Shunqi Mei, Jinyu Yang, Bin Xu, Jia Chen, Cong Zhou
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.
{"title":"Effect of Heat Treatment on Chemical Plating of Ni-Cr-P on 65Mn Alloy Steel","authors":"Shunqi Mei, Jinyu Yang, Bin Xu, Jia Chen, Cong Zhou","doi":"10.4018/ijirr.349925","DOIUrl":"https://doi.org/10.4018/ijirr.349925","url":null,"abstract":"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.","PeriodicalId":43345,"journal":{"name":"International Journal of Information Retrieval Research","volume":null,"pages":null},"PeriodicalIF":0.5,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141801638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"A Noval Approach for Object Recognition Using Decision Tree Clustering by Incorporating Multi-Level BPNN Classifiers and Hybrid Texture Features","authors":"Upendra Kumar","doi":"10.4018/ijirr.338394","DOIUrl":"https://doi.org/10.4018/ijirr.338394","url":null,"abstract":"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.","PeriodicalId":43345,"journal":{"name":"International Journal of Information Retrieval Research","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139958358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Effective Information Retrieval Framework for Twitter Data Analytics","authors":"Ravindra Kumar Singh","doi":"10.4018/ijirr.325798","DOIUrl":"https://doi.org/10.4018/ijirr.325798","url":null,"abstract":"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.","PeriodicalId":43345,"journal":{"name":"International Journal of Information Retrieval Research","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46243796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"A New Scalable Deep Learning Model of Pattern Recognition for Medical Diagnosis Using Model Aggregation and Model Selection","authors":"Choukri Djellali, Mehdi Adda","doi":"10.4018/ijirr.316131","DOIUrl":"https://doi.org/10.4018/ijirr.316131","url":null,"abstract":"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.","PeriodicalId":43345,"journal":{"name":"International Journal of Information Retrieval Research","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44738596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the information retrieval system, relevance manifestation is pivotal and regularly based on document-term statistics, i.e., term frequency (tf), inverse document frequency (idf), etc. Query term proximity (QTP) within matched documents is mostly under-explored. In this article, a novel information retrieval framework is proposed to promote the documents among all relevant retrieved ones. The relevance estimation is a weighted combination of document statistics and query term statistics, and term-term proximity is simply aggregates of diverse user preferences aspects in query formation, thus adapted into the framework with conventional relevance measures. Intuitively, QTP is exploited to promote the documents for balanced exploitation-exploration, and eventually navigate a search towards goals. The evaluation asserts the usability of QTP measures to balance several seeking tradeoffs, e.g., relevance, novelty, result diversification (coverage, topicality), and overall retrieval. The assessment of user search trails indicates significant growth in a learning outcome (due to novelty).
{"title":"Promoting Document Relevance Using Query Term Proximity for Exploratory Search","authors":"Vikram Singh","doi":"10.4018/ijirr.325072","DOIUrl":"https://doi.org/10.4018/ijirr.325072","url":null,"abstract":"In the information retrieval system, relevance manifestation is pivotal and regularly based on document-term statistics, i.e., term frequency (tf), inverse document frequency (idf), etc. Query term proximity (QTP) within matched documents is mostly under-explored. In this article, a novel information retrieval framework is proposed to promote the documents among all relevant retrieved ones. The relevance estimation is a weighted combination of document statistics and query term statistics, and term-term proximity is simply aggregates of diverse user preferences aspects in query formation, thus adapted into the framework with conventional relevance measures. Intuitively, QTP is exploited to promote the documents for balanced exploitation-exploration, and eventually navigate a search towards goals. The evaluation asserts the usability of QTP measures to balance several seeking tradeoffs, e.g., relevance, novelty, result diversification (coverage, topicality), and overall retrieval. The assessment of user search trails indicates significant growth in a learning outcome (due to novelty).","PeriodicalId":43345,"journal":{"name":"International Journal of Information Retrieval Research","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44530414","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Prabha Rajagopal, Taoufik Aghris, Fatima-Ezzahra Fettah, Sri Devi Ravana
A user expresses their information need in the form of a query on an information retrieval (IR) system that retrieves a set of articles related to the query. The performance of the retrieval system is measured based on the retrieved content to the query, judged by expert topic assessors who are trained to find this relevant information. However, real users do not always succeed in finding relevant information in the retrieved list due to the amount of time and effort needed. This paper aims 1) to utilize the findability features to determine the amount of effort needed to find information from relevant documents using the machine learning approach and 2) to demonstrate changes in IR systems' performance when the effort is included in the evaluation. This study uses a natural language processing technique and unsupervised clustering approach to group documents by the amount of effort needed. The results show that relevant documents can be clustered using the k-means clustering approach, and the retrieval system performance varies by 23%, on average.
{"title":"Clustering of Relevant Documents Based on Findability Effort in Information Retrieval","authors":"Prabha Rajagopal, Taoufik Aghris, Fatima-Ezzahra Fettah, Sri Devi Ravana","doi":"10.4018/ijirr.315764","DOIUrl":"https://doi.org/10.4018/ijirr.315764","url":null,"abstract":"A user expresses their information need in the form of a query on an information retrieval (IR) system that retrieves a set of articles related to the query. The performance of the retrieval system is measured based on the retrieved content to the query, judged by expert topic assessors who are trained to find this relevant information. However, real users do not always succeed in finding relevant information in the retrieved list due to the amount of time and effort needed. This paper aims 1) to utilize the findability features to determine the amount of effort needed to find information from relevant documents using the machine learning approach and 2) to demonstrate changes in IR systems' performance when the effort is included in the evaluation. This study uses a natural language processing technique and unsupervised clustering approach to group documents by the amount of effort needed. The results show that relevant documents can be clustered using the k-means clustering approach, and the retrieval system performance varies by 23%, on average.","PeriodicalId":43345,"journal":{"name":"International Journal of Information Retrieval Research","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2023-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43685807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Question Answering system is the most promising way of retrieving data from the available knowledge base to the end-users, to get the appropriate result for their questions. Many Question Answering systems convert the questions into triples which are mapped to the Knowledge base from which answer is derived. However, these triples do not express the semantic representation of the question, due to which the answers cannot be located. To handle this, a template-based approach is proposed which classifies the question types and finds appropriate SPARQL query template for each type including comparatives and superlatives. The SPARQL query built is executed in the DBpedia endpoint and results are obtained. Compared with other factoid question answering systems, the proposed approach has the potential to deal with a large number of question types, including comparatives and superlatives. Also, the experimental evaluations of the system performed on the QALD 8 dataset presents good performance and can help users to find answers to their questions.
{"title":"Template based Question Answering System Over Semantic Web","authors":"","doi":"10.4018/ijirr.299933","DOIUrl":"https://doi.org/10.4018/ijirr.299933","url":null,"abstract":"Question Answering system is the most promising way of retrieving data from the available knowledge base to the end-users, to get the appropriate result for their questions. Many Question Answering systems convert the questions into triples which are mapped to the Knowledge base from which answer is derived. However, these triples do not express the semantic representation of the question, due to which the answers cannot be located. To handle this, a template-based approach is proposed which classifies the question types and finds appropriate SPARQL query template for each type including comparatives and superlatives. The SPARQL query built is executed in the DBpedia endpoint and results are obtained. Compared with other factoid question answering systems, the proposed approach has the potential to deal with a large number of question types, including comparatives and superlatives. Also, the experimental evaluations of the system performed on the QALD 8 dataset presents good performance and can help users to find answers to their questions.","PeriodicalId":43345,"journal":{"name":"International Journal of Information Retrieval Research","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43223615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An efficient cluster-based cab recommender system (CBCRS) provides solo cab drivers with recommendations about the next pickup location having high passenger finding potential at the shortest distance. To recommend the cab drivers with the next passenger location, it becomes imperative to cluster the global positioning system (GPS) coordinates of various pick-up locations of the geographic region as that of the cab. Clustering is the unsupervised data science that groups similar objects into a cluster. Therefore, the objectives of the research paper are fourfold: Firstly, the research paper identifies various clustering techniques to cluster GPS coordinates. Secondly, to design and develop an efficient algorithm to cluster GPS coordinates for CBCRS. Thirdly, the research paper evaluates the proposed algorithm using standard datasets over silhouette coefficient and Calinski-Harabasz index. Finally, the paper concludes and analyses the results of the proposed algorithm to find out the most optimal clustering technique for clustering GPS coordinates assisting cab recommender system.
{"title":"Cluster-Based Cab Recommender System (CBCRS) for Solo Cab Drivers","authors":"Supreet Kaur Mann, Sonal Chawla","doi":"10.4018/ijirr.314604","DOIUrl":"https://doi.org/10.4018/ijirr.314604","url":null,"abstract":"An efficient cluster-based cab recommender system (CBCRS) provides solo cab drivers with recommendations about the next pickup location having high passenger finding potential at the shortest distance. To recommend the cab drivers with the next passenger location, it becomes imperative to cluster the global positioning system (GPS) coordinates of various pick-up locations of the geographic region as that of the cab. Clustering is the unsupervised data science that groups similar objects into a cluster. Therefore, the objectives of the research paper are fourfold: Firstly, the research paper identifies various clustering techniques to cluster GPS coordinates. Secondly, to design and develop an efficient algorithm to cluster GPS coordinates for CBCRS. Thirdly, the research paper evaluates the proposed algorithm using standard datasets over silhouette coefficient and Calinski-Harabasz index. Finally, the paper concludes and analyses the results of the proposed algorithm to find out the most optimal clustering technique for clustering GPS coordinates assisting cab recommender system.","PeriodicalId":43345,"journal":{"name":"International Journal of Information Retrieval Research","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42193048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper deals with the problem of determining the optimal selling price and order quantity simultaneously under EOQ model for deteriorating items. It is assumed that the demand rate depends not only on the on-display stock level but also the selling price per unit, as well as the amount of shelf/display space is limited. We formulate two types of mathematical models to manifest the extended EOQ models for maximizing profits and derive the algorithms to find the optimal solution. Numerical examples are presented to illustrate the models developed and sensitivity analysis is reported.
{"title":"Inventory models with stock - and price-dependent demand for deteriotating items based on limited space","authors":"","doi":"10.4018/ijirr.289568","DOIUrl":"https://doi.org/10.4018/ijirr.289568","url":null,"abstract":"This paper deals with the problem of determining the optimal selling price and order quantity simultaneously under EOQ model for deteriorating items. It is assumed that the demand rate depends not only on the on-display stock level but also the selling price per unit, as well as the amount of shelf/display space is limited. We formulate two types of mathematical models to manifest the extended EOQ models for maximizing profits and derive the algorithms to find the optimal solution. Numerical examples are presented to illustrate the models developed and sensitivity analysis is reported.","PeriodicalId":43345,"journal":{"name":"International Journal of Information Retrieval Research","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49050991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a novel hybrid framework with BWO based feature reduction technique which combines the merits of both machine learning and lexicon-based approaches to attain better scalability and accuracy. The scalability problem arises due to noisy, irrelevant and unique features present in the extracted features from proposed approach, which can be eliminated by adopting an effective feature reduction technique. In our proposed BWO approach, without changing the accuracy (90%), the feature-set size is reduced up to 43%. The proposed feature selection technique outperforms other commonly used PSO and GAbased feature selection techniques with reduced computation time of 21 sec. Moreover, our sentiment analysis approach is analysed using performance metrices such as precision, recall, F-measure, and computation time. Many organizations can use these online reviews to make well-informed decisions towards the users’ interests and preferences to enhance customer satisfaction, product quality and to find the aspects to improve the products, thereby to generate more profits.
{"title":"A HYBRID SENTIMENT ANALYSIS APPROACH USING BLACK WIDOW OPTIMIZATION BASED FEATURE SELECTION","authors":"Anand Joseph Daniel, M. Meena","doi":"10.4018/ijirr.289955","DOIUrl":"https://doi.org/10.4018/ijirr.289955","url":null,"abstract":"This paper proposes a novel hybrid framework with BWO based feature reduction technique which combines the merits of both machine learning and lexicon-based approaches to attain better scalability and accuracy. The scalability problem arises due to noisy, irrelevant and unique features present in the extracted features from proposed approach, which can be eliminated by adopting an effective feature reduction technique. In our proposed BWO approach, without changing the accuracy (90%), the feature-set size is reduced up to 43%. The proposed feature selection technique outperforms other commonly used PSO and GAbased feature selection techniques with reduced computation time of 21 sec. Moreover, our sentiment analysis approach is analysed using performance metrices such as precision, recall, F-measure, and computation time. Many organizations can use these online reviews to make well-informed decisions towards the users’ interests and preferences to enhance customer satisfaction, product quality and to find the aspects to improve the products, thereby to generate more profits.","PeriodicalId":43345,"journal":{"name":"International Journal of Information Retrieval Research","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2021-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45893608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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