International Journal of Information Retrieval Research (IJIRR)

In recent times, self-driving vehicles have been widely adopted across different countries as they are equipped to drastically reduce the number of road accidents and congestion on the road thereby improving the traffic efficiency. To detect, identify, and label the traffic signs on the road in order to help the Advanced Driver Assistance Systems (ADAS) in these autonomous vehicles with navigation details, a Traffic Sign Recognition (TSR) System using a deep convolutional neural network model, Mask RCNN (Mask Regional Convolutional Neural Network), is proposed in this paper that aims to help the autonomous vehicles comprehend the road ahead and safely navigate to the desired destination. This paper presents the detection and labelling of Indian and European Signs and also the results of the system working efficiently under various challenging visibility conditions. The results obtained show that the Mask RCNN model has recorded higher performance compared to all the other CNN models that have been previously used for traffic sign recognition.

Amritha Barade

Haritha Poornachandran

K. M. Harshitha

None Shiloah Elizabeth D.

None Sunil Retmin Raj C.

International Journal of Information Retrieval Research

Automatic Traffic Sign Recognition System Using CNN

使用卷积神经网络的自动交通标志识别系统

Trust-based mechanisms are widely used in wireless networks of different kinds for providing security against attack. Trust mechanism provides security from various attacks using both detective and preventive manner. This paper presents a quality service paradigm that can be integrated with any underlying trust mechanism. The paradigm includes different flags corresponding to different services incorporated in various routing packets. The paradigm provides flexible customization and adaptability as per the demand of communicating nodes for effective data transfer. Various quality service classes are designed to formulate route as per the requirement to minimize the routing overhead and balancing of load among nodes. This paradigm power is the trust mechanism with proactive action for detection of malicious nodes in the network. The proposed paradigm is incorporated in an established trust mechanism and compared with standard version of that trust mechanism for packet delivery ratio and routing overhead.

Nitin Khanna

Sandeep Singh

Anshu Bhasin

Kamal Malik

Integration of Multi-Class Service Paradigm With Generic Trust Mechanism for Innovation, Customization and Adaptability in MANETs

多类服务范式与通用信任机制的整合：MANETs中的创新、定制与适应性

The present work uses flutter and transfer learning-based techniques for predicting the shelf-life of fruits in real-time by reducing the demand for huge number of samples and longer training durations. The work uses mango fruit as a case study, by capturing two types of images axiomatically through smartphone rear camera and a Seek thermal camera. A set of models based on transfer learning were created, then trained, and applied post-training quantization for optimizing the model size and low latency. The models were subsequently converted into TFLite format with TF-Select Ops and deployed in the Flutter app code, and thereby generating the APK files for real-time execution on mobile devices. The performance metrics of the models were evaluated on accuracy, model size, and latency. With a minor level of degradation in accuracy, the model with quantization outperforms and achieves better latency and 12x times reduction in size. The overall accuracy achieved for both the cameras (normal and Thermal) on mobile device was 90% and 93.33% respectively, for predicting the fruit shelf life.

Varsha Yogesh Bhole

Arun Kumar

Flutter-Based Real-Time Mobile App for Fruit Shelf-Life Prediction (FSP) Using Multi-Modality Imaging

基于Flutter的水果货架期预测（FSP）实时移动应用的多模态成像技术

Selecting appropriate views that provide faster query response time is a critical decision in data warehouse design. Top-level users expect quick results from a data warehouse for faster decision-making to gain a competitive edge in business. Prioritizing a view can distinguish views required to answer top-level users' queries from regular users and provide a better selection chance. The prioritized materialized view selection (PMVS) problem addresses how to utilize the given space to materialize prioritized views more relevant to users. Particle swarm optimization algorithm has been used to achieve minimized query processing costs. Evolutionary algorithms are widely known to solve complex optimization problems quickly by reaching a semi-optimal solution. This paper explores the performance of six evolutionary algorithms: particle swarm optimization, coral reef optimization, cuckoo search, ant colony optimization, grey wolf optimization, and artificial bee colony. The results of empirical and statistical analysis show that PSO, CRO, and GWO algorithms are best suited to solve PMVS.

Heena Madaan

Anjana Gosain

Evolutionary Optimization for Prioritized Materialized View Selection

优先化物化视图选择的进化优化

SDN has come up as a promising technology for a future network as a logically centralized controlled framework along with its physically distributed architecture isolating the control plane from sending data moving the entire choice capacity to the regulator. SDNs are turning out to be significant because of scalability, adaptability and testing. As SDN needs overhead for operation, it makes it as a target of Distributed Denial of service (DDoS) attacks. The extensive review in the existing literature survey provides results for small footprint of dataset causing over fitting of the classifier. In the survey it is also been observed that the KNN based algorithms to detect DDOS attacks are lazy learners resulting in the noisy data. This paper proposes a Dual Probability Transductive Confidence Machines and Support Vector Machine (DPTCM-SVM) classifier to avoid the over-fitting for detecting DDoS in SDN. The results generated for detection are more than 98% for all the attack classes making it an Eager Learning System which requires less learning space unlike the Lazy Learning Systems.

Gaganjot Kaur

Prinima Gupta

Yogesh Kumar

Detection Mechanism Using Transductive Learning and Support Vectors for Software-Defined Networks

用于软件定义网络的基于传导学习和支持向量的检测机制

During fabric production, machine malfunctions and yarn breakages can lead to defects, affecting product quality. Thus, fabric defect detection is crucial for quality control. Existing detection systems struggle with speed and accuracy, especially for small defects, due to the diversity of defect types and shapes. This paper introduces an improved YOLOv5s algorithm, YOLOv5s-GSD, which enhances detection performance by integrating a Global Attention Mechanism (GAM), the SIOU loss function, and a decoupling head. The GAM enhances focus on relevant features, improving detection accuracy; the SIOU loss function replaces the GIOU loss function to optimize the vector angle of regressions, enhancing convergence speed and accuracy; and the decoupling head separates classification and regression tasks, further improving detection performance. Experimental results show the improved algorithm achieves an accuracy of 97.6% and a recognition rate of 36 frames per second, effectively reducing the miss rate of small targets.

Shunqi Mei

Zhi Xiao

Jia Chen

Shiyi Shan

Shi Zhou

Improved YOLOv5s Fabric Defect Detection Algorithm Based on the Fusion of Global Attention Mechanism and Decoupling Head

基于全局注意力机制与解耦头的改进YOLOv5s织物缺陷检测算法

The chemical plating of Ni-Cr-P significantly affects the improvement of the structure and characteristics of the metal surface. This study aimed to investigate the impact of various plating solution components and process parameters on the deposition rate and surface hardness of Ni-Cr-P coatings on 65Mn alloy steel. Additionally, the study examined the changes in coating properties resulting from different heat treatment temperatures. The specimens coated with Ni-Cr-P underwent heat treatment at temperatures of 180°C, 200°C, 220°C, and 240°C, respectively. The testing results indicate that the plating layer achieves its highest level of performance when subjected to a heat treatment temperature of 200°C. The heat-treated coating exhibits superior wear and corrosion resistance compared to the 65Mn steel substrate. Additionally, the coefficient of friction decreases from 0.5 to 0.4. The self-corrosion potential shifts positively from -569 mV to -389 mV, and the corrosion current decreases from 61.4 μA to 14.48 μA. Furthermore, the impedance improves from 234 Ω·cm2 to 3512 Ω·cm2.

Jinyu Yang

Bin Xu

Cong Zhou

Effect of Heat Treatment on Chemical Plating of Ni-Cr-P on 65Mn Alloy Steel

热处理对65Mn合金钢上化学镀Ni-Cr-P的影响

This work proposes a novel approach to object recognition, particularly for human faces, based on the principle of human cognition. The suggested approach can handle a dataset or problem with a large number of classes for classification more effectively. The model for the facial recognition-based object detection system was constructed using a combination of decision tree clustering based multi-level Backpropagation neural network classifier-TFMLBPNN-DTC and hybrid texture feature (ILMFD+GLCM) and applied on NS and ORL databases. This model produced the classification accuracy (±standard deviation) of 95.37 ±0.951877% and 90.83 ± 1.374369% for single input and 96.58 ±0.5604582% and 91.50 ± 2.850439% for group-based decision for NS and ORL database respectively. The better classification results encourage its application to other object recognition and classification issues. This work's basic idea also makes it easier to improve classification management for a wide range of classes.

Upendra Kumar

A Noval Approach for Object Recognition Using Decision Tree Clustering by Incorporating Multi-Level BPNN Classifiers and Hybrid Texture Features

一种结合多层次BPNN分类器和混合纹理特征的决策树聚类物体识别新方法

The widespread adoption of opinion mining and sentiment analysis in higher cognitive processes encourages the need for real time processing of social media data to capture the insights about user's sentiment polarity, user's opinions, and current trends. In recent years, lots of studies were conducted around the processing of data to achieve higher accuracy. But reducing the time of processing still remained challenging. Later, big data technologies came into existence to solve these challenges but those have its own set of complexities along with having hardware deadweight on the system. The contribution of this article is to touch upon mentioned challenges by presenting a climbable, quick and fault tolerant framework to process real-time data to extract hidden insights. This framework is versatile enough to support batch processing along with real time data streams in parallel and distributed environment. Experimental analysis of proposed framework on twitter posts concludes it as quicker, robust, fault tolerant, and comparatively more accurate with traditional approaches.

Ravindra Kumar Singh

Effective Information Retrieval Framework for Twitter Data Analytics

Twitter数据分析的有效信息检索框架

In recent years, pattern recognition has become a research area with increasing importance using several techniques. One of the most common techniques used is deep learning. This paper presents a new deep learning model to pattern recognition for medical diagnosis. The uncovering of hidden structures is performed by feature selection, model aggregation, and model selection. The deep learning model has the ability to reach the optimal solution and create complex decision boundaries when used to look for and diagnose breast cancer. The evaluation, based on 10-fold cross-validation, showed that the proposed model, which is named BaggingSMF, yielded good results and performed better than radial basis function, bidirectional associative memory, and ELMAN neural networks. Experimental studies demonstrate the multidisciplinary applications of the model.

Choukri Djellali

Mehdi Adda

A New Scalable Deep Learning Model of Pattern Recognition for Medical Diagnosis Using Model Aggregation and Model Selection

一种用于医学诊断的新型可扩展深度学习模式识别模型：基于模型聚合与模型选择

短名	Int. J. Inf. Retr. Res.
Journal Impact	0.38
国际分区	COMPUTER SCIENCE, INFORMATION SYSTEMS(Q4)

涉及主题	计算机科学人工智能数学机器学习程序设计语言情报检索哲学操作系统万维网数据挖掘工程类语言学自然语言处理生物统计物理
出版信息	出版商: IGI Global，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: ，原创研究文献占比: 100.00%，自引率:0.00%， Gold OA占比: 73.49%

International Journal of Information Retrieval Research (IJIRR)

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