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2024 IEEE International Conference on Recent Advances in science and engineering technology, ICRASET 2024

作者： Rekha, V. Sharmila, N. Komala, M. Pavithra, G.S. Mallikarjunaswamy, S. Naveen, K.B. Shilpa, M. Sheela, S. Department of Computer Science and Engineering Bangalore India Jss Science and Technology University Dept of Electronics and Electrical Engineering Mysuru India Sjb Institute of Technology Dept of Electronics and Communication Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Computer Science and Engineering Bengaluru India Jss Academy of Technical Eduction Dept of Electronics and Communication Engineering Bengaluru India Bgs Institute of Technology Adichunchanagiri University Dept of Electronics and Electrical Engineering BG Nagara India Bengaluru Institute of Technology Dept of Information Science and Engineering Bengaluru India Global Academy of Technology Dept of Computer Sceinec and Engineering Bengaluru India

ISBN: (纸本)9798350388602

The integration of renewable energy sources into smart grids requires advanced management systems to optimize energy production, storage, and distribution. Conventional methods like Static Energy Allocation (SEA) and Rule-Based Control (RBC) struggle with real-time adaptability, leading to inefficiencies in handling the variable nature of renewable energy. SEA and RBC lack the predictive capabilities needed to effectively manage dynamic energy generation, resulting in increased energy losses and operational *** paper proposes a Hybrid Edge-Cloud Renewable Energy Management System (HECREMS) using Deep Learning with Predictive Analytics (DLPA). HECREMS integrates edge computing for real-time data processing with cloud computing for enhanced predictive analytics, providing a robust framework for efficient energy management. The proposed system leverages the low latency of edge devices and the computational power of the cloud to improve energy forecasting and decision-making. HECREMS outperforms traditional SEA and RBC methods, achieving a 0.25% improvement in energy utilization efficiency, a 0.20% reduction in operational costs, and a 0.30% increase in system responsiveness. By addressing the drawbacks of conventional approaches, HECREMS demonstrates significant advancements in the efficient and sustainable management of renewable energy within smart grids. © 2024 IEEE.

关键词： Cloud analytics

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Explainable AI for Enhanced Intrusion Detection Systems 3

Explainable AI for Enhanced Intrusion Detection Systems

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Ananya, G. Siri, N. Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science and Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Electronic and Communication Engineering Karnataka Bengaluru India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bengaluru India

ISBN: (纸本)9798350354171

As everything is turning digital there is a hike in the invasion of digital space, because of which advanced tools are needed. One such tool is the Intrusion Detection System (IDS). In this work, we have tried to detect and classify suspicious connections accurately. This is done by incorporating advanced computing into Intrusion Detection Systems. To achieve this, we have used the Coburg Intrusion Detection Dataset. To achieve this, we used the Coburg intrusion detection dataset. We investigate the effectiveness of Single Classifiers and Bagging and Boosting in this context. We proposed a four-stage Ensemble model to classify the connections using its attributes. The proposed model uses four Single Classification models: Logistic Regression, KNN, Decision Tree, and SVM. Before applying the algorithms, the data was Preprocessed. Later, the models were hyper-tuned to increase the accuracy. The results showed that Random Forest outperforms other single classifiers and machine learning algorithms, with a mean accuracy of 99.99%, a precision of 99.99%, and an F1-score of 99.99%. Furthermore, as models are usually hard to understand by using Explainable AI we can increase the transparency of the model. Explainable AI techniques are used to make the advanced computing models reliable and more understandable. This study aims to develop advanced and dependable intrusion detection systems. © 2024 IEEE.

关键词： Network intrusion

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DLMNN Based Heart Disease Prediction with PD-SS Optimization Algorithm

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Intelligent Automation & Soft Computing 2023年第2期35卷 1353-1368页

作者： S.Raghavendra Vasudev Parvati R.Manjula Ashok Kumar Nanda Ruby Singh D.Lakshmi S.Velmurugan Department of Information and Communication Technology Manipal Institute of TechnologyManipal Academy of Higher EducationManipal576104India Department of Information Science and Engineering SDM College of Engineering and TechnologyDharwad580002India Department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyKattankulathurChennai603203India Department of Computer Science and Engineering B.V Raju Institute of TechnologyTelangana502313India Faculty of Engineering and Technology SRM Institute of Science and TechnologyNCR CampusUttar Prasesh201204India School of Computing Science and Engineering VIT Bhopal UniversityMadhya Pradesh466114India Department of Computer Science and Engineering Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India

In contemporary medicine,cardiovascular disease is a major public health *** diseases are one of the leading causes of death *** are classiﬁed as vascular,ischemic,or *** information contained in patients’Electronic Health Records(EHR)enables clin-icians to identify and monitor heart *** failure rates have risen drama-tically in recent years as a result of changes in modern *** diseases are becoming more prevalent in today’s medical *** year,a substantial number of people die as a result of cardiac *** primary cause of these deaths is the improper use of pharmaceuticals without the supervision of a physician and the late detection of *** improve the efﬁciency of the classiﬁcation algo-rithms,we construct a data pre-processing stage using feature ***-ments using unidirectional and bidirectional neural network models found that a Deep Learning Modiﬁed Neural Network(DLMNN)model combined with the Pet Dog-Smell Sensing(PD-SS)algorithm predicted the highest classiﬁcation performance on the UCI Machine Learning Heart Disease *** DLMNN-based PDSS achieved an accuracy of 94.21%,an F-score of 92.38%,a recall of 94.62%,and a precision of 93.86%.These results are competitive and promising for a heart disease *** demonstrated that a DLMNN framework based on deep models may be used to solve the categorization problem for an unbalanced heart disease *** proposed approach can result in exceptionally accurate models that can be utilized to analyze and diagnose clinical real-world data.

关键词： Machine learning random forest coronary heart disease cardiovascular optimization algorithm

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MaGIC: Multi-modality Guided Image Completion 12

MaGIC: Multi-modality Guided Image Completion

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12th International Conference on Learning Representations, ICLR 2024

作者： Wang, Hao Yu, Yongsheng Luo, Tiejian Fan, Heng Zhang, Libo School of Computer Science and Technology University of Chinese Academy of Sciences China Institute of Software Chinese Academy of Sciences China Department of Computer Science University of Rochester United States Department of Computer Science and Engineering University of North Texas United States

Vanilla image completion approaches exhibit sensitivity to large missing regions, attributed to the limited availability of reference information for plausible generation. To mitigate this, existing methods incorporate the extra cue as a guidance for image completion. Despite improvements, these approaches are often restricted to employing a single modality (e.g., segmentation or sketch maps), which lacks scalability in leveraging multi-modality for more plausible completion. In this paper, we propose a novel, simple yet effective method for Multi-modal Guided Image Completion, dubbed MaGIC, which not only supports a wide range of single modality as the guidance (e.g., text, canny edge, sketch, segmentation, depth, and pose), but also adapts to arbitrarily customized combination of these modalities (i.e., arbitrary multi-modality) for image completion. For building MaGIC, we first introduce a modality-specific conditional U-Net (MCU-Net) that injects single-modal signals into a U-Net denoiser for single-modal guided image completion. Then, we devise a consistent modality blending (CMB) method to leverage modality signals encoded in multiple learned MCU-Nets through gradient guidance in latent space. Our CMB is training-free, thereby avoiding the cumbersome joint re-training of different modalities, which is the secret of MaGIC to achieve exceptional flexibility in accommodating new modalities for completion. Experiments show the superiority of MaGIC over state-of-the-art methods and its generalization to various completion tasks. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Image segmentation

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Ensemble Attention Guided Multi-SEANet Trained with Curriculum Learning for Noninvasive Prediction of Gleason Grade Groups from MRI

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Journal of Shanghai Jiaotong university(science) 2024年第1期29卷 109-119页

作者：沈傲胡冀苏金鹏飞周志勇钱旭升郑毅包婕王希明戴亚康 School of Computer Science and Technology Changchun University of Science and TechnologyChangchun 130022China Suzhou Institute of Biomedical Engineering and Technology Chinese Academy of SciencesSuzhou 215163JiangsuChina School of Biomedical Engineering(SooChow) University of Science and Technology of ChinaSuzhou 215163JiangsuChina Department of Radiology The First Afiliated Hospital of Soochow UniversitySuzhou 215006JiangsuChina

The Gleason grade group(GG)is an important basis for assessing the malignancy of prostate can-cer,but it requires invasive biopsy to obtain *** noninvasively evaluate GG,an automatic prediction method is proposed based on multi-scale convolutional neural network of the ensemble attention module trained with curriculum ***,a lesion-attention map based on the image of the region of interest is proposed in combination with the bottleneck attention module to make the network more focus on the lesion ***,the feature pyramid network is combined to make the network better learn the multi-scale information of the lesion ***,in the network training,a curriculum based on the consistency gap between the visual evaluation and the pathological grade is proposed,which further improves the prediction performance of the ***-perimental results show that the proposed method is better than the traditional network model in predicting GG *** quadratic weighted Kappa is 0.4711 and the positive predictive value for predicting clinically significant cancer is 0.9369.

关键词： prostate cancer Gleason grade groups(GGs) bi-parametric magnetic resonance imaging deep learn-ing curriculum learning

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Grocery Purchase through Blockchain System 7

Grocery Purchase through Blockchain System

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Kumar, Boddu Manoj Rajeswari, G. Thanush, B.V. Krishna, Vamsi Sree, E. Divija Balakrishnan, Arunkumar Sathyabama Institute of Science and Technology School of Computer Science Engineering with Blockchain Technology Tamil Nadu Chennai600119 India Sathyabama Institute of Science and Technology School of Computer Science Engineering Tamil Nadu Chennai600119 India VIT-AP University School of Computer Science Engineering India Crm Institute of Technology School of Computer Science Engineering Karnataka Bengaluru India Sree Vidyanikethan Engineering College Rangampeta School of Computer Science Engineering with Artificial Intelligence Tirupati517102 India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Information Technology Manipal India

ISBN: (纸本)9798331527822

The integration of blockchain technology into the grocery purchasing process offers a transformative approach to enhancing transparency, security, and efficiency. This paper presents a comprehensive framework for a blockchain-based grocery system, detailing each step from user registration and identity verification to product listing, smart contract creation, and real-time inventory management. Consumers can place orders with confidence, knowing that payments are securely processed and supply chain logistics are fully transparent. The use of IoT devices ensures quality control during transportation, while smart contracts automate delivery and receipt confirmation. Additionally, the system supports consumer feedback and dispute resolution through immutable records and predefined arbitration mechanisms. This innovative application of blockchain technology aims to revolutionize the grocery sector, fostering trust and accountability among all stakeholders involved. Future research will explore the potential for AI integration and address scalability and interoperability challenges. © 2024 IEEE.

关键词： Smart contract

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Visual Computing Unified Application Using Deep Learning and computer Vision Techniques

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第1期18卷 59-74页

作者： Sowmya, B.J. Meeradevi Seema, S. Dayananda, P. Supreeth, S. Shruthi, G. Rohith, S. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Karnataka Bengaluru India Department of Artificial Intelligence and Machine Learning Ramaiah Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering M S Ramaiah Institute of Technology Karnataka Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal India School of Computer Science and Engineering REVA University Karnataka Bengaluru India Department of Electronics & Communication Engineering Nagarjuna College of Engineering & Technology Karnataka Bengaluru India

Vision Studio aims to utilize a diverse range of modern deep learning and computer vision principles and techniques to provide a broad array of functionalities in image and video processing. Deep learning is a distinct class of machine learning algorithms that utilize multiple layers to gradually extract more advanced features from raw input. This is beneficial when using a matrix as input for pixels in a photo or frames in a video. computer vision is a field of artificial intelligence that teaches computers to interpret and comprehend the visual domain. The main functions implemented include deepfake creation, digital ageing (de-ageing), image animation, and deepfake detection. Deepfake creation allows users to utilize deep learning methods, particularly autoencoders, to overlay source images onto a target video. This creates a video of the source person imitating or saying things that the target person does. Digital aging utilizes generative adversarial networks (GANs) to digitally simulate the aging process of an individual. Image animation utilizes first-order motion models to create highly realistic animations from a source image and driving video. Deepfake detection is achieved by using advanced and highly efficient convolutional neural networks (CNNs), primarily employing the EfficientNet family of models. © 2024 by the authors of this article. Published under CC-BY.

关键词： Animation

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WPIML: Web Page Indexing Using Heterogeneous Multi-source Knowledge and Reasoning by Fact Driven Learning 15th

WPIML: Web Page Indexing Using Heterogeneous Multi-source Kn...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Jayanth, M.K.V. Deepak, Gerard Santhanavijayan, A. Department of Computer Science and Engineering JNTU GV College of Engineering Vizianagaram Vizianagaram India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli Tiruchirappalli India

ISBN: (纸本)9783031889912

The epoch of Web 3.0, there arises a necessity for a strategic framework that focuses on a knowledge-centric strategy for indexing web pages. Existing web page indexing frameworks predominantly rely on either keywords or clustering mechanisms. The current demand is for a knowledge-centric model that incorporates learning and reasoning. This paper introduces a statistical model for web page indexing, which not only involves various sources of knowledge but also contributes significantly to web page indexing. The framework's operational process involves the extraction of URLs and descriptions from the dataset. Furthermore, keywords from the URLs are extracted, and an RDF generation paradigm is implemented. This is coupled with ontology generation and knowledge incorporation, utilizing knowledge stores like Wikidata and YAGO. The substantial augmentation of knowledge is accomplished through an ingenious metadata generation strategy. To categorize the metadata, a deep learning Convolutional Neural Network (CNN) classifier is integrated. Additionally, a XGBoost classifier is employed for web page indexing, with the selection of features being driven by Shannon's entropy mechanism. The model facilitates strategic reasoning via normalized Google distance and Renkonen index, employing differential ratios of step deviance measures. The optimization prowess of the artificial algae algorithm is harnessed for effective solutions. This optimization aids in achieving the finest instances for web page indexing, thereby narrowing the cognitive gap between the existing worldwide web and the incorporated knowledge of Web 3.0. The proposed framework's performance is remarkable, boasting an average precision of 96.45%, an average recall of 97.19%, and an impressive F-measure of 387.27%. The framework also achieves the lowest value of False Discovery Rate (FDR) at a mere 0.04%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Indexing (materials working)

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SemWIRet: A Semantically Inclined Strategy for Web Image Recommendation Using Hybrid Intelligence 5th

SemWIRet: A Semantically Inclined Strategy for Web Image Rec...

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5th International Conference on Recent Innovations in Computing , ICRIC 2022

作者： Bobde, M. Yash Deepak, Gerard Santhanavijayan, A. Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Engineering National Institute of Technology Tiruchirappalli India

ISBN: (纸本)9789819906000

With a rapid increase of Websites, retrieving information from the Internet has become a very challenging and time-consuming endeavour. There is always room for improvement in terms of yielding and recommending the correct informative measures so that the user would get a very pertinent topic for which the user is searching over the Internet. To ease the work of the user, an enhanced recommendation system is best suited to recommend the optimal solution. Furthermore, relevant articles are displayed according to the user queries. It is highly likely that the relevant search article recommended will be of no use to the user if the recommendation algorithm is not effective. This paper proposes a semantic-infused hybrid intelligence approach to recommendation for user-centric queries relevant using concept and ANOVA cosine similarity and extreme gradient boosting (XGBoost) where the documents are classified. Using the flickr30k dataset, the above experiment was conducted, and 97.16% accuracy was achieved. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Semantics

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Development of Deepfake Detection Techniques for Protecting Multimedia Information using Deep Learning 3

Development of Deepfake Detection Techniques for Protecting ...

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3rd International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2024

作者： Siva Rama Lingham, N. Devakanth, J Jude Moses Anto Raj, Gowtham Gayathri, K. Janani, R. Dhanapal, R. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Acharya Institute of Technology Department of Computer Science and Engineering Bangalore India Bannari Amman Institute of Technolgy Department of Information Technology Erode India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350375190

The development of proposed deepfake detection techniques plays a prominent role in the safeguarding the multimedia information. The various challenges are addressed using the aid of deep learning techniques. This involves hybrid optimization techniques such as particle swarm optimization and genetic algorithms. This helps to improve the accuracy and efficiency in the detection of deepfake. PSO helps in optimizing the weights and parameters of the neural network to obtain faster convergence. GA helps in obtaining potential solutions to obtain the robust deepfake detection model. The integration of deep learning with hybrid optimization involves the collection and preprocessing of the multimedia information and proceeds to an optimization algorithm. The manipulated content in the online platform are detected during the training process. The hybrid optimization tcehniques helps in obtaining model generability and achieving resilience in various attacks occurring in the deepfake generation. The integration of PSO and GA helps in the accurate detection of deepfake content through continuous training and evaluation. Thus the proposed system forms a safeguarding tool for multimedia content and helps to eliminate various risks. © 2024 IEEE.

关键词： Genetic algorithms

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