检索结果-内蒙古大学图书馆

Towards a smarter education system: an investigation into ML and DL for information retrieval

Multimedia Tools and Applications 2025年 1-34页

作者： Sengupta, Subhabrata Chakrabarti, Satyajit University of Engineering and Management Kolkata India Faculty of Computer Science Institute of Engineering & Management Kolkata India

Artificial Intelligence (AI) is a transformative technology that can embed intelligence and human-like behavior into systems and devices. To develop smart, intelligent, and automated systems that address current needs, it is essential to employ AI-based models. This paper explores various components of AI—visual, textual, interactive, functional, and analytical—that enhance the capabilities and intelligence of applications to solve real-world problems. Given the ever-changing and diverse nature of real-life issues and data, creating effective AI models presents significant challenges. This study examines how Machine Learning (ML) and Deep Learning (DL) techniques can be leveraged for Information Retrieval (IR) within the context of a smarter education system. It provides an overarching view of AI-based modeling and its potential to create intelligent systems in fields such as cybersecurity, smart cities, agriculture, healthcare, finance, and business. Furthermore, the paper discusses research challenges related to AI modeling and offers insights for decision-makers in various application fields on employing ML and DL to enhance educational systems. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Bengali reduplication generation with finite-state transducers (FSTs)

引用

International Journal of Speech Technology 2024年第3期27卷 729-737页

作者： Barman, Abhijit Saha, Diganta Pal, Alok Ranjan Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Department of Computer Science and Engineering College of Engineering and Management Kolaghat721171 India

Reduplication is a highly productive process in Bengali word formation, with significant implications for various natural language processing (NLP) applications, such as parts-of-speech tagging and sentiment analysis. Despite its importance, this area has not been extensively explored in computational linguistics, especially for low-resource languages like Bengali. This study first demonstrates that a two-way finite-state transducer (FST) can effectively capture complete reduplication generation processes in Bengali. Second, it is shown that the formation of partial reduplication requires a set of 2-way FSTs due to the diverse patterns involved in Bengali partial reduplications. Third, the research highlights the utility of the reduplication generation process in identifying Bengali reduplication instances, achieving a commendable F1-Score of 88.11%. This method outperforms current state-of-the-art methods for identifying reduplicated expressions in Bengali text. This research contributes valuable insights into the computational representation of reduplication in Bengali, offering potential enhancements for NLP tasks in low-resource language scenarios. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Computational linguistics

来源：评论

学校读者我要写书评

暂无评论

Detection of Brain Tumor Using U-Net Model Approach

Detection of Brain Tumor Using U-Net Model Approach

引用

International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Patra, Arunima Paul, Krishnangshu Paul, Prithwineel Department of Computer Science and Engineering Institute of Engineering and Management Kolkata India Department of Computer Science and Engineering University of Engineering and Management Kolkata India Department of Computer Science and Engineering Centre of Excellence for Quantum Computing Institute of Engineering & Management University of Engineering and Management Kolkata India

ISBN: (纸本)9789819613472

This study introduces an innovative deep learning methodology leveraging the U-Net framework for medical image segmentation and lesion detection in brain tumors. U-net architecture contains encoder and decoder blocks which in turn are capable of capturing and propagation of information of the features. In this paper, for brain tumor segmentation, a U-Net Convolutional Neural Network (CNN) is employed, utilizing multimodal magnetic resonance imaging (MRI) data and skip connections for precise tumor localization and segmentation. Through comprehensive experiments, we show that our method outperforms traditional techniques and current CNN models for medical image segmentation and lesion detection in brain tumors. Moreover, the proposed method can perform the task with higher accuracy. This tailored U-Net architecture demonstrates the potential for advancing medical image analysis, enabling precise segmentation and lesion detection, ultimately leading to improved patient care and treatment outcomes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Once upon a Time, in a computer engineering Department ….

引用

SIGMOD Record 2024年第4期52卷 36-38页

作者： Catarci, Tiziana Department of Computer Control and Management Engineering Sapienza University of Roma Italy

When you decide to pursue a career in the academy, you may encounter many difficulties, obstacles, and moments of discouragement, but also have great satisfaction, exaltation, and a lot of fun. By the time you get to the mid-career period, many doubts and problems are behind you (if you are still in the academy and did not give up) and a less stressful time begins with even more freedom to study, create your own group, and choose your own ventures. This is also the time when you may decide to impact the society, not just work for yourself, but do it to contribute to the creation of a better world and be a positive example for the younger generation. © 2024 Association for Computing Machinery. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

An improved multi-scale convolutional neural network with gated recurrent neural network model for protein secondary structure prediction

引用

Neural Computing and Applications 2024年第24期36卷 15063-15074页

作者： Bongirwar, Vrushali Mokhade, A.S. Department of Computer Science and Engineering Shri Ramdeobaba College of Engineering and Management Nagpur India Department of Computer Science and Engineering Visvesvaraya National Institute of Technology Nagpur India

Protein structure prediction is one of the main research areas in the field of Bio-informatics. The importance of proteins in drug design attracts researchers for finding the accurate tertiary structure of the protein which is dependent on its secondary structure. In this paper, we focus on improving the accuracy of protein secondary structure prediction. To do so, a Multi-scale convolutional neural network with a Gated recurrent neural network (MCNN-GRNN) is proposed. The novel amino acid encoding method along with layered convolutional neural network and Gated recurrent neural network blocks helps to retrieve local and global relationships between features, which in turn effectively classify the input protein sequence into 3 and 8 states. We have evaluated our algorithm on CullPDB, CB513, PDB25, CASP10, CASP11, CASP12, CASP13, and CASP14 datasets. We have compared our algorithm with different state-of-the-art algorithms like DCNN-SS, DCRNN, MUFOLD-SS, DLBLS_SS, and CGAN-PSSP. The Q3 accuracy of the proposed algorithm is 82–87% and Q8 accuracy is 69–77% on different datasets. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

Nonlinearity in Large-Cycle Cellular Automata

引用

Complex Systems 2025年第1期34卷 129-160页

作者： Mukherjee, Sukanya Adak, Sumit Department of Computer Science and Engineering Institute of Engineering and Management Kolkata University of Engineering and Management West Bengal Kolkata700091 India Department of Applied Mathematics and Computer Science DTU Compute Technical University of Denmark Kgs. Lyngby2800 Denmark

The class of maximal-length cellular automata (CAs) has gained significant attention over the last few years due to the fact that it can generate cycles with the longest possible lengths. For every l of the form l = 2n-1 where n ∈ , a cycle of length l can be generated by a cellular automaton (CA) of size ⌊log2 l⌋ + 1;this maximal-length CA is in fact the optimal (minimum-sized) CA to generate a cycle of length l. However, maximal-length CAs are, in general, linear and have some drawbacks. Moreover, it is nontrivial to address this problem for any l ∈ , that is, removing the constraint that l is of the form 2n-1. In this paper we therefore employ nonlinearity in the rules and aim to generate the near-optimal CA for any given cycle length l, meaning that the generated CAs will be of size n = (⌊log2 l⌋ + 1) + d, where the displacement d is as small as possible. We use a generic operator to concatenate a collection of component CAs of sizes n1, n2, …, nk by employing an evolutionary strategy. Here, n = n1 + n2 + ⋯ + nk and l ≥ lcm(1, 2, …, k ), where i (1 ≤ i ≤ k) is the maximum cycle length of a CA of size ni . An influencing factor in the design of component CAs is to achieve the near-optimal CA for a given cycle length. Here we consider nonlinear reversible large-cycle CAs as component CAs. We also measure the effectiveness of nonlinearity in large-cycle generation. © 2025, Complex Systems Publications, Inc. All rights reserved.

关键词： CAs cellular automata evolution large-cycle CA neighborhood dependent nonlinearity reversible CAs

来源：评论

学校读者我要写书评

暂无评论

Requirement Classification using Deep Learning and Nature-Inspired Optimization Technique

Informatica (Slovenia)

引用

Informatica (Slovenia) 2025年第6期49卷 117-146页

作者： Sonawane, Sonal Puthran, Shubha Department of Computer Engineering Mukesh Patel School of Technology Management & Engineering SVKM’s NMIMS Mumbai400056 India

Recent advancements in requirements engineering (RE) methods have increasingly leveraged machine learning (ML) algorithms to address challenging RE issues such as the identification and classification of software requirements in requirement documents. Classifying software requirements accurately and efficiently is crucial for the success of the project. Functional and nonfunctional requirements define the core attributes and constraints of a system. When requirements are documented in natural language, they often introduce uncertainties such as ambiguity, inconsistency, or poor readability making the process challenging. Further, manual extraction of requirements is tideous and error prone as it requires precise interpretation, thereby mitigating the risks of miscommunication and errors in the development *** address these challenges, it is vital to employ Natural Language Processing (NLP)techniques to enhance the clarity of requirements. This paper proposes a method that combines deep learning models with NLP techniques, supplemented by Flower Pollination Optimizer (FPO) algorithm, to automate the classification of requirements. The methodology leverages NLP to extract significant features for training a Convolutional Neural Network (CNN) model. CNN model is enhanced using the FPO algorithm to ensure better convergence. The performance of the proposed CNN-FPO framework is evaluated using an industry SmartNet dataset, with results indicating that the accuracy of the classification can reach up to 98% as compared to other traditional machine learning(ML) *** integration of NLP with an optimized CNN model creates a robust framework for requirement classification, addressing the challenges posed by natural language documentation in requirements engineering. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Isomorphic Classification of First Degree Cellular Automata

引用

Complex Systems 2025年第1期34卷 59-90页

作者： Vikrant, Vicky Bhattacharjee, Kamalika Mukherjee, Sukanya Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirappalli620015 India Department of Computer Science and Engineering Institute of Engineering and Management West Bengal Kolkata700091 India

This paper explores the concept of isomorphism in cellular automata (CAs), focusing on identifying and understanding isomorphic relationships between distinct CAs. A cellular automaton (CA) is said to be isomorphic to another CA if the configuration transition diagrams of the two CAs are identical. To illustrate how isomorphism entails the preservation of structural properties while allowing for variations in state representations, we exhaustively explore the isomorphic reversible rules in the first degree CAs (FDCAs), that is, the simple three-neighborhood CAs represented by eight parameters. The experimental study includes reversible FDCAs for states two to 10 with cell lengths of four to seven under the null boundary condition and states two to seven with cell lengths of four to seven under the periodic boundary condition. Given a CA, we also provide two synthesis methods for identifying the set of CAs that are isomorphic to it. The first method operates by permuting the states of a group of CA cell locations, whereas the second method involves exchanging cells at various points. We demonstrate that, depending on the range of exchanged cell positions, the isomorphic CAs produced in this way may have a bigger neighborhood than the input CA. Finally, we provide an exhaustive classification of the elementary CAs (ECAs) using the first synthesis method and compare it with existing results. © 2025, Complex Systems Publications, Inc. All rights reserved.

关键词： classification cycle structure FDCA first degree cellular automaton parameters reversibility synthesis schemes

来源：评论

学校读者我要写书评

暂无评论

Investigation of sustainable strategies with metaheuristic algorithm on surface roughness, cutting temperature, and chip morphology during machining of Ti6Al4V alloy

引用

International Journal of Machining and Machinability of Materials 2024年第3期26卷 223-243页

作者： Misra, Sutanu Kumar, Yogesh Paul, Goutam Pradhan, Buddhadeb Department of Mechanical Engineering University of Engineering and Management Kolkata India Department of Mechanical Engineering National Institute of Technology Patna Bihar Patna India Department of Computer Science and Engineering University of Engineering and Management Kolkata India

This research work explores the effects of dry, liquid N2-based cryogenic cooling and cryogenic plus MQL hybrid strategy on surface roughness, rake surface temperature, principal cutting-edge temperature, auxiliary cutting-edge temperature, and chip morphology to understand the machinability of Ti-6Al-4V alloy using sustainable manufacturing techniques. Here, a metaheuristic algorithm approach has been introduced to determine the experimental data’s accuracy, which is approximately 75% for further investigation. Logistic regression and support vector machine provided higher accuracy and best fitted with the dataset and problem formulation. During this work, it has been observed that chip morphology is influenced by cutting parameters and lubrication strategy. The cryogenic plus MQL hybrid approach is the best method over cryogenic cooling by decreasing the heat by 12% and giving 8% better surface quality. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Titanium alloys

来源：评论

学校读者我要写书评

暂无评论

Designing of Smart Pothole Detection System 9

Designing of Smart Pothole Detection System

引用

9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Maji, Anushka Sinha, Sagnik Banerjee, Subhabrata Das, Amit Kumar Gangopadhyay, Malay Rateria, Roshan Institute of Engineering & Management Department of Computer Science and Engineering Kolkata India Institute of Engineering & Management University of Engineering and Management Department of Electronics & Communication Engineering Kolkata India Institute of Engineering & Management University of Engineering and Management Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350377972

Recently, Artificial Intelligence (AI) algorithms have been used to detect the potholes. In this article, a machine learning algorithm has been designed to solve the problem efficiently. A software model has also been created for drivers, enabling them to detect and recognize potholes in advance through a mobile application. This mobile app utilizes various sensors to detect potholes and provides timely alerts to the driver. Moreover, the system is capable of responding to newly detected potholes and alerts the driver about them. The proactive notification system improves road safety by ensuring drivers are informed about upcoming road hazards. © 2024 IEEE.

关键词： Mobile applications

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：