Journal of Computers, Mechanical and Management最新文献

A Multi-Model Approach for Disaster-Related Tweets 灾害相关推文的多模型方法

Journal of Computers, Mechanical and Management Pub Date : 2024-07-01 DOI: 10.57159/gadl.jcmm.3.2.240125

Parth Mahajan, Pranshu Raghuwanshi, Hardik Setia, Princy Randhawa

引用次数: 0

Enhancing Secondary Education in Kamrup District Through Value-Added Courses 通过增值课程加强甘鲁普县的中学教育

Journal of Computers, Mechanical and Management Pub Date : 2024-07-01 DOI: 10.57159/gadl.jcmm.3.2.240128

Aruna Dev Rroy, B. Rajkhowa

{"title":"Enhancing Secondary Education in Kamrup District Through Value-Added Courses","authors":"Aruna Dev Rroy, B. Rajkhowa","doi":"10.57159/gadl.jcmm.3.2.240128","DOIUrl":"https://doi.org/10.57159/gadl.jcmm.3.2.240128","url":null,"abstract":"The concept of value-added courses in education is rooted in supplementing the core curriculum with additional learning opportunities beyond standard academic subjects. The secondary stage of education should emphasize optimal learning based on students' cognitive development through experiential and hands-on experience by incorporating value-added courses in the curriculum. Today, while the world has become globalized with limited resources, colleges and universities should pay more attention to preparing students to be sustainable (McCaffrey & Hamilton, 2007) both in act and behavior. The preparation of the students for the industry should begin from the secondary stage. So, the policymakers should contemplate and pay attention to the pedagogy to meet future needs. The conceptual framework underlying value-added courses emphasizes their role in supplementing the core curriculum, providing students with practical skills and competencies essential for holistic development. Also, the sustainability of behavioral changes induced by value-added courses is contingent on several factors. The goal is to gain access to an expansive repertory of knowledge to grow professional skills. This study attempts to determine the sustainability and the challenges of incorporating value-added courses in the Secondary School curriculum.","PeriodicalId":372188,"journal":{"name":"Journal of Computers, Mechanical and Management","volume":"58 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141843049","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

AI-Driven Decision Support System Innovations to Empower Higher Education Administration 人工智能驱动的决策支持系统创新为高等教育管理赋能

Journal of Computers, Mechanical and Management Pub Date : 2024-07-01 DOI: 10.57159/gadl.jcmm.3.2.24070

Jiangang Zhang, S. Goyal

{"title":"AI-Driven Decision Support System Innovations to Empower Higher Education Administration","authors":"Jiangang Zhang, S. Goyal","doi":"10.57159/gadl.jcmm.3.2.24070","DOIUrl":"https://doi.org/10.57159/gadl.jcmm.3.2.24070","url":null,"abstract":"This study explores the utilization, perceptions, and impacts of Decision Support Systems (DSS) in higher education administration. With a focus on DSS, a cross-sectional survey was conducted among higher education administrators from various institutions. The findings underscore the essential role of DSS in higher education administration, with administrators reporting significant utilization and praising their effectiveness and user-friendliness. The study reveals the positive influence of DSS on strategic planning, enrollment management, resource allocation, and student success initiatives. Moreover, it demonstrates the association between DSS usage and favorable outcomes, including increased efficiency and perceived positive consequences. However, persistent challenges such as data quality issues, privacy concerns, and resistance to change highlight the need for improved data management strategies, ethical considerations, and change management approaches. These findings contribute to the ongoing discourse on the transformative potential of DSS in higher education administration and provide valuable insights for businesses seeking to enhance decision-making, resource allocation, and data-driven initiatives. The innovative integration of AI in DSS for higher education administration represents a paradigm shift in decision-making processes, offering unprecedented opportunities for improvement and innovation.","PeriodicalId":372188,"journal":{"name":"Journal of Computers, Mechanical and Management","volume":"1 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141845358","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

Evaluating Climate Change Mitigation Strategies of G20 Countries: Policies, Actions, and Progress Towards Global Emission Reduction Goals 评估 20 国集团国家的气候变化减缓战略：政策、行动和实现全球减排目标的进展情况

Journal of Computers, Mechanical and Management Pub Date : 2024-07-01 DOI: 10.57159/gadl.jcmm.3.2.240130

Sonal Devesh, Anchal Sharma, Arjun Maheshwari

{"title":"Evaluating Climate Change Mitigation Strategies of G20 Countries: Policies, Actions, and Progress Towards Global Emission Reduction Goals","authors":"Sonal Devesh, Anchal Sharma, Arjun Maheshwari","doi":"10.57159/gadl.jcmm.3.2.240130","DOIUrl":"https://doi.org/10.57159/gadl.jcmm.3.2.240130","url":null,"abstract":"The G20 countries are responsible for over 75% of the greenhouse gas (GHG) emissions at a global level. The research summarises the role of G20 countries in combating Climate Change. This research study explores the comprehensive assessment of the G20 nations’ policies and the impacts of climate change across the globe. The paper studies the policies of the G20 countries’ governments to meet the Nationally Determined Contribution (NDC) target and achieve the global goal of the Paris Agreement (or COP28) and Net Zero Emissions Target of limiting the level of global temperature increase to well below 2 degrees C while pursuing efforts aligning to a global threshold objective of 1.5-degree C. Through the review of existing literature, the researchers aim to provide a better understanding of climate change and the biodiversity and ecosystem. In addition to this, the study provides various strengths and opportunities for the countries to explore soon, reducing the emission levels in the ecosystem and thus, promoting a sustainable future, through an interlinked phenomenon.","PeriodicalId":372188,"journal":{"name":"Journal of Computers, Mechanical and Management","volume":"160 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141852784","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

Deep Learning-Driven Compiler Enhancements for Efficient Matrix Multiplication 针对高效矩阵乘法的深度学习驱动编译器增强功能

Journal of Computers, Mechanical and Management Pub Date : 2024-07-01 DOI: 10.57159/gadl.jcmm.3.2.240122

Raunak Kumar, Karma Chhering Negi, Nitish Kumar Sharma, Priya Gupta

{"title":"Deep Learning-Driven Compiler Enhancements for Efficient Matrix Multiplication","authors":"Raunak Kumar, Karma Chhering Negi, Nitish Kumar Sharma, Priya Gupta","doi":"10.57159/gadl.jcmm.3.2.240122","DOIUrl":"https://doi.org/10.57159/gadl.jcmm.3.2.240122","url":null,"abstract":"Matrix multiplication is a fundamental operation in many computational fields, requiring optimization to handle increasing data sizes efficiently. In this paper, the implementation of Deep Learning in Matrix multiplication is reviewed, which is considered important nowadays due to the growing complexity of matrix multiplication for gaming and complex programs. The current standard matrix multiplication and the time taken by it on different matrix sizes are described. The Tiled Matrix multiplication, which trims the matrix into various pieces and calculates the product for each piece, and thereafter combines the result, is also described. The times taken by both methods for different matrix sizes were compared. The main idea was to use Deep Neural Networks (DNN) to compare and rank code variants that are obtained in pieces and determine their relative performance. A tournament-based ranking system is used for assigning ranks to the code versions. The effectiveness of these techniques was evaluated on various matrix multiplication operations commonly found in deep learning workloads. Up to 8.844x speedup over the naive implementation for a matrix size of 1024 is achieved by this approach. The results demonstrate the effectiveness of combining compiler optimization techniques and deep learning models in optimizing matrix multiplication.","PeriodicalId":372188,"journal":{"name":"Journal of Computers, Mechanical and Management","volume":"21 19","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141846127","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

Editorial Comments 编辑评论

Journal of Computers, Mechanical and Management Pub Date : 2024-01-03 DOI: 10.57159/gadl.jcmm.2.6.230112

Nanjangud Subbaro Mohan, Lokesh Choudhary

引用次数: 0

Fundus Image Generation using EyeGAN 使用 EyeGAN 生成眼底图像

Journal of Computers, Mechanical and Management Pub Date : 2023-12-31 DOI: 10.57159/gadl.jcmm.2.6.230106

Preeti Kapoor, Shaveta Arora

引用次数: 0

Traffic Noise Prediction 交通噪音预测

Journal of Computers, Mechanical and Management Pub Date : 2023-12-31 DOI: 10.57159/gadl.jcmm.2.6.230104

Suman Mann, Gyanendra Singh

引用次数: 0

Bond strength of substrate with repair material for masonry structures 砌体结构基底与修补材料的粘结强度

Journal of Computers, Mechanical and Management Pub Date : 2023-12-31 DOI: 10.57159/gadl.jcmm.2.6.230107

Khyati Saggu, Shilpa Pal, Nirendra Dev

引用次数: 0

Sentiment Analysis on IMDB Review Dataset IMDB 评论数据集的情感分析

Journal of Computers, Mechanical and Management Pub Date : 2023-12-31 DOI: 10.57159/gadl.jcmm.2.6.230108

Shubham Kumar Singh, Neetu Singla

{"title":"Sentiment Analysis on IMDB Review Dataset","authors":"Shubham Kumar Singh, Neetu Singla","doi":"10.57159/gadl.jcmm.2.6.230108","DOIUrl":"https://doi.org/10.57159/gadl.jcmm.2.6.230108","url":null,"abstract":"A computational method known as sentiment analysis is employed to ascertain the emotional undertone or attitude of a text document, such as a review, tweet, or news story. Using machine learning models, deep neural network models, and natural language processing, the method entails examining the text to determine whether it expresses positive or negative sentiment. In this study, models like Naive Bayes, Logistic Regression, LSTM, LSVM, Decision tree, and BiLSTM are utilized to conduct a sentiment analysis (SA) study on the IMDB dataset. The goal of the investigation is to evaluate how well these models perform in retrospect on movie reviews, categorizing them as positive or negative. The study investigates the effects of data pre-processing methods and hyperparameter tuning on the models’ accuracy. The final results demonstrate that the BiLSTM model outperforms the other models in terms of recall, precision, and accuracy, followed by the LSTM, Logistic Regression, LSVM, Decision Tree, and Naive Bayes models. The research emphasizes the potential of deep learning models—in particular, BiLSTM in sentiment analysis tasks, as well as the significance of hyper-parameter tuning and pre-processing methods in achieving high accuracy.","PeriodicalId":372188,"journal":{"name":"Journal of Computers, Mechanical and Management","volume":"112 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139135178","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