2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI)最新文献_第4页

Empirical Evaluation of Word Representation Methods in the Context of Candidate-Job Recommender Systems 词表示方法在候选人-职位推荐系统中的实证评价

2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2022-11-26 DOI: 10.1109/ISCMI56532.2022.10068466

Gazmira Brahushi, Uzair Ahmad

{"title":"Empirical Evaluation of Word Representation Methods in the Context of Candidate-Job Recommender Systems","authors":"Gazmira Brahushi, Uzair Ahmad","doi":"10.1109/ISCMI56532.2022.10068466","DOIUrl":"https://doi.org/10.1109/ISCMI56532.2022.10068466","url":null,"abstract":"In this paper, we have evaluated our hybrid two-way recommendation system with expert-ranked resumes and job descriptions. The aim of the paper is to compare the lists produced by the recommendation system with human-ranked lists for candidate and job descriptions. Firstly, we set up four scenarios such as the matching of resume to resumes, job to jobs, resume to jobs, and job to resumes, and prepared a human ranking based on the content similarity on a total of 400 documents. Based on this annotated corpus we tested our system to calculate the cosine-similarity-based ranking for each scenario using the Global Vectors for Word Embeddings and Term Frequency-Inverse Document Frequency representations. Finally, we compared the similarities of human ranked lists and system-ranked lists by using the Rank Biased Overlap (RBO) similarity score. In both methods, GloVe and TF-IDF, the median RBO between human-ranked lists and system ranked are greater than 0.5. The highest median score is achieved on TF-IDF with a slight difference compared to GloVe apart from the ranking of resume-to-resume scenario where the variation between the two methods is considerable. This is due to the similarity between human-ranked lists and program-generated lists.","PeriodicalId":340397,"journal":{"name":"2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"93 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131434223","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}

引用次数: 0

A Cooperative Population-Based Method for Solving the Max-Min Knapsack Problem with Multi-scenarios 基于协作群体的多场景最大最小背包问题求解方法

2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2022-11-26 DOI: 10.1109/ISCMI56532.2022.10068488

Méziane Aïder, M. Hifi, Khadidja Latram

{"title":"A Cooperative Population-Based Method for Solving the Max-Min Knapsack Problem with Multi-scenarios","authors":"Méziane Aïder, M. Hifi, Khadidja Latram","doi":"10.1109/ISCMI56532.2022.10068488","DOIUrl":"https://doi.org/10.1109/ISCMI56532.2022.10068488","url":null,"abstract":"In this paper, we study the max-min knapsack problem with multi-scenarios, where a cooperative population based method is designed for approximately solving it. Its instance is represented by a knapsack of fixed capacity, a set of items (with weights and profits) and possible scenarios related to overall items. Its goal is to select a subset of items whose total weight fills the knapsack, and whose total profit is maximized in the worst scenario according the whole scenarios. The designed method is based upon the grey wolf optimizer, where a series of local searches are employed for highlighting the performance of the method. It starts with a reference set of positions related to the wolves, which is provided with a random greedy procedure. In order to enhance the behavior of the standard version, a series of exploring strategies is employed. Next, in order to avoid premature convergence, a drop and rebuild strategy is added hopping to exploit new unexplored subspaces. Finally, the behavior of the method is computationally analyzed on benchmark instances of the literature, where its provided results are compared to the best results available in the literature. Encouraging results have been obtained.","PeriodicalId":340397,"journal":{"name":"2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"50 s1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132389860","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}

引用次数: 0

A Hybrid Gain-Ant Colony Algorithm for Green Vehicle Routing Problem 绿色车辆路径问题的混合增益-蚁群算法

2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2022-11-26 DOI: 10.1109/ISCMI56532.2022.10068439

V. Sangeetha, R. Krishankumar, K. S. Ravichandran, A. Gandomi

引用次数: 1

Enhanced RecycleNet for Efficient Waste Classification 强化 RecycleNet，实现高效废物分类

2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2022-11-26 DOI: 10.1109/ISCMI56532.2022.10068455

Bhagawat Adhikari, R. Ranabhat, Mohammad Mizanur Rahman, R. Kashef

{"title":"Enhanced RecycleNet for Efficient Waste Classification","authors":"Bhagawat Adhikari, R. Ranabhat, Mohammad Mizanur Rahman, R. Kashef","doi":"10.1109/ISCMI56532.2022.10068455","DOIUrl":"https://doi.org/10.1109/ISCMI56532.2022.10068455","url":null,"abstract":"Segregation of recyclable waste items is one of the crucial aspects of smart cities and their industrial applications. CNN-based machine learning models are widely used to predict and classify image datasets. Traditional deep learning models are fast in training the image dataset, but the classification accuracy is usually too low. Different densely connected CNN architectures are widely used to improve the accuracy in the image waste classification. Despite the remarkable accuracy in such densely connected models, these models often suffer from high computational complexity during the training phase. To overcome this computational complexity, DenseNet121 has been developed, which reduces the training time due to its unique dense block architecture. RecycleNet is a modification of DenseNet121 where the skip connections in the dense block architecture are changed to reduce the computational complexity. In this paper, we propose a unique model called Enhanced RecycleNet, where the skip connections between the dense block architecture are reduced to one-third than in the DenseNet121 model. This unique architecture has improved the model's performance by 46.3% and decreased the trainable parameters from 7 million to about 2.4 million.","PeriodicalId":340397,"journal":{"name":"2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127106225","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}

引用次数: 0

Optimizing Speed and Accuracy Trade-off in Machine Learning Models via Stochastic Gradient Descent Approximation 基于随机梯度下降逼近的机器学习模型速度与精度权衡优化

2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2022-11-26 DOI: 10.1109/ISCMI56532.2022.10068476

Jasper Kyle Catapang

引用次数: 1

Priority-First Search and Mining Popular Packages 优先级搜索和挖掘热门软件包

2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2022-11-26 DOI: 10.1109/ISCMI56532.2022.10068470

Yangjun Chen, Bobin Chen

引用次数: 0

Texture Analysis on Digital Microscopic Leather Images For Species Identification 用于物种识别的数字显微皮革图像纹理分析

2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2022-11-26 DOI: 10.1109/ISCMI56532.2022.10068472

Anjli Varghese, M. Jawahar, A. Prince, A. Gandomi

引用次数: 1

Naïve Bayes with Negation Handling for Sentiment Analysis of Twitter Data Naïve推特数据情感分析的贝叶斯与否定处理

2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2022-11-26 DOI: 10.1109/ISCMI56532.2022.10068474

Lobna H. Kamal, Gerard McKee, N. A. Othman

{"title":"Naïve Bayes with Negation Handling for Sentiment Analysis of Twitter Data","authors":"Lobna H. Kamal, Gerard McKee, N. A. Othman","doi":"10.1109/ISCMI56532.2022.10068474","DOIUrl":"https://doi.org/10.1109/ISCMI56532.2022.10068474","url":null,"abstract":"This paper proposes an enhanced negation handling technique for sentiment analysis of Twitter data using the Naïve Bayes algorithm and Part-of-Speech (POS) tagging. Negation handling detects negated content in text and can thus improve sentiment prediction. The proposed technique focuses on the detection of direct negation words such as “not” and “no”, and implicitly negated content such as “could have been” and “should have been”. The paper compares the proposed negation handling technique with an existing negation handling technique. The Sentiment140 dataset is used in the experiments. Naïve Bayes with the proposed negation handling technique gave an accuracy of 77.57% while the accuracy of the Naïve Bayes with the existing negation handling was 76.93% and the accuracy of the standard Naïve Bayes was 76.12 % for a dataset of 1,000,000 tweets. Of these 1,000,000 tweets 197,381 contained one or more negations. Taking these negated tweets alone, the proposed technique showed an improvement over the existing technique and standard Naïve Bayes with accuracies respectively of 76.51%, 75.98%, and 75.09%. The improvements and shortcomings of the proposed technique are discussed.","PeriodicalId":340397,"journal":{"name":"2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134026302","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}

引用次数: 1

Agent-Based Document Expansion for Information Retrieval Based on Topic Modeling of Local Information 基于局部信息主题建模的基于agent的信息检索文档扩展

2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2022-11-26 DOI: 10.1109/ISCMI56532.2022.10068457

Oliver Strauß, Damian Kutzias, H. Kett

引用次数: 1

Heart Disease Prediction Using Hybrid Machine Learning Model Based on Decision Tree and Neural Network 基于决策树和神经网络的混合机器学习模型的心脏病预测

2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI) Pub Date : 2022-11-26 DOI: 10.1109/ISCMI56532.2022.10068473

Mostafa Bakhshi, S. L. Mirtaheri, S. Greco

引用次数: 0