检索结果-内蒙古大学图书馆

Informatica (Slovenia) 2025年第11期49卷 165-176页

作者： Ilias, Shaik Mohammad Sharmila, V. Ceronmani Durga, V. Sathya Dept. of Computer Science and Engineering Hindustan Institute of Technology and Science Chennai India Department of Information Technology Hindustan Institute of Technology and Science Chennai India Department of Computer Science&Engineering Hindustan Institute of Technology and Science Chennai India

If adversaries were to obtain quantum computers in the future, their massive computing power would likely break existing security schemes. Since security is a continuous process, more substantial security schemes must be developed. Current PQC schemes primarily focus on data security or key exchange, and further improvement towards enhanced PQC primitives is required. Our proposal in this research is an innovative paradigm for PQC-focused cloud data security. The proposed HEDT approach achieves encryption and decryption with significantly lower latency (20% improvement) and higher reliability than AES, DES, and RSA, as demonstrated through experimental results. Furthermore, ECSIDH, a hybrid key exchange mechanism combining SIDH and ECDH, improves security strength by 50% while maintaining computational costs within 1.13x of SIDH. Compared to individual key exchange schemes like SIDH, ECSIDH offers superior security as a PQC candidate. These results confirm the robustness and efficiency of the proposed framework in ensuring secure data outsourcing and key exchange in cloud environments. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Quantum computers

来源：评论

学校读者我要写书评

暂无评论

Deep Adversarial Network Based Dental Inlay Restoration Using Point Cloud Segmenntation 3

Deep Adversarial Network Based Dental Inlay Restoration Usin...

引用

3rd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Lakshmi, S. Shanmuganathan, C. Srm Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798350369083

ISBN: (纸本)9798350369083

computer Aided Design (CAD) and dental industry are going hand-in-hand due to the increasing requirements for digitalizing the process of dental diagnosis and treatment. The crucial step in dental treatment is the identification of the individual affected teeth and gingiva region. These days 3D intraoral scanners are widely used to take the inputs for analysis and design of the tooth. High accuracy and resolution play a predominant role in providing efficient and hassle-free treatment to the patients. Since the intraoral scanners provide low quality input images in deep intraoral regions, current automated systems are facing the challenge of automated tooth labeling. The problem of segmentation of teeth and gingiva region of 3D IOS mesh scans are complex to process and produce desired output. The design of dental inlay restoration requires a fully automated end-to-end network to be developed. We propose a deep learning method called DPCNet to produce segmentation outputs that can automate the construction of a realistic surface of the defective tooth. Our goal was to start using point clouds as input to the neural network, which are obtained from mesh scans. DPCNet employs graph-based learning modules to acquire local features, which are summarized and fed into a stack of convolutional layers to achieve global features. Our approach is effective in real-time datasets, with minimal need for manual adjustment scenarios encountered. © 2024 IEEE.

关键词： Mesh generation

来源：评论

学校读者我要写书评

暂无评论

Meal Magic: An Image-Based Recipe-Generation System 8th

Meal Magic: An Image-Based Recipe-Generation System

引用

8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Sravya, Pemmasani Pariga, Swetha Swetha, S. Devi, Prasanna Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai India

ISBN: (纸本)9789819748945

The growing popularity of food photography on social media platforms has seen a surge in demand for recipe ideas and cooking inspiration. However, creating a recipe from scratch can be a daunting task, especially for someone with little cooking experience. To address this issue, our study proposes Meal Magic, a web-based image-to-recipe generator that generates recipes from the images of dishes. The image-based recipe creation system employs Inception v3, a Convolutional Neural Network (CNN) that achieves an accuracy of 78.1% when applied to the ImageNet dataset. It has a more efficient and deeper network and is computationally less expensive than the Inception v1 and v2 models. When compared to its predecessors, the model has an extremely low error rate. The web-based application is designed to be user-friendly, allowing users to simply upload an image of a dish that they wish to recreate and receive a recipe in return. As a result, it saves users’ time and streamlines the recipe creation process by eliminating the need for manual ingredient searching and recipe browsing. The image-to-recipe generator has many potential applications, including assisting home cooks in meal planning and aiding chefs in creating new recipes. Overall, this system represents a promising approach to the generation of recipes that has the potential to transform the way we approach cooking and recipe creation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Enhanced Genomic Prediction for Maize Breeding Using Deep Learning and K-mer-based Sequence Encoding 5

Enhanced Genomic Prediction for Maize Breeding Using Deep Le...

引用

5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Kudiyarasudevi, C. Suresh, S. SRM Institute of Science and Technology Department of Computer Science and Engineering Ramapuram India

ISBN: (纸本)9798331518097

Maize (Zea mays L.) plays a pivotal role in global food security due to its substantial economic and nutritional value. With advancements in agricultural technologies, crop yields have surged, but challenges remain in optimizing maize breeding for even higher productivity. This research explores cutting-edge approaches in maize breeding, focusing on novel genomic prediction tools that enhance crop yield. While numerous studies have demonstrated the potential of genomic prediction in accelerating maize yields, current models often suffer from suboptimal data quality and lengthy processing times due to the use of raw data for genotype prediction. To address these challenges, this study introduces a deep learning- based model that leverages polymorphism data, employs k-mer methods to shorten sequences, and applies one-hot encoding for preprocessing. This data is then processed by a convolutional neural network (CNN) to predict genotypes efficiently. The proposed model aims to achieve a 98% accuracy in genomic prediction, outperforming existing models in both speed and accuracy, thereby contributing to the generation of superior maize traits. © 2024 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Information Security by employing RSA Algorithm and Graph Labeling

引用

IAENG International Journal of Applied Mathematics 2024年第12期54卷 2563-2568页

作者： Vani Shree, S. Dhanalakshmi, S. Department of Mathematics SRM Institute of Science and Technology - Ramapuram Chennai600089 India Department of Mathematics Faculty of Engineering and Technology SRM Institute of Science and Technology - Ramapuram Chennai600089 India

The RSA technique provides security for every user connected across the network. The mathematical factorization problem, which demonstrates how challenging it is to find two prime numbers whose product equals a particular extremely large number, provides the foundation for this theory. In order to have a decent public key cryptosystem, it need really high integers, it can be dealt using RSA technique. In this work we have introduced a Total Edge Bimagic Mean Labeling of Graphs (TEBMML). TEBMML of a graph G is a bijection ψ: V (G) → {1, 2, 3,… |V|} such that ψ * (uv) = (Formula presented.) is either k1 or k2, a constant for any edge uv ∈ E(G). A graph is known as Total edge bimagic mean graph if it admits Total edge bimagic mean labeling. Also it is examined that Comb graph, Bistar graph, twig graph and Coconut tree graph are total edge bimagic mean graphs. Along with that information security is determined by exerting RSA algorithm with the above quoted labeling technique. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Encryption algorithms

来源：评论

学校读者我要写书评

暂无评论

Generative AI: A Transformative Force in Business Intelligence 2

Generative AI: A Transformative Force in Business Intelligen...

引用

2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Thiruneelakandan, A. Umamageswari, A. SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350327533

In an era where data is the cornerstone of strategic decision-making, Generative Artificial Intelligence (Gen AI) is revolutionizing the Business Intelligence (BI) landscape. This study investigates the multifaceted role of Generative AI in BI, encompassing data generation and augmentation, predictive analytics, reporting automation, product and service innovation, and personalization of customer experiences. The work employs a combination of synthetic data generated by Generative Adversarial Networks (GANs) and Seasonal Autoregressive Integrated Moving Average (SARIMA) predictive modeling techniques to demonstrate the potent capabilities of Generative AI in enhancing data-driven insights. The findings reveal that Generative AI not only significantly improves the accuracy of predictive models, especially in scenarios with limited historical data but also streamlines reporting processes and catalyzes innovation by uncovering latent customer needs. The research underscores the transformative impact of Generative AI in BI, while also addressing the attendant ethical considerations and the necessity for rigorous data governance. Through this exploration, the study provides a roadmap for businesses to harness the power of Generative AI, paving the way for smarter, more agile, and more personalized business strategies. © 2024 IEEE.

关键词： Data Augmentation Ethical Artificial Intelligence Generative Adversarial Networks Generative Artificial Intelligence Reporting Automation

来源：评论

学校读者我要写书评

暂无评论

Multigraph-Based Curvilinear Regression Analysis of Two Novel Class of Drugs to Treat Cardiovascular Disease using Revan Indices

引用

IAENG International Journal of Applied Mathematics 2024年第11期54卷 2159-2175页

作者： Preetha, Ugasini P. Suresh, M. Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamilnadu Kattankulathur603203 India

In this research, two distinct categories of medications for treating cardiovascular conditions, specifically fibrates and calcium channel blockers were analyzed. QSPR analysis of curvilinear regression models was used to establish a relationship between these degree-based indices and the physicochemical properties of some novel drugs used in the treatment of heart disease. The results show that quadratic regression outperformed linear regression for both drug classes. Additionally, comparing multigraph and simple graph modeling revealed that multigraph modeling provides a more detailed understanding of drug structures, enhancing the accuracy of estimating physicochemical properties. Specifically, eight Revan indices showed a strong correlation (R=1) in quadratic regression for fibrate drugs with the first Revan index (R1) serving as the most accurate estimator for molar refractivity and polarizability. Meanwhile, the Geometric-Arithmetic Revan index (GAR) emerged as the best estimator for polar surface area in quadratic regression equations for calcium channel blockers. These studies highlight the potential of topological indices in estimating physicochemical properties of drugs, which could be useful in the development of new drugs and therapies. © (2024), (International Association of Engineers). All rights reserved.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Sem-AI: A Unique Framework for Sentiment Analysis and Opinion Mining Using Social Network Data

引用

SN computer science 2025年第2期6卷 1-23页

作者： Maruthupandi, J. Sivakumar, S. Kumar, V. Senthil Srikaanth, P. Balaji Department of Computer Science and Engineering New Horizon College of Engineering Karnataka Bangalore India Department of Computer Science and Engineering SRM Institute of Science and Technology Tamilnadu Trichy India Department of Networks and Communication SRM Institute of Science and Technology Tamilnadu Chennai India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology Chennai India

Social media has become an essential forum for people to share their thoughts and sentiments owing to the quick rise in mobile technology. Business and political organizations might benefit from understanding public sentiment when making strategic decisions. Challenges in the area include handling noisy and incomplete data, variation in language use across domains, and integration into existing systems of models developed for analysis. In these ways, they can seriously hamper the accuracy and generalizability of sentiment analysis models. In view of the challenges, this work has developed a new and innovative framework of AI-based semantic analysis for assessing and classifying user views from social data. In spite of this, sentiment analysis is crucial for determining the opposing perspectives of the general public's sentiments. This work constructed a new and innovative framework called Semantic analysis based on AI (SemAI) for assessing and categorizing user views from social data. With high efficiency, a collection of intelligence algorithms in the stages of feature extraction, optimization, and sentiment prediction are applied here. The Degree of Correlation Network Model (DCNM) is used in this work to extract the subset of dependable and most-required features from social network data. The most recent meta-heuristics model, Heap based Optimization (HbO), is used to reduce the dimensionality of retrieved features. Furthermore, the unique classification algorithm, Self-Attention based Deep Analyzing Network (SA-DAN), is used in this work to precisely identify and categorize attitudes into positive, neutral, and negative classifications. This present study used a variety of most recent social corpus data to investigate and evaluate the Sem-AI model's outputs. The SemAI model achieved impressive results, with accuracy, precision, and F1-scores of 99%, 98.9%, and 99%, respectively, across various social corpus datasets. © The Author(s), under exclusive licence to S

关键词： Classification Degree of correlation network model (DCNM) Heap based optimization (HbO) Opinion mining Self-attention based deep analyzing network (SA-DAN) Semantic analysis Social data

来源：评论

学校读者我要写书评

暂无评论

Leveraging Convolutional Neural Networks for Enhanced Educational Question Classification

引用

SN computer science 2024年第8期5卷 1052页

作者： Deena, G. Ancy Breen, W. Raja, K. Shanmuganathan, C. Department of Computer Science and Engineering SRM Institute of Science and Technology Tamil Nadu Ramapuram Chennai India

Question classification is a critical task in natural language processing, as well as applications in question-answering systems and intelligent personal assistants. This study presents novel machine learning approach for automatic question classification based on Bloom’s taxonomy, a hierarchical structure of cognitive levels. Traditional methods rely on feature engineering and classic machine learning models, but these methods often lack the ability to understand complex patterns and connections in natural language data. This study proposes a novel approach using a convolutional neural network (CNN) model, which can automatically learn relevant features from input data without extensive feature engineering. The model classifies questions into pre-established categories or labels using an embedding layer, convolutional and pooling layers, and a fully linked layer for classification. We assess the model’s efficacy on a publicly accessible dataset generated by the subject expert and compare it to conventional machine learning methods like logistic regression and support vector machines. The CNN-based model outperforms conventional techniques, achieving greater recall, accuracy, precision, and F1-score. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bloom’s Taxonomy Convolutional Neural Network Machine Learning Word embedding

来源：评论

学校读者我要写书评

暂无评论

An Efficient Dilated DTCN with Residual LSTM-based Allergenicity Prediction for Chemical Compounds using Optimal Weighted Feature Selection and Improved Optimization Mechanism 2

An Efficient Dilated DTCN with Residual LSTM-based Allergeni...

引用

2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Padmavathy, T. Thenmozhi, M. Madhavan, Thirumurthy SRM Institute of Science and Technology Department of Computer Science and Engineering Chennai India SRM Institute of Science and Technology Department of Networking and Communications Chennai India SRM Institute of Science and Technology Department of Genetic Engineering Chennai India

ISBN: (纸本)9798350365269

Allergy is the sudden response of the immunity system which arises later the disclosure of allergens like chemicals, proteins and peptides. In the early times, multiple techniques were formed for allergenicity forecasting of peptides and proteins. However, there is no technique to anticipate the chemical and potential allergenic. The standard food allergen recognition mainly depends on vitro and vivo research, which frequently needs more time and is expensive. Artificial Intelligence (AI) operates a quick food allergen detection model, which solves the above-defined problems and becomes an effective auxiliary tool. Evaluation of potential allergenicity of protein is required at any movement of transgenic proteins implemented in the food chain. Therefore, it is necessary to solve more complications involved in the conventional allergenicity prediction technique for chemical compounds. Thus, a new allergenicity prediction framework for chemical compounds is designed using deep learning techniques. Initially, essential allergenicity data are accumulated from the standard measures. Next, the acquired data are presented for the optimal weighted feature selection stage. In this phase, the weights and features are selected optimally using Opposition Tasmanian Devil Optimization (OTDO). Further, the adopted optimal weighted features are offered to the allergenicity forecasting stage. Here, the allergenicity is predicated on using the developed "Dilated Deep Temporal Context Networks with Residual Long Short-Term Memory Networks (DiDNet-ResLSTM)". Hence, the recommended allergenicity prediction model effectively higher performance rate in different experimental observations. © 2024 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：