kingdom

The two main research threads in computer-based music generation are the construction of autonomous music-making systems and the design of computer-based environments to assist musicians. In the symbolic domain, the key problem of automatically arranging a piece of music was extensively studied, while relatively fewer systems tackled this challenge in the audio domain. In this contribution, we propose CycleDRUMS, a novel method for generating drums given a bass line. After converting the waveform of the bass into a mel-spectrogram, we can automatically generate original drums that follow the beat, sound credible, and be directly mixed with the input bass. We formulated this task as an unpaired image-to-image translation problem, and we addressed it with CycleGAN, a well-established unsupervised style transfer framework designed initially for treating images. The choice to deploy raw audio and mel-spectrograms enabled us to represent better how humans perceive music and to draw sounds for new arrangements from the vast collection of music recordings accumulated in the last century. In the absence of an objective way of evaluating the output of both generative adversarial networks and generative music systems, we further defined a possible metric for the proposed task, partially based on human (and expert) judgment. Finally, as a comparison, we replicated our results with Pix2Pix, a paired image-to-image translation network, and we showed that our approach outperforms it. © The Author(s) 2023.

关键词： Music

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A fast verifiable fully homomorphic encryption technique for secret computation on cloud data

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International Journal of Information Technology (Singapore) 2025年第4期17卷 2193-2207页

作者： Mahato, Ganesh Kumar Chakraborty, Swarnendu Kumar Department of Computer Science and Engineering National Institute of Technology Arunachal Pradesh Jote 791113 India

In the domain of cloud computing, safeguarding the confidentiality and integrity of outsourced sensitive data during computational processes is of utmost importance. This paper introduces a pioneering verifiable homomorphic encryption (VFHE) scheme, combining data encryption with the ability to verify computation integrity through homomorphic operations. The proposed approach leverages a fast lattice-based reduction key generation method, drastically reducing key generation time and resulting in a substantial reduction in key size while maintaining security. By integrating this innovation into a fully homomorphic encryption scheme, cloud data computations are expedited while preserving data security. The efficiency of the proposed scheme is highlighted through analyses of key generation time, encryption, decryption, and verification processes across varying security parameters. Verification time remains minimal, showcasing the practical applicability of our approach. Furthermore, novel metrics such as privacy cost and amortized cost are introduced to evaluate the efficiency and overhead associated with incorporating privacy into the verifiable computation scheme. The proposed VFHE scheme not only ensures data security but also substantially improves computation speed, making it a promising solution for enhancing privacy and efficiency in cloud computing applications. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Cloud security Homomorphic encryption Key generation Lattice-based encryption Secret computation Verifiable computation

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Health Data Deduplication Using Window Chunking-Signature Encryption in Cloud

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Intelligent Automation & Soft Computing 2023年第4期36卷 1079-1093页

作者： G.Neelamegam P.Marikkannu Department of Computer Science and Engineering Anna University Regional Campus CoimbatoreCoimbatore641046India

Due to the development of technology in medicine,millions of health-related data such as scanning the images are *** is a great challenge to store the data and handle a massive volume of *** data is stored in the cloud-fog storage *** cloud-Fog based health model allows the users to get health-related data from different sources,and duplicated informa-tion is also available in the ***,it requires an additional sto-rage area,increase in data acquisition time,and insecure data replication in the *** paper is proposed to eliminate the de-duplication data using a window size chunking algorithm with a biased sampling-based bloomﬁlter and provide the health data security using the Advanced Signature-Based Encryp-tion(ASE)algorithm in the Fog-Cloud Environment(WCA-BF+ASE).This WCA-BF+ASE eliminates the duplicate copy of the data and minimizes its sto-rage space and maintenance *** data is also stored in an efﬁcient and in a highly secured *** security level in the cloud storage environment Win-dows Chunking Algorithm(WSCA)has got 86.5%,two thresholds two divisors(TTTD)80%,Ordinal in Python(ORD)84.4%,Boom Filter(BF)82%,and the proposed work has got better security storage of 97%.And also,after applying the de-duplication process,the proposed method WCA-BF+ASE has required only less storage space for variousﬁle sizes of 10 KB for 200,400 MB has taken only 22 KB,and 600 MB has required 35 KB,800 MB has consumed only 38 KB,1000 MB has taken 40 KB of storage spaces.

关键词： Health data encryption chunks cloud fog deduplication bloomﬁlter Algorithm 3:Generation of Key

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