Bo Mei, College of Science and Engineering, Texas Christian University, Fort Worth, TX 76129, USA
The classification of videos has become increasingly important in the field of data science research, as it has numerous practical applications in modern society. Compared to image classification, video classification poses a significantly greater challenge. One of the most obvious difficulties is that video classification tasks require more powerful computers due to the large number of features that need to be computed. Additionally, conventional 2D Convolutional Neural Networks (2D CNNs) are not effective in handling such tasks. This paper proposes a novel 2-layer Convolutional Neural Network (CNN) architecture for action recognition that addresses these challenges. The proposed architecture achieved a high test accuracy of 79.66% for classifying large video clips. The results indicate the effectiveness of the proposed approach for video classification tasks.
Neural networks, video classification, action recognition.
Dr B.V. Rama Krishna1, B. Sushma2 and V. ChandraSekharRao3, 1,3Department of Computer Engineering, Aditya College of Engineering, Surampalem, India, 2IT-Department, MLRIT, Hyderabad, India
The smart devices communication and data sharing becoming vital part of today’s digital life. The interaction between electronic devices and computational devices made effectively easy with IoT technology. In Metro cities managing the traffic and avoiding traffic congestions becoming a serious issue for police department. In this paper RFID based communication among IoT devices of Traffic Management System and its governance overviewed. Automation of major traffic management system highlighted with RFID technology perspective. The economic benefits and limitations of RFID application over Traffic Systems are coined in this paper. The data mining technologies empowering the data analysis over RFID systems improved the automation process of traffic management systems. The decision support system is boosted with data mining interface with RFID technology in traffic systems.
RFID, IoT, Sensors, Transmitters, Receivers, Data Mining Techniques.
Neural networks, video classification, action recognition.
Diego Vallarino, Independent Researcher, Madrid, Spain
Businesses generate massive volumes of data that is frequently underused due to the fast rise of the Internet and information technology. Translating raw data into information and knowledge that drives decisionmaking unlocks its worth. Machine Learning (ML) algorithms can analyze big datasets and produce accurate predictions. ML has been used in market segmentation, customer lifetime value, and marketing strategies. This article reviews marketing ML methods such Support Vector Machines, Genetic Algorithms, Deep Learning, and K-Means. ML is used to consumer behaviour analysis, recommendation systems, andbankruptcy prediction. Kernel SVM, DeepSurv, Survival Random Forest, and MTLR survival models are compared to predict bank failures. The data show that consumer purchase history, promotions, discounts, pleasant customer experiences, age, online activity, and hobbies impact purchase time. These insights help marketers improve conversion rates.The DeepSurv model predicts purchase completion best in the research.
Survival Analysis, Machine Learning for survival, marketing analytics.
Cheng Xiang and Zheng Yangfei, Department 8 of System, North China Institute of Computing Technology
With the development and application of Internet technology, a large amount of text information has become an important source of information for people. However, the diversity of information sources and the complexity of expression make it one of the hotspots in current natural language processing research to efficiently and accurately extract valuable information from a large amount of text. In view of this problem, this paper takes the extraction of stock increase or decrease information as an example to study how to automatically extract unstructured information from the announcements of listed companies to help investors better obtain information and make wise investment decisions. This paper proposes a rule-based unstructured information extraction model that combines web scraping technology and NLP technology to extract features from HTML documents, automatically identifying and extracting information related to stock increase or decrease. The model has strong practicality and promotion value and has broad application prospects in the field of investors and related research. At the same time, this paper also recognizes that the model still has some limitations and shortcomings, which need to be further explored and improved.
Unstructured Information, NLP, Automatic Identification, Feature Extraction
Mutiu Iyanda Lasisi1 and Kingsley Mawuli Kesseh2, 1Department of Media Communications, HSE National Research University, Moscow, Russian Federation and 2Department of World Economy and International Affairs, HSE National ResearchUniversity, Moscow, Russian Federation
This paper investigates the positions of state and non-state actors during the ban of Twitter by the Nigerian government between June 4, 2021 and January 13, 2022. It answers questions about whether non-state actors resisted the ban due to Twitters contributions to democratic, civic space, and economic growth, and whether state actors considered the medium a threat to the countrys unity and sovereignty with supportive evidence. Conflict theory and the Advocacy Coalition Framework are used as theoretical and analytical frameworks to explore the different beliefs and interests of the actors involved. The study found that the ban generated a series of reactions from these actors, leading to the development of three forms of narratives from the policy and advocacy coalition beliefs. The study concludes that examining the views of state and non-state actors during the ban period provides insights into the different interests, beliefs, and values that shape policy conflicts.
Conflict, Democracy, Twitter, Nigeria, Policy Actors
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