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IEEE Students' Conference on electrical, Electronics and computer science (SCEECS)

作者： Dipesh Dwivedi Sajjad Ahmed Adarsh Patel H Azath Dept. of Computer Science and Engineering VIT University Sehore India

The proliferation of phishing URLs has experienced rapid growth in recent years, necessitating urgent attention to phishing attack detection in cybersecurity. In response, we introduce an improved predictive model that leverages both machine learning and a Deep Learning Model. Our dataset consists of 88,674 instances, encompassing 112 features, including the class level. Notably, this dataset comprises 58,000 legitimate instances and 30,647 phishing *** proposed method incorporates six distinct algorithms: Decision Tree, Support Vector Machine, K-nearest neighbors, Logistic Regression, Ensemble Classifier, and Neural Network algorithm. we systematically evaluated total 28 models performance with different types of feature changes. Following enhancements, every algorithm demonstrated an accuracy performance surpassing 93.16%. Among them, the Ensemble Classifier emerged as the most effective, boasting an accuracy 97.45%, MCC 94.37%, precision a96.35%, and an F1 Score 96.31%.

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Adaptive Attention-Based Model for 5G Radio-based Outdoor Localization

arXiv

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arXiv 2025年

作者： Yaman, Ilayda Tian, Guoda Tufvesson, Fredrik Edfors, Ove Zhang, Zhengya Liu, Liang Dept. of Electrical and Information Technology Lund University Sweden Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States

Radio-based localization in dynamic environments, such as urban and vehicular settings, requires systems that can efficiently adapt to varying signal conditions and environmental changes. Factors such as multipath interference and obstructions introduce different levels of complexity that affect the accuracy of the localization. Although generalized models offer broad applicability, they often struggle to capture the nuances of specific environments, leading to suboptimal performance in real-world deployments. In contrast, specialized models can be tailored to particular conditions, enabling more precise localization by effectively handling domain-specific variations and noise patterns. However, deploying multiple specialized models requires an efficient mechanism to select the most appropriate one for a given scenario. In this work, we develop an adaptive localization framework that combines shallow attention-based models with a router/switching mechanism based on a single-layer perceptron (SLP). This enables seamless transitions between specialized localization models optimized for different conditions, balancing accuracy, computational efficiency, and robustness to environmental variations. We design three low-complex localization models tailored for distinct scenarios, optimized for reduced computational complexity, test time, and model size. The router dynamically selects the most suitable model based on real-time input characteristics. The proposed framework is validated using real-world vehicle localization data collected from a massive MIMO base station (BS), demonstrating its ability to seamlessly adapt to diverse deployment conditions while maintaining high localization accuracy. Copyright © 2025, The Authors. All rights reserved.

关键词： NP-hard

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UAV Platform for Real-time Indoor Mapping

UAV Platform for Real-time Indoor Mapping

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Innovations in High Speed Communication and Signal Processing (IHCSP), International Conference on

作者： Spruha Nitin Kshirsagar Abhishek Rawat Ravi Bhandari Dept. of Computer Science and Engineering Visvesvaraya National Institute of Technology Nagpur India Dept. of Electrical and Computer Science Engineering Institute of Infrastructure Technology Research and Management (IITRAM) Ahmedabad India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

SLAM enables Robots and UAVs to effectively understand unknown surroundings and estimate their own position within those environments. Recent advancements in 2D mapping have significantly increased its popularity for navigation tasks, primarily because it is easier to generate and requires less computational power compared to 3D mapping. This paper provides a comparison of two widely used ROS-based 2D SLAM libraries: Google Cartographer and Hector SLAM, deployed on a custom built UAV that uses 2D LiDAR, and finds Hector SLAM to be more feature rich as well as computationally efficient.

关键词： Cartography Technological innovation Simultaneous localization and mapping Laser radar Three-dimensional displays Signal processing Autonomous aerial vehicles Real-time systems Internet Robots

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A Bengali Physics Chatbot Utilizing Sequence-to-Sequence Model with Beam Search

A Bengali Physics Chatbot Utilizing Sequence-to-Sequence Mod...

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Computing, Applications and Systems (COMPAS), IEEE International Conference on

作者： Javed Hossain Dept. of Computer Science & Engineering City University Dhaka Bangladesh

ISBN: (数字)9798331529765

ISBN: (纸本)9798331529772

Chatbots, also known as talkbots or interactive agents, are software applications designed to facilitate communication between humans and machines. While most students in Bangladesh currently waste their valuable time engaging in casual conversations with each other, using a Bengali educational chatbot for such interactions would provide them with a more beneficial use of their time by providing hand note-wise answers for physics. The challenge faced by a Bengali chatbot lies in the necessity of creating a dataset in Bengali, given the scarcity of available Bengali datasets online. We have developed a physics-focused educational chatbot, akin to personalized hand notes, offering comprehensive answers to aid students in learning and revising complex physics concepts. The development of chatbots involves diverse techniques, including rule-based systems, machine learning-based approaches, and generative models. Our proposed system implements the seq2seq model with a beam search decoding algorithm, featuring BiLSTM layers for enhanced context understanding and efficient exploration of potential output sequences. This approach outperforms traditional seq2seq models, ensuring higher accuracy and delivering high-quality responses. Finally, we obtained the highest BLEU1, BLEU2, BLEU3, and BLEU4 scores, respectively, of 0.52, 0.41, 0.29, and 0.23.

关键词： Hands Accuracy Machine learning algorithms Social networking (online) Oral communication Predictive models Chatbots Software Decoding Physics education

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Evaluating Online Sexism Detection: A Comparative Study of Machine Learning Models using the EDOS Dataset

Evaluating Online Sexism Detection: A Comparative Study of M...

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International Conference for Convergence of Technology (I2CT)

作者： Priom Deb Asibur Rahman Bhuiyan Habiba Mahrin Marzanul Momenine Md. Saharan Evan Md. Sajid Ullah Sohan Md. Tazfiq Khan Dept. of Computer Science and Engineering BRAC University Dhaka Bangladesh

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Online sexism refers to gender-based discrimination and harassment that occurs in online spaces, such as social media platforms, online communities, and forums. Machine learning models can recognize and lessen online sexism by automatically detecting sexist content in social media posts. In this experimental analysis, we have evaluated the performance of five different machine learning models, including Logistic Regression, Gaussian Naive Bayes, Decision Tree Classifier, Support Vector Machine (SVM), and KNeighbors Classifier. Our objective was to detect online sexism using the Explainable Detection of Online Sexism (EDOS) data set. We preprocess the data set by cleansing the text data with a regular expression, removing null values, removing redundant columns, and vectorizing it with TfidfVectorizer. The results of our study indicate that the Logistic Regression, Gaussian Naive Bayes, Decision Tree Classifier, and Support Vector Machine (SVM) models are efficacious in detecting occurrences of online sexism. Nonetheless, the KNeighbors Classifier algorithm shows comparatively lower accuracy in this aspect. The present analysis highlights the capacity of machine learning models to identify instances of online sexism. The highest accuracy score we obtained for the Support Vector Machine (SVM) model is 94.64%.

关键词： Support vector machines Logistic regression Analytical models Machine learning algorithms Social networking (online) Null value Machine learning

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Leveraging Virtual Reality and AI Tutoring for Language Learning: A Case Study of a Virtual Campus Environment with OpenAI GPT Integration with Unity 3D

arXiv

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arXiv 2024年

作者： Adithya, T.G. Abhinavaram, N. Srinivasa, Gowri Dept. Computer Science & Engineering PES University Bangalore India

This paper presents a new approach to multiple language learning, with Hindi the language to be learn’t in our case, by using the integration of virtual reality environments and AI enabled tutoring systems using OpenAI’s GPT api calls. We have developed a scenario which has a virtual campus environment using Unity which focuses on a detailed representation of our university’s building's 11th floor, where most of the cultural and technological activities take place. Within this virtual environment that we have created, we have an AI tutor powered by OpenAI's GPT model which was called using an api which moves around with the user. This provided language learning support in Hindi, as GPT is able to take care of language translation. Our approach mainly involves utilising speech to text, text to text conversion and text to speech capabilities to facilitate real time interaction between users and the AI tutor in the presence of internet. This research demonstrates the use of combining VR technology with AI tutoring for immersive language learning experiences and provides interaction. Copyright © 2024, The Authors. All rights reserved.

关键词： Virtual environments

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Topic Relation Graph Construction to Identify Challenges in Introducing Programming in Indonesian K-12 Education 1

Topic Relation Graph Construction to Identify Challenges in ...

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1st IEEE International Conference on Smart Technology, ICE-SMARTec 2023

作者： Ayub, Mewati Karnalim, Oscar Toba, Hapnes Wijanto, Maresha Caroline Dulimarta, Hans Saputra, Williyandie Kotambunan, Tabitha Emanuella Master Program of Computer Science Maranatha Christian University Bandung Indonesia Ewha Womans University Dept. of Computer Science and Engineering Seoul Korea Republic of School of Computing Grand Valley State University MI United States Informatics Engineering Maranatha Christian University Bandung Indonesia

ISBN: (纸本)9798350306156

Programming can help K-12 students to develop their 21st-century core skills. Despite the benefits, programming is not common to be delivered in Indonesian K-12 education. There is a need to understand potential challenges in introducing programming to K-12 students. We developed a questionnaire survey covering four identified dimensions of challenges: administrative, facilities, teachers, and students. We also asked about common programming assessments and their preferred software features for teaching programming. Forty K-12 teachers were invited to complete the survey. The responses were analyzed with thematic analysis using a bigram-based Latent Dirichlet Allocation topic modeling and descriptive statistics. Our study shows that the challenges include limited learning modules, an insufficient number of computers, limited programming skills, and limited computational thinking skills. Scratch was the most common programming language used and many programming assessments were about debugging a program or writing a small program. Visualization and animation can be helpful in teaching programming. © 2023 IEEE.

关键词： Students

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Vehicle Tracking Using Lane Cameras for Personalized Parking Guidance

Vehicle Tracking Using Lane Cameras for Personalized Parking...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Chun-Yao Zheng Chi-Yi Lin Dept. Computer Science and Information Engineering Tamkang University Taipei Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

Real-time monitoring of vehicle movements within a parking lot is crucial for creating a personalized parking guidance service. This applied research proposes using lane cameras to monitor vehicle dynamics, employing YOLOv8-based license plate object tracking and character recognition to determine the real-time location and movement direction of vehicles, while achieving cross-camera object tracking capabilities. With this critical information, the parking management system can provide tailored parking guidance instructions for each vehicle, reducing the burden on drivers to locate available parking spaces and contributing to lower carbon emissions.

关键词： Space vehicles Object detection Carbon dioxide Cameras Real-time systems Object tracking Character recognition Vehicle dynamics Monitoring License plate recognition

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Enhancing Vision Models for Text-Heavy Content Understanding and Interaction

arXiv

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arXiv 2024年

作者： Adithya, T.G. Adithya, S.K. Bharadwaj, Abhinav R. Abhiram, H.A. Narayan, Surabhi Dept. Computer Science & Engineering PES University Bangalore India

Interacting and understanding with text heavy visual content with multiple images is a major challenge for traditional vision models. This paper is on enhancing vision models' capability to comprehend or understand and learn from images containing a huge amount of textual information from the likes of textbooks and research papers which contain multiple images like graphs, etc and tables in them with different types of axes and scales. The approach involves dataset preprocessing, fine tuning which is by using instructional oriented data and evaluation. We also built a visual chat application integrating CLIP for image encoding and a model from the Massive Text Embedding Benchmark which is developed to consider both textual and visual inputs. An accuracy of 96.71% was obtained. The aim of the project is to increase and also enhance the advance vision models' capabilities in understanding complex visual textual data interconnected data, contributing to multimodal AI. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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A Review on Cosmetic Product Recommendation Using Deep Learning

A Review on Cosmetic Product Recommendation Using Deep Learn...

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Soft Computing for Security Applications (ICSCSA), International Conference on

作者： Ruchika Chouhan Snehlata Barde Dept. Of Computer Science Engineering Sage University Indore India

ISBN: (数字)9798331515720

ISBN: (纸本)9798331515737

Cosmetic products play an important role in every one’s life. Cosmetic products also called as makeup are substance that is applied on the body especially on face. The modern lifestyle can be demanding, leaving little time for self-care. However, it’s crucial to prioritize self-care, including skincare, despite hectic schedules. Indeed, relying solely on best-seller products or in-store recommendations may not always be effective in determining compatibility between a skincare product and an individual’s skin condition. This is because everyone’s skin tone and type of the skin is unique, with varying sensitivities, concerns, and needs. Many people buy their goods online today, using various e-commerce sites. So, to choose the best skincare product during online purchasing is difficult, since it depends entirely on skin type and usage. The main aim of this paper is to survey and explores the existing recommendation systems designed to assist users in selecting cosmetics personalized to their unique skin type, by using advanced algorithms and machine learning. This study has reviewed various methods like face recognition, cosmetic product recognition, tag recommendation, facial skin image classification using convolution neural network, a new examples-rules guided deep neural network approach. Through a comprehensive review of literature and existing research, this paper examines the methodologies, algorithms, and techniques employed in cosmetic recommendation systems. Key factors such as user preferences, skin type, skin concerns, and product attributes are analysed to understand how recommendation systems use this information to generate personalized recommendations.

关键词： Deep learning Machine learning algorithms Convolution Reviews Face recognition Artificial neural networks Skin Classification algorithms Usability Recommender systems

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