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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Arun, Aashna Singh, Rimjhim Padam Kanchan, Sneha Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Universiti Tunku Abdul Rahman Department of Internet Engineering and Computer Science Malaysia

ISBN: (纸本)9798350372120

Ascertaining diagnosis for Coronary Heart Disease (CHD) is a difficult procedure & must be completed precisely within a stipulated period. Numerous researchers have taken a gander at coronary illness risk factors & recommended specific Machine Learning (ML) & Artificial Intelligence (AI) models. This paper attempts to demonstrate the development of various predictive models for the risk of CHD over the next decade using the Framingham dataset with relevant features. The data undergoes thorough preprocessing, including handling missing values, outliers, & duplicates based on which a predictive model was built & evaluated. The work encapsulates essential steps, emphasizing the approach to predicting CHD & providing insights for potential healthcare applications. Attempting to improvise the predictive accuracy the work proposes implementing several ensemble approaches, combining the strengths of multiple models which showcased exceptional performance. Through the ensemble techniques, the individual models were integrated to mitigate weaknesses & enhance overall predictive performance that contributes to a more advanced and accurate prediction of CHD. The various strategic ensemble approaches enhance the reliability of the predictive model, offering a more effective tool for healthcare practitioners in assessing the likelihood of CHD in subjects. © 2024 IEEE.

关键词： Diagnosis

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Fake Social Media Profile Identification and Report Using Machine Learning

Fake Social Media Profile Identification and Report Using Ma...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Subramaniam, Suthashini Sangodiah, Anbuselvan Iftikhar, Aqsa Kiran, Ajmeera Kalsalingam Academy of Research and Education Department of Computer Science and Engineering School of Computing TamilNadu Srivilliputtur India Kampar Malaysia School of Computing Faculty of Computing and Engineering Quest International University Perak Ipoh30250 Malaysia MLR Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331540685

In the digital age, Online Social Networking platforms are integral to daily life, but they also attract malicious bots that mimic human behaviour and pose cybersecurity threats. This literature review focuses on recent Machine Learning-based techniques for detecting and classifying bots on major social media platforms. It covers supervised, semi-supervised, and unsupervised methods, dataset details, and feature categories. While the technology is advancing, an ongoing challenge lies in the creation of ever- evolving bots to evade detection. The study offers insights into the evolving landscape of cybersecurity and the need for continual research in this field, but potential drawbacks may include data privacy concerns, The pervasive influence of the internet and social media as platforms for real-time information dissemination and their potential to shape perceptions and lives. It highlights the concern that unverified and malicious content can significantly impact users' perceptions. The paper offers a taxonomy for classifying malicious information at various stages and presents technologies to combat the issue from content generation to containment. The results illustrate the efficacy of machine learning in improving the dependability and safety of social media platforms, offering a scalable approach to address the increasing problem of online deception. © 2024 IEEE.

关键词： Bot (Internet)

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Energy-Efficient and Latency-Aware Task Offloading for Industrial Cloud-Edge Systems with Heterogeneous CPUs and GPUs

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IEEE Internet of Things Journal 2025年第13期12卷 25757-25772页

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhang, Jia Buyya, Rajkumar Beijing University of Technology College of Computer Science Beijing100124 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China Southern Methodist University Department of Computer Science DallasTX75206 United States Lab School of Computing and Information Systems MelbourneVIC3010 Australia

The unprecedented prosperity of the Industrial Internet of Things has significantly driven the transition from traditional manufacturing to intelligent one. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm to reduce latency and energy consumption for IEs. However, the increasing number of IEs in industrial settings relies on heterogeneous platforms integrated with different processing units, i.e., CPUs and GPUs. To address this challenge, we propose a software-defined networking-based equipment-edge-cloud architecture with three-stage heterogeneous computing. This architecture accurately models the multi-task processing of both scientific and concurrent workflows in real industrial environments. We formulate a joint optimization problem to simultaneously minimize task completion time and energy consumption for IEs. To solve this problem, we design an Improved Two-stage Multi-Objective Evolutionary Algorithm (IT-MOEA). IT-MOEA employs a novel multi-objective grey wolf optimizer based on manta ray foraging and associative learning to accelerate convergence in the early evolution stages and adopts a diversity-enhancing immune algorithm to enhance diversity in the later stages. Simulation results with various benchmarks demonstrate that IT-MOEA outperforms several state-of-the-art single-objective optimization algorithms by an average of 24.7% and multi-objective algorithms by 41.0% in terms of delay and energy consumption. © 2025 IEEE.

关键词： Mobile edge computing

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Ethnicity Prediction From Audio Samples for Different Languages Using Convolutional Neural 5

Ethnicity Prediction From Audio Samples for Different Langua...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Thoguluva, Sharvaani Ravikumar Gopinath, Suraj Udith, S. Singh, Rimjhim Padam Kanchan, Sneha Dept of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Dept of Internet Engineering and Computer Science University Tunku Abdul Rahman Malaysia

ISBN: (纸本)9798350361155

"Ethnicity Prediction from Voice Samples" deals with prediction of a person's ethnicity from his/her voice samples. Ethnicity Prediction has gained more and more importance and attention in fields like security, recommendations systems, learning apps etc. The proposed work uses a customized Convolutional Neural Network (CNN) based model to predict the ethnicity of the speaker based on combination of varied audio features like Mel-Frequency Cepstral Coefficients (MFCC), pitch, energy, and zero crossing rate. The different voice samples are obtained from the popular Speech Accent Archive Dataset. Augmentation of data by performing time and pitch shifting proved to give a significant boost in the accuracy as it increased the number of voice recordings. The work also compares the performance of the proposed model with other state-of-art models like LSTM. The F-score obtained was 98% for the class Russian, Polish and 90% for the class French, Italian, Spanish i.e. Romance Languages and 80% for mixed language category that comprises of Indian, Italian, German and Polish. © 2024 IEEE.

关键词： Long short-term memory

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An Innovative Fake News Detection in Social Media with an Efficient Attention-Focused Transformer Slimmable Network

An Innovative Fake News Detection in Social Media with an Ef...

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2024 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Rane, Rashmi Subhashini, R. Surendran, R. Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai India School of Computer Engineering and Technology Dr. Vishwanath Karad World Peace University Pune India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (数字)9798331533137

ISBN: (纸本)9798331533137

Rapid developments in digital technology have expedited the dissemination of information on social media platforms like as Twitter, Facebook, and Weibo. Unverified information can create protests and mislead the public. It can also cause significant issues for governmental entities and society at large. Unverified content is frequently shared as text, images, audio, or video. This paper introduces a novel Self-Guided Edge Attention-Focused Transformer Slimmable Network improved by Osprey Optimization (SGEAFTSN-OO) for the purpose of identifying fake news. This system operates using the Politifact and Gossipcop databases. Text and visual data are preprocessed using NLP Stemming Self-Guided Filtering (NLPS-SGF). To extract features, the Multilayer Edge Attention with Focused Transformer (MEAFT) is then applied. Lastly, Slimmable Pruned Neural Network with Osprey Optimization (SP-Net-OO) algorithm is employed to identify fake news events. Metrics including accuracy, recall, precision, and F1-score are compared with those of the existing approaches in order to assess the effectiveness of the suggested strategy. The proposed model achieved 99.21% accuracy, 99.15% precision, and 99.12% F1-score based on experimental data. ©2024 IEEE.

关键词： Tweets

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An Ensemble based Convolutional Neural Network modelling for Classifying Medulloblastoma subtype 5

An Ensemble based Convolutional Neural Network modelling for...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Sathwik, T. Reddy, Tadi Sai Vishruth Charitha, Talluri Singh, Rimjhim Padam Kanchan, Sneha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Internet Engineering and Computer Science Universiti Tunku Abdul Rahman Malaysia

ISBN: (纸本)9798350361155

Medulloblastoma a heterogeneous pediatric brain tumor, requires specific subtype identification in order to determine the most effective treatment strategies. To evaluate the accuracy of classification, this study examines state-of-art leading Convolutional Neural Network (CNN), models like ResNet-50, MobileNet, Xception model, ensembles, etc in differentiating between medulloblastoma subtypes using histopathological images. The models undergo rigorous training and fine-tuning using a comprehensive benchmark dataset encompassing a variety of medulloblastoma subtypes. We also employ advanced image pre-processing techniques to optimize feature extraction. Through comparative analyses based on the accuracy, sensitivity, and specificity and precision of each model to uncover their unique strengths and weaknesses, the work identifies the top performing model for medulloblastoma subtypes classification. Then considering unique strengths of these models, we propose an ensemble methodology that combines top performing models for the underlying problem. Through experimentation, our ensemble approach proves to have the highest accuracy in medulloblastoma subtype classification. The combination of ResNet-50's depth, MobileNet's efficiency, and Xception's feature extraction abilities contribute to the ensemble's outstanding performance with 97% of acuuracy and F-score. © 2024 IEEE.

关键词： Convolutional neural networks

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Automation of Text Summarization using Hugging Face NLP 5

Automation of Text Summarization using Hugging Face NLP

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Asmitha, M. Danda, Aashritha Bysani, Hemanth Singh, Rimjhim Padam Kanchan, Sneha Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Internet Engineering and Computer science Universiti Tunku Abdul Rahman Malaysia

ISBN: (纸本)9798350361155

Within the expansive domain of"Natural Language Processing" (NLP), the task of"text summarization" emerges as a foundational element, playing a pivotal role in distilling relevant information from extensive textual corpora. In the digital age, the importance of efficient summarization becomes increasingly critical, given the overwhelming volume of textual information. This comprehensive study delves into the intricacies of both extractive and abstractive summarization techniques, placing a specific focus on transformer-based models like BERT and GPT. These models, celebrated for their remarkable capabilities in context comprehension and coherent summarization, are rigorously evaluated alongside established methods like TF-IDF, TextRank, Sumy, Fine Tuning Transformers, Model-T5, LSTM, greedy, and beam search. The practical implications of text summarization extend across diverse fields, encompassing news stories, academic papers, and social media content, underscoring its broad utility in various domains. This study not only incorporates cutting-edge models but also explores a gamut of evaluation methods to discern the quality of summarization. By intertwining theory and application, this research positions itself at the forefront of evolving summarization approaches, shedding light on the transformative impact on information consumption patterns. The dynamic landscape of summarization methods underscores the need for continuous research and innovation, as technological advancements continue to reshape how individuals access and comprehend information. © 2024 IEEE.

关键词： Quality control

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Thoracic Surgery Outcome Prediction: Harnessing Machine Learning for Post-Operative Life Expectancy Assessment 7

Thoracic Surgery Outcome Prediction: Harnessing Machine Lear...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Rajendran, Adithya Abhishek, S. Sha, Akhbar Anjali, T. Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350340600

Lung cancer, a complex and formidable disease, frequently necessitates surgical intervention as a pivotal aspect of its treatment. Accurate prediction of post-operative life expectancy holds paramount significance in the realm of clinical decisionmaking and patient counselling This study undertook a rigorous comparison of various machine learning algorithms, encompassing Support Vector Classifier (SVC), Logistic Regression, XGBoost, Random Forest, K-Nearest Neighbors (K-NN), and CatBoost. Using an expansive dataset containing clinical and demographic variables collected from lung cancer patients, the research team rigorously assessed the predictive capabilities of these models. The outcome of the research team analysis unveiled exceptional mean accuracy scores across the models, underscoring their utility in this context. Specifically, SVC, Random Forest, and CatBoost consistently exhibited remarkable mean accuracy rates, each reaching 0.864. Logistic Regression also demonstrated robust performance, achieving a commendable mean accuracy of 0.862, while XGBoost and K-NN delivered competitive mean accuracies of 0.846 and 0.84, respectively. These findings underscore the substantial potential of machine learning in furnishing precise predictions regarding post-operative life expectancy. By doing so, these models empower healthcare professionals to make well-informed treatment decisions, paving the path for the development of personalized treatment strategies and the enhancement of patient care in the management of lung cancer. © 2023 IEEE.

关键词： Forecasting

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Vehicle Door Safety System 2

Vehicle Door Safety System

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2nd International Conference on Computational Systems and Communication, ICCSC 2023

作者： Sivasankar, Abilash Kumaran, U.K. Amrita School of Computing Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665493932

Carelessness and absence of mind leading to unseen obstacles or moving objects from passenger or driver inside the vehicle will make way for accidents to happen when they try to open the side door of the vehicle which will damage the door as well as harm any person or animal. The proposed system with the use of a camera and object detection will be able to detect vehicles and stationary objects that might hit the vehicle door when the passenger or driver tries to open the door without being aware of them. The system will alert the person inside the car so they can open the door in safe manner which prevents damage to the door. The object detection YOLO model is custom trained for the proposed system. Then based on this model object detection is performed using raspberry pi and camera setup. The camera is placed on the mirrors to cover both front and back door. The raspberry pi is remotely connected to laptop for visualizing the detection of objects which will damage the door. The system uses the computer vision concept and objects detection for ensuring the safety of vehicle door. © 2023 IEEE.

关键词： Object detection

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Consumer Complaints Classification using Deep Learning & Word Embedding Models 14

Consumer Complaints Classification using Deep Learning & Wor...

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14th International Conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Vinayak, Vineet Jyotsna, C. Department of Computer Science and Engineering Amrita School of Computing Bengaluru India

ISBN: (纸本)9798350335095

The goal of Text classification is to categorize a document into predefined categories. Various supervised and unsupervised classifiers can be used to achieve this. In the research work proposed, SOTA (State of the Art) deep learning models and embedding techniques are used to classify consumers' complaints, which is in form of text, into 6 classes. Words and documents are represented as numeric vectors through embedding, enabling vector representations for related words. These representations are easily ingested by NLP algorithms. The classes represent departments where complaints are routed. Deep learning models like LSTM, Bi-LSTM, GRU and 1D CNN are used, along with word embedding techniques like Word2Vvec, Fasttext, Bert and Distilbert to represent text. The experimental results indicate that DistilBert and CNN achieved a 93% F-score. © 2023 IEEE.

关键词： Embeddings

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