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26th International Conference on computer and Information technology, ICCIT 2023

作者： Noor, Emamun Khan, Arafath Islam, Ahnaf Kamal, A.H.M. Tabassum, Nuzhat Anannya, Tasmiah Tamzid Karim Majumder, Md Mahfuzul Islam, Muhammad Nazrul Military Institute of Science and Technology Department of Computer Science and Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798350359015

A nation's global security is significantly impacted by border security. A significant number of human and illegal items are trafficked between countries across borders, creating a source of concern in many nations. Avoiding tariff duties and the business of illegal goods are the common issues in ensuring border security. Traditional security forces are insufficient to deal with the intruders with their ever-changing ideas of intrusion. Therefore, this research proposed a digital border surveillance system to help the border security forces for enhancing the command and control of the borders. The proposed system utilized YOLOv7 architecture to detect movement in real-time and monitors the real-time movement of individuals in uniform, civilians (without uniform), and animals within the proximity of the responsible area as well as detects longitudinal movements through sensors. The system is also capable of sending different forms of notifications depending on the types of movements. Finally, the system showed an average success rate of 89% and 85% for sending alarms and notifications, respectively while detecting any movements. © 2023 IEEE.

关键词： border Border Out Post (BOP) migration movement detection surveillance trafficking YOLOv7

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Predicting Credit Card Defaults with Machine Learning

Predicting Credit Card Defaults with Machine Learning

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems, AICERA/ICIS 2023

作者： Bakshi, Kanishk Jinny, S. Vinila School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350303452

Detecting credit card fraud is of paramount importance to financial institutions and credit card companies, as it plays a vital role in preventing monetary losses due to fraudulent transactions. The application of machine learning algorithms has become prevalent in this domain due to their capacity to analyze large datasets and identify patterns. In this specific study, we performed a comparative analysis of five machine learning algorithms, namely Logistic Regression, Decision Tree Classifier, Gaussian Naive Bayes, Random Forest Classifier, and Extra Trees Classifier, with the aim of detecting credit card fraud. The dataset employed in this study was characterized by its imbalanced nature, necessitating the use of the SMOTEENN algorithm to achieve balance in the dataset. To evaluate and compare the performance of the algorithms, various evaluation metrics were used, including precision, recall, F1-score, and the average precision-recall (AUPRC) curve. The outcomes indicate that the Extra Trees Classifier demonstrated superior performance across all metrics, including precision, recall, F1-score, and AUPRC. These results suggest that the Extra Trees Classifier exhibits significant potential as an effective instrument for detecting credit card fraud in real-world situations. © 2023 IEEE.

关键词： Large datasets

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A Method Based on Genetic Programming to Automatically Construct Factors for Annual Report Scenarios 20

A Method Based on Genetic Programming to Automatically Const...

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20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2024

作者： Ma, Yan Zhang, Changsheng Gao, Yan Guo, Ying School of Computer Science and Engineering Northeastern University Shenyang China Software College Northeastern University Shenyang China College of Computer Science and Engineering Ningxia Institute of Science and Technology Shizuishan China

ISBN: (纸本)9798350356328

Factors have always played an important role in stock analysis, but they are only effective for specific problems in specific scenarios. Therefore, constructing factors timely and quickly for different scenarios is an urgent problem to be solved. Although some experts have constructed factors, they need to manually construct factors for each scenario, and the construction process consumes time and effort. The annual report is an important and common scenario. It is an important form of information disclosure and financial reporting. Therefore, this paper proposes a method for automatically constructing factors based on genetic programming that combines expert experience for this scenario. It incorporates the knowledge and insights of experts in the field of stock analysis into the process of automati-cally constructing factors, and continuously adjusts and improves the combination of factors through genetic programming to adapt to the needs of the scenario. The effectiveness of this method is verified through empirical analysis and its advantages in specific scenarios are demonstrated. © 2024 IEEE.

关键词： Genetic programming

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Collaborative, Privacy-Preserving Federated Learning Framework for the Detection of Diabetic Eye Diseases

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SN computer science 2024年第8期5卷 1100页

作者： Gulati, Seema Guleria, Kalpna Goyal, Nitin Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India Department of Computer Science and Engineering School of Engineering and Technology Central University of Haryana Haryana Mahendergarh India

Diabetes Mellitus is one of the chronic metabolic disorders that is easily identified by elevation in blood sugar levels. The high blood sugar levels over time can lead to substantial damage to the blood vessels, heart, eyes, kidneys, and nerves. Diabetic Eye Diseases (DED) such as Diabetic Macular Edema (DME), Diabetic Retinopathy (DR), and glaucoma, are serious complications of diabetes that can result in irreversible vision loss. Thus, affecting the health and quality of life of the population worldwide. The primary objective of the proposed work is to evaluate the efficacy of a privacy-preserved Federated Learning (FL) framework for detecting DEDs. To detect DEDs a collaborative and privacy-preserving Federated Deep Learning (FDL) framework using a lightweight MobileNetV2 architecture is proposed. The aggregation scheme selected for the FDL framework is FedProx and to achieve a high accuracy score the proximal term, µ is tuned with values ranging from µ= (0.01, 0.02, 0.03, 0.04 and 0.05). The impact of the variance in the proximal term on model performance is analyzed, and the results show that higher µ (0.03,0.4 and 0.5) values provide more stable and consistently high precision, recall, and F1-scores. The findings suggest that FedProx aggregation successfully stabilizes the performance of the framework with the least standard deviation of 0.035 in accuracy at µ = 0.05. The balance between accuracy and stability is a key factor in the successful application of federated learning in disease prediction. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial intelligence Collaborative training Diabetic eye diseases Federated learning Privacy-protection

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A Comprehensive Survey on Metaheuristic Algorithm for Feature Selection Techniques

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Materials Today: Proceedings 2022年 64卷 435-441页

作者： Arun Kumar, R. Vijay Franklin, J. Koppula, Neeraja Department of Information Technology Bannari Amman Institute of Technology Sathyamangalam Erode India Department of Computer Science and Engineering Bannari Amman Institute of Technology Sathyamangalam Erode India Department of Computer Science and Engineering Geethanjali College of Engineering and Technology Hyderabad Hyderabad India

In Healthcare industries, patients’ medical records consist of a greater number of features or attributes related to certain health factors and medical terminologies. In these feature set, many attributes are either redundant one or irrelevant to the medical disease that is going to be dealt with it. These redundant and irrelevant feature sets is not going to enhance the final result at any cost and moreover it out bounds the final prediction. Feature selection is the process of selecting feature sets in a neural model that are related to a specific problem in order to aid in the inclusion of relevant features and the exclusion of irrelevant or redundant features, thereby reducing the amount of noisy data and the size of the entire dataset. In this paper, metaheuristic related techniques are taken into account and summarized about its key feature along with how it works. Also, various Feature selections related research works that have been published in last three years are highlighted with its proposed methodology, benefits and limitations. From this, it is inferred that many open challenges and enhancements are still available in the feature selection problem even though many researchers introduced their novelty in this problem. © 2022

关键词： Feature Selection

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Enhancing Cyberbullying Detection on Twitter with Psychological Features and Machine Learning 1

Enhancing Cyberbullying Detection on Twitter with Psychologi...

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1st IEEE International Conference on Emerging Research in Computational science, ICERCS 2023

作者： Lalitha, N. Sumaya Thabasum, S.K. Tejaswini, N. Bhagya Sri, D. Srivani, R. Vignan Nirula Institute of Technology and Science for Women Computer Science & Engineering Guntur India

ISBN: (纸本)9798350359756

Today, a large number of people dabble in the realm of social media. Due to the pandemic situation, people are even more engaged since they frequently use social media to vent their emotions. One of the many detrimental effects of this pervasive usage of social media is cyberbullying, which is a troubling form of online harassment. Though it can take several forms, the most common one is text. Cyberbullying is common on social media, and instead of confronting the perpetrator, victims often have mental breakdowns as a result of it. This study's computerized cyberbullying detection method accesses Twitter users' psychological traits, including their personalities, moods, and emotions. Our study provides an innovative solution for detecting cyberbullying tweets by an attention-based transformer algorithm combined with embeddings. Our model acts as a detector in classifying tweets that are related to cyberbullied actions. These tweets are converted into numerical vectors by Embeddings and divided into fixed segments through the padding technique. The transformer model learns from the encoder part comprising of self-attention and feed-forward neural network and normalization through the tweet's dataset. Incredibly accurate cyberbullying detection is made possible by this integrated technology. Our approach promises to identify cyberbullying quickly and precisely to give more control to women over the situation. © 2023 IEEE.

关键词： Embeddings

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A Comprehensive Analysis of Machine Learning and Deep Learning Based Product Recommendation System 5th

A Comprehensive Analysis of Machine Learning and Deep Learni...

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5th EAI International Conference on Cognitive Computing and Cyber Physical Systems, IC4S 2024

作者： Chakravarthi, GKalyan Kumar, Raghvendra Addagarla, Ssvr Kumar Department of Computer Science and Engineering School of Engineering and Technology GIET University Odisha Gunupur India Nadimpalli Satyanarayana Raju Institute of Technology Visakhapatnam India

ISBN: (纸本)9783031770746

Recommender Systems (RS) have been widely applied in various real-time applications to support identifying valuable information. The RS tries to give actual suggestions to every user based on their behavior as well as interests. Recommendations generated by these systems often contend with the unique personal interests of individual users, thereby playing a pivotal role in their decision-making processes. Recommender Systems (RS) act as efficient tools for filtering vast amounts of online data, shaping the behaviors of smartphone users, personalization trends, and the evolution of internet access. RS are generally classified into three primary types: Hybrid Filtering, Content-Based Filtering (CBF), and Collaborative Filtering (CF). These systems find wide application across various fields including online education systems, e-commerce, marketing, tourism, food service, movies, business, and beyond. Although the recent RS are well-known in providing valuable recommendations, they suffer from a number of limitations as well as challenges such as scalability, sparsity, and cold-start and so on. Due to the various approach’s existence, the selection of these approaches becomes challenging during the development of application-based RS. Moreover, every approach comes with its individual feature sets, advantages as well as limitations, which must be addressed. This survey reviews the research inclinations which integrates the progressive technical characteristics of the RS. The aim of this survey is to ensure a systematic review on recent contributions in RS domain, and concentrated on various applications such as education, food, products and so on. This survey provides a comprehensive review of these types of RS, recent literature, and applications of visual RS. © ICST institute for computer sciences, Social Informatics and Telecommunications engineering 2025.

关键词： Collaborative filtering

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A Novel Approach on Deep Reinforcement Learning for Improved Throughput in Power-Restricted IoT Networks 1

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International Conference on Electrical and Electronics engineering, ICEEE 2023

作者： Sweety Bakyarani, E. Singh, Navneet Pratap Shekhawat, Jyoti Bhardwaj, Saurabh Chaku, Shweta Singh, Jagendra Department of Computer Science SRM Institute of Science and Technology Kattankulathur India School of Computer Science Engineering and Technology Bennett University Greater Noida India Vivekanand Global University Rajasthan Jaipur India Department of Information Technology Raj Kumar Goel Institute of Technology AKTU Lucknow Ghaziabad India Department of Computer Science Engineering Inderprastha Engineering College AKTU Lucknow Ghaziabad India

ISBN: (数字)9789819986613

ISBN: (纸本)9789819986606

The rapid expansion of the Internet of Things (IoT) has stressed the importance of energy-efficient communication protocols, particularly in networks operating under power constraints. This paper presents a unique approach for managing communication in energy-limited IoT networks using a deep reinforcement learning (DRL)-based communication protocol. By integrating Sparse Code Multiple Access (SCMA), Code Division Multiple Access (CDMA) techniques, and the Combined Experience Replay Deep Deterministic Policy Gradient (CER-DDPG) algorithm, we developed a novel protocol to improve the throughput of power-constrained sensors in an IoT network. Through comprehensive simulations, we compared the proposed protocol’s performance with benchmark systems like traditional DDPG and stochastic algorithms. The results reveal superior energy efficiency and throughput with the proposed protocol, establishing its potential to significantly enhance the performance of energy-constrained IoT networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Reinforcement learning

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Automated Exoskeleton Prosthetic Suit With Monitoring System 4

Automated Exoskeleton Prosthetic Suit With Monitoring System

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Jeeva, C. Sagar, Lalit Kumar Srivastava, Mudit P. Abinaya, R. Narmatha, M. Shanmuga Priya, M. Sri Sairam Engineering College Department of Electrical and Electronic Engineering Tamil Nadu India Srm Institute of Science and Technology Department of Computer Science Engineering Delhi India Srm Institute of Science and Technology Department of Physics Delhi India

ISBN: (纸本)9798331508845

The Automated Exoskeleton Prosthetic Suit with Monitoring System aims to develop a fully functional prosthetic suit with coordinated strength and response time. The suit is ameliorative and can be worn or removed as needed. Designed as a wearable inner suit, it operates autonomously, incorporating sensors and navigation systems to reduce inequality. In regions where the suit is worn, it analyzes blood flow using ultrasound Doppler principles and aids in improving circulation. The exoskeletons support or replace impaired body parts, allowing users to function properly. Electrical motors and precise position control form the core of its most common design. This suit explores the entire body using various actuators, tasks, and degrees of freedom. The product can be worn as an inner suit, featuring an autonomous sensor and navigation system to eliminate inequality. It measures blood flow in the regions covered by the suit, enhancing circulation. The primary goal of the product is to detect even small objects and prevent the wearer from falling. © 2024 IEEE.

关键词： Exoskeleton (Robotics)

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Multi lingual Video Summarizer using Natural Language Processing 5

Multi lingual Video Summarizer using Natural Language Proces...

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5th IEEE International Conference for Emerging technology, INCET 2024

作者： Adithya, T. Sai Divya, P. Vamshiteja, N. Prasad, Andraju Bhanu Hariharan, Shanmugasundaram Kukreja, Vinay Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Chitkara University Institute of Engineering and Technology Punjab India

ISBN: (纸本)9798350361155

Throughout the day, a huge quantity of video recordings is made and shared online. Because most films are uploaded with little to no context, it has become quite difficult to spend time watching these kinds of movies in today's hectic lifestyle. Our efforts may also be in vain if we are unable to find any useful information from them. The quantity of videos that need to be made could be directly impacted by the sharp rise in YouTube users' year over year. The likelihood that the makers of the videos will provide inaccurate information about the original content of the film is certainly very high due to their desperation for views. The user's precious time and resources could be wasted in this way. A summary button is one of the user-friendly features of the summarizer Chrome extension, which is designed to further enhance user interaction. This integration of Gensim algorithm with the summarizer Chrome extension streamlines the browsing experience, helping users navigate the overwhelming amount of online video content effectively. When you click this button, the Google Chrome web browser's Chrome extension shows the synopsis of the YouTube video that is currently playing. © 2024 IEEE.

关键词： Video recording

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