检索结果-内蒙古大学图书馆

2024 International Conference on IT and Industrial Technologies, ICIT 2024

作者： Azam, Hadiqa Chaudhry, Hassan Nazeer Majid, Muhammad Computer Department University of Engineering and Technology Taxila Pakistan Computer Science Department Barani Institute of Information Technology Rawalpindi Pakistan

ISBN: (纸本)9798331504465

Reviews and ratings significantly impact businesses, either boosting their reputation or harming competitors. To manipulate these outcomes, some businesses falsify product reviews. This paper introduces a transformer-based model that utilizes GloVe (Global Vectors for Word Representation) semantic embeddings to detect fraudulent reviews. Our approach captures subtle linguistic patterns indicative of fake reviews by fine-tuning a pre-trained GloVe model on a labelled dataset. The GloVe embeddings improve the model’s grasp of word semantics, while an attention-based neural network classifies reviews as genuine or fraudulent. Our model has surpassed traditional machine learning methods, achieving 98% accuracy across various datasets. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

An Improved Multi-Objective Particle Swarm Optimization Routing on MANET

引用

computer Systems science & engineering 2023年第2期44卷 1187-1200页

作者： G.Rajeshkumar M.Vinoth Kumar K.Sailaja Kumar Surbhi Bhatia Arwa Mashat Pankaj Dadheech Department of Computer Science and Engineering Erode Sengunthar Engineering CollegePerunduraiErode638057Tamil NaduIndia Department of Information Science and Engineering Dayananda Sagar Academy of Technology and ManagementBangalore560082India Department of Master of Computer Applications M.S.Ramaiah Institute of TechnologyBangalore560054India Department of Information Systems College of Computer Science and Information TechnologyKing Faisal UniversityRiyadh11533Saudi Arabia Faculty of Computing and Information Technology King Abdulaziz UniversityRabigh21911Saudi Arabia Department of Computer Science and Engineering Swami Keshvanand Institute of TechnologyManagement&GramothanJaipur302017RajasthanIndia

A Mobile Ad hoc Network(MANET)is a group of low-power con-sumption of wireless mobile nodes that conﬁgure a wireless network without the assistance of any existing infrastructure/centralized *** primary aim of MANETs is to extendﬂexibility into the self-directed,mobile,and wireless domain,in which a cluster of autonomous nodes forms a MANET routing *** Intrusion Detection System(IDS)is a tool that examines a network for mal-icious behavior/policy violations.A network monitoring system is often used to report/gather any suspicious attacks/*** IDS is a software program or hardware system that monitors network/security trafﬁc for malicious attacks,sending out alerts whenever it detects malicious *** impact of Dynamic Source Routing(DSR)in MANETs challenging blackhole attack is investigated in this research *** Cluster Trust Adaptive Acknowledgement(CTAA)method is used to identify unauthorised and malfunctioning nodes in a MANET *** system is active and provides successful delivery of a data packet,which implements Kalman Filters(KF)to anticipate node ***,KF is used to eliminate synchronisation errors that arise during the sending and receiving *** order to provide an energy-efﬁcient solution and to minimize network trafﬁc,route optimization in MANET by using Multi-Objective Particle Swarm Optimization(MOPSO)technique to determine the optimal num-ber of clustered MANET along with energy dissipation in *** to the researchﬁndings,the proposed CTAA-MPSO achieves a Packet Delivery Ratio(PDR)of 3.3%.In MANET,the PDR of CTAA-MPSO improves CTAA-PSO by 3.5%at 30%malware.

关键词： MANET intrusion detection system cluster kalmanﬁlter dynamic source routing multi-objective particle swarm optimization packet delivery ratio

来源：评论

学校读者我要写书评

暂无评论

IVIDNet: Intelligent iris vitality detection via weighted prediction score level fusion

引用

Multimedia Tools and Applications 2023年第29期82卷 45959-45981页

作者： Verma, Palak Selwal, Arvind Sharma, Deepika Department of Computer Science and Engineering Indian Institute of Technology Jammu PO-Nagrota Jammu181221 India Department of Computer Science and Information Technology Central University of Jammu Samba181143 India

Iris recognition is one of the most widely used human authentication mechanism that has gained huge popularity due to its security and efficiency. However, recently attackers have breached the security of these systems via synthetically generated fake iris traits using a variety of artefacts such as paper printouts, video attacks, and cosmetic lenses. To countermeasure these attacks, an anti-deception sub-system is instilled between sensor and feature extractor modules of the system. These sub-systems intelligently measure the vitality of the presented iris traits and are popularly known as iris vitality detection (IVID) or iris anti-spoofing techniques. Recently, the deep learning-based models are widely deployed for iris anti-spoofing approaches, but these methods result in additional training overhead as well as require larger training dataset. In this paper, we expound a novel iris vitality detection network (IVIDNet) that employs the robust features of two pre-trained deep convolutional neural networks (DCNN). The extracted features are then integrated via a new weighted score-level fusion technique. To demonstrates the efficacy of the proposed technique, experimental analysis is carried out on benchmark iris liveness detection Notre Dame 2017 and Notre Dame cosmetic lenses dataset (NDCLD) 2015. The proposed IVIDNet exhibits excellent performance in the known attack scenario with an average classification error rate (ACER) of 0.60%. Moreover, IVIDNet shows superior performance in the unseen attacks scenario (i.e. cross-database) with an ACER of 9.72%. In overall, the proposed model outperforms other similar iris anti-spoofing approaches and efficient in terms of computational overhead. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Biometrics

来源：评论

学校读者我要写书评

暂无评论

Enhancing Elephant Emotion Detection Using YOLOv5, YOLOv8, and YOLOv9: A Study on Performance and Explainability

Enhancing Elephant Emotion Detection Using YOLOv5, YOLOv8, a...

引用

2024 IEEE Silchar Subsection Annual Conference, SILCON 2024

作者： Subair, Rubiya Benedict, Shajulin Indian Institute of Information Technology Kottayam Department of Computer Science and Engineering Kottayam India

ISBN: (纸本)9798331540821

Elephant emotion detection has marked a crucial component owing to the increased casualties due to human-elephant conflicts. The difficulty has increased due to the lack of algorithms that quickly identify emotions with explainable features. This paper proposes a holistic architecture that detects the emotions of elephants using YOLOv5, YOLOv8, and YOLOv9 models, The study aims to integrate explainable AI features such as Eigen-CAM to manifest the training and testing capabilities of models. In addition, we evaluated the capability of the proposed system using labeled datasets that distinguish elephant emotions to angry, happy, and sad. Our experimental results, conducted at the IoT Cloud Research laboratory, revealed training accuracy of 79.3% for YOLOv5, 79.5% for YOLOv8, and 81.1 % for YOLOv9 and test accuracy of 93%, 95%, and 95% for YOLOv5, YOLOv8, and YOLOv9. Additionally, we recorded the impact of resolutions while performing model evaluations. The observations learned from the experiments will benefit AI-assisted machines to detect elephant emotions that can be utilized by mahouts or people living near forest locations. © 2024 IEEE.

关键词： Elephant Emotion Explainable AI Yolo V5 Yolo V8 Yolo V9

来源：评论

学校读者我要写书评

暂无评论

IoT-Based Pesticide Detection in Fruits and Vegetables Using Hyperspectral Imaging and Deep Learning 4th

IoT-Based Pesticide Detection in Fruits and Vegetables Usi...

引用

4th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2023

作者： Augustin, Anju Kiliroor, Cinu C. Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam Kottayam India

ISBN: (纸本)9783031488870

Fruits and vegetables contain rich nutrients and vitamins. So that they are part of our daily diet. For proper cell growth and health, we need these nutrients. Today in most crops during their growth and post-harvesting preservation different kinds of pesticides were used. Normal usage of such pesticides not that much affects health. But the actual situation is beyond our control. From soil preparation to the post-harvesting stage, pesticides are being added at alarming rates. It affects our health in a harmful way and leads to major health issues. Various studies exist to detect the pesticide levels in fruits and vegetables. This article analyses different existing methods of pesticide detection and examines their features and problems. Through this study, it is understood that Hyperspectral Imaging (HSI) is a very good method, and with it, more accurate results can be obtained by Transfer Learning (few-shot learning). This paper proposes an architecture and algorithm based on HSI and few-shot learning. Future studies are needed in this area to convert an RGB image to a spectral image because the HSI device is very expensive. © 2024, ICST institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Nutrients

来源：评论

学校读者我要写书评

暂无评论

Deep transfer learning technique to detect white blood cell classification in regular clinical practice using histopathological images

引用

Multimedia Tools and Applications 2025年第9期84卷 5699-5723页

作者： Davamani, K. Anita Jawahar, Malathy Anbarasi, L. Jani Ravi, Vinayakumar Al Mazroa, Alanoud Robin, C. R. Rene Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Leather Process Technology Division CSIR-Central Leather Research Institute Chennai India School of Computing Science and Engineering Vellore Institute of Technology Vellore India Computer Science and Engineering Sri Sairam Engineering College Sirukalathur India Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh11671 Saudi Arabia

Leukemia, a malignant disease characterized by the rapid proliferation of specific types of white blood cells (WBC), has prompted increased interest in leveraging automatic WBC classification system. This study presents the design of a robust WBC classification system, integrating deep learning with microscopic image analysis. The proposed classification model is designed to categorize blood cells into four distinct groups: eosinophil, lymphocyte, monocyte, and neutrophil. The study employs a Kaggle image dataset for comprehensive learning. The dataset comprises 12,500 microscopic images used to categorize the four blood cell groups. To enhance performance and reduce the likelihood of overfitting, transfer learning techniques are employed. Six deep transfer learning models namely VGG16, ResNet50, AlexNet, MobileNet, GoogLeNet, and EfficientNetBO are used for feature extraction and classification. The results of the experiments show that VGG16 and MobileNet demonstrated enhancements compared to other pre-trained models when evaluated on both augmented and original datasets. A diverse set of evaluation criteria was employed to thoroughly assess the effectiveness of medical image classification. Notably, the VGG16 and MobileNet models accurately predicted Lymphocytes and Monocytes with a 100% accuracy rate, and Eosinophil and Neutrophil with rates of 99.35% and 99.81%, respectively. This research work empower medical practitioners, particularly in the field of pathology and hematology by providing advanced tools for accurate blood cell classification. The proposed VGG16 and MobileNet models hold promise for simplifying WBC classification, improving decision-making processes, and ultimately advancing patient care in the medical domain. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

An Efficient Framework for Crime Prediction Using Feature engineering and Machine Learning 1

引用

5th International Conference on Data and information sciences, ICDIS 2023

作者： Vengadeswaran Binu, Dhanush Rai, Lokesh Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam Kottayam India

ISBN: (数字)9789819969067

ISBN: (纸本)9789819969050

The growth of crime in society produces insecurity among people and severely impacts the country’s economic development. Understanding crime patterns is necessary to provide a proactive response to curb criminal activities. Most crime prediction models harnessing machine learning/deep learning techniques fail to exhibit significant results for the vague and inconsistent dataset. This is due to the non-consideration of suitable pre-processing and feature engineering techniques, resulting in an unreliable and inaccurate model. Hence in this work, an efficient framework for crime prediction using Feature engineering and Machine learning is proposed. Initially, the vague and inconsistent input dataset is pre-processed using Feature Scaling and Encoding input values. After pre-processing DBSCAN clustering is applied to extract geospatial features, which is used for improving model accuracy. Then SelectKbest library is used to select the 10 best features for the prediction model. Further, the SMOTE library is used to oversample crime types with the lowest sample size. Finally, for classifying the crime types, XGBoost and LGBM classifier models are evaluated. The proposed crime prediction model was validated by deploying an Amazon EC2 P3 instance in the cloud environment. The proposed model yields an improved accuracy of 62% for the XGBoost Classifier and 63% for the LGBM Classifier compared with KNN and Decision Tree. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

A Diagnosis Model Based on Federated Learning for Lung Cancer Classification 2nd

A Diagnosis Model Based on Federated Learning for Lung Cance...

引用

2nd International Conference on science, engineering Management and information technology, SEMIT 2023

作者： Babu, Ann Mary Bellamkonda, Sivaiah Computer Science and Engineering Department Indian Institute of Information Technology Kerala Kottayam India

ISBN: (纸本)9783031722837

Lung cancer, causing a significant number of fatalities annually, is a prominent contributor to global mortality rates among individuals of both genders, with an estimated five million cases resulting in death each year. The utilization of Computed Tomography (CT) scans contribute valuable data for the detection of lung cancer. The main objectives of this study are to diagnose malignant lung nodules in the provided input lung images and to classify lung nodule based on its level of severity. This study proposes a solution wherein locally computed updates are combined to stimulate the learning of a shared model, while the training data remains dispersed throughout mobile devices. Federated Learning (FL) is an advanced technology that operates in a collaborative and decentralized manner, with the primary objective of preserving privacy. Its purpose is to address the obstacles posed by data silos and the sensitivity of data. Therefore, the FL approach is employed for the classification of medical images in collaborative and real-world circumstances. A new approach is proposed in this paper, where the model is trained on data distributed across multiple institutions, and the training process occurs locally at each institution. The performance of the proposed method in terms of accuracy, specificity and sensitivity is evaluated and compared with that of the existing models such as Googlenet, Alexnet, and SVM. The proposed method performs comparably better in all three performance measures indicating that it works better than other methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Lung cancer

来源：评论

学校读者我要写书评

暂无评论

An Ensemble-Based Approach for Cyber Attack Detection in Financial Systems 5th

An Ensemble-Based Approach for Cyber Attack Detection in Fin...

引用

5th International Conference on Deep Learning, Artificial Intelligence and Robotics, ICDLAIR 2023

作者： Kumar, Dhanisht Srivastava, Harshit Bhamare, Kartik S. Kumar, Malay Department of Computer Science and Engineering Indian Institute of Information Technology Karnataka Dharwad India

ISBN: (纸本)9783031609343

Digital commerce is the process of exchanging money or assets using electronic or digital channels such as the internet or mobile devices. Instead of physical cash or checks, individuals or businesses can use digital platforms to transfer money, make payments or conduct financial transactions securely and efficiently. As more and more people conduct financial transactions online, the risk of cyberattacks targeting financial institutions will increase. This article presents an integrated approach to detecting cyber-attacks in the financial sector. The proposed method combines learning models including decision trees, random forests, and gradient boosting to increase the accuracy and robustness of the model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cyber attacks

来源：评论

学校读者我要写书评

暂无评论

Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition

引用

SN computer science 2024年第5期5卷 564页

作者： Duhayyim, Mesfer Al Sharif, Mahir Mohammed Munjal, Muskaan Hilal, Anwer A. Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj 16273 Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan

Sleep quality prediction in Internet of Things (IoT) involves leveraging a system of interrelated devices to gather as well as analyse related data. Smart devices like wearable devices or smart mattresses endlessly monitor many parameters such as body movement, heart rate, and environmental situations in bedroom. Real-time recommendations and feedback transported to consumers, allowing them to create informed lifestyle changes for superior sleep hygiene. As IoT ecosystem continues to develop, a combination of various data streams as well as classy analytics holds assurance for further refining sleep quality forecasts and improving overall well-being. The article designs a Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition (BESHDL-SQP) technique. The purpose of the BESHDL-SQP technique is to recognize and classify the quality of the sleep level in the IoT environment. In the presented BESHDL-SQP technique, the preliminary stage of data pre-processing using min–max normalization involved to normalize the input data. For predicting sleep quality, hierarchical long short-term memory (LSTM) technique exploited. Lastly, BES model utilized for optimal hyperparameter selection of LSTM method which helps in attaining enhanced results. To validate better performance of BESHDL-SQP technique, a huge range of simulation analysis is carried out. The extensive results stated that the BESHDL-SQP technique exhibits supreme performance over other models. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Bald eagle search Internet of things LSTM Sleep quality recognition Smart devices

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：