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Wearable Sensor-Based Activity Recognition over Statistical Features Selection and MLP Approach 3

Wearable Sensor-Based Activity Recognition over Statistical ...

引用

3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Nazar, Fakhra Jalal, Ahmad Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9798331504861

Supervising and examining the physical activities of different individuals by using wireless sensors is beneficial in favor of recognizing postural positioning and moving actions in the everyday ambiance. Accelerometers have been exercised in intelligent environments to identify human activities for many years. Using these accelerometer sensors and human activity recognition systems, people going through many sicknesses and illnesses can be proficiently scrutinized, and thus remedial medication can be dispensed in a well-timed approach. In this research paper, a naïve, and intricate technique founded on certain time domain features on the way to classify the physical activities is suggested. Feature extraction is a momentous step in HAR, and in this study, six statistical features were obtained from the preprocessed data. To further optimize the feature set, the Binary Grey Wolf Optimizer (GWO) was integrated. Further, a Multilayer Perceptron (MLP) was utilized for activity recognition to distinguish between the six activity classes. MLP has the advantage of finding non-linear patterns in data, the algorithm possesses powerful features of reducing overfitting in the data and good generality. It also offers resistance to noises usually experienced in the sensor data. Experimental results evaluated that the proposed framework achieved 87.14% recognition accuracy. This validates that the suggested framework can be used to observe physical activities and discriminate between healthy and unhealthy patients. ©2024 IEEE.

关键词： Wearable sensors

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Stomach Cancer Prediction and Detection using Deep Learning: A Review 2

Stomach Cancer Prediction and Detection using Deep Learning:...

引用

2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Kaur, Sharandeep Goel, Paurav Kapoor, Nitika Chandigarh University dept. of Computer Science Engineering Mohali India

ISBN: (纸本)9798331505745

Stomach cancer, sometimes referred to as gastric cancer, is still one of the most common and widespread deadly disease worldwide offers a significant challenge in oncology due to late detection and high mortality rates. The advent of deep learning has revolutionized stomach cancer prediction by leveraging sophisticated models such as convolutional neural net-works (CNNs), multimodal learning and hybrid frameworks that integrate multiple data sources like medical imaging, histological slides, clinical records and genetic profiles and provide better performance with high accuracy. This review comprehensively ex-plores recent advances in deep learning applications for the detection of stomach cancer, highlighting their performance, accuracy, and clinical implications. Comparative analysis demonstrates that different deep learning models, such as ResNet50, DenseNet121, EfficientNetB5, and MobileNetV2, provide impressive results with accuracy up to 99% and higher sensitivity and specificity. It also indicates that the system accuracy needs to be improved to 100% in order to detect stomach cancer as early as possible. Early detection helps in the stoppage of further spreading of the disease and lowers the death rates. © 2025 IEEE.

关键词： Convolutional neural networks

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Robust Machine Learning Approach to Plant Species Classification for Sustainable Agriculture 3

Robust Machine Learning Approach to Plant Species Classifica...

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3rd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2024

作者： Mumtaz, Sara Jalal, Ahmad Dept. of Computer Science Air University Islamabad Pakistan

ISBN: (纸本)9798331504861

Sustaining resilient agricultural practices is essential for the overall yield because it guarantees both economic stability and food supplies. By incorporating Zero-Shot learning into a strong leaf classification architecture, our method meets this demand. We optimized an Al-powered four-step categorization process using the Bee Colony Algorithm. To pre-process, segment, extract features from, and classify leaf image data, zero-shot learning is employed. Pre-processing, which includes histogram equalization and contrast stretching, is followed by morphological segmentation. In feature extraction, two methods are used: Harris corner detection and SURF (Speeded Up Robust Features). Zero-Shot learning for classification is optimized using the Bee Colony Algorithm. At 92.73% accuracy, the suggested method surpasses out previous methods like CNN and SVM, suggesting that it has the potential to enhance crop quality and disease control in agriculture. ©2024 IEEE.

关键词： Zero-shot learning

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Robust Object Recognition with Genetic Algorithm and Composite Saliency Map 5

Robust Object Recognition with Genetic Algorithm and Composi...

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5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Ahmed, Muhammad Waqas Jalal, Ahmad Air University Dept. of Computer Science Islamabad Pakistan

ISBN: (纸本)9798350394764

Object detection and recognition have emerged as crucial research areas, driven by the dire need to address the challenges posed by identifying and recognizing objects in diverse domains. Accurate object identification and recognition is required for tasks such as autonomous navigation, video surveillance, precision agriculture, and medical imaging. We propose a method based on a unique combination of machine learning techniques for object recognition. The proposed approach starts by applying K-means segmentation to the input image to cluster similar regions and colors. Next, a composite saliency map is generated based on the K-means segmentation output. Subsequently, the technique of extracting objects from the saliency map is accomplished through the employment of the connected pixel object extraction method. Ultimately, the Genetic Algorithm is utilized to optimize decision tree classifier for the purpose of object recognition. The MSRC dataset was utilized to assess the proposed approach, resulting in an accuracy of 81.5%, accompanied by a precision of 83.3% and recall of 86.1%. The results depicts that the our approach is highly valuable for accurate object categorization, wherein the utilization of Genetic Algorithm significantly enhances the precision of the classification. © 2024 IEEE.

关键词： Genetic algorithms

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Understanding the Evolution in Tourist Behavior Patterns through Context-Aware Spatio-Temporal k-Means

Understanding the Evolution in Tourist Behavior Patterns thr...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Belussi, Alberto Vecchia, Anna Dalla Gambini, Mauro Migliorini, Sara Quintarelli, Elisa University of Verona Dept. of Computer Science Verona Italy

ISBN: (纸本)9798350362480

Understanding tourist behavior patterns is crucial for developing effective recommendation and decision support systems. The behaviors are often captured through the trajectories followed by tourists during their journeys or the sequences of visited Points of Interest (PoIs). Identifying common patterns and tracking their evolution over time can enhance the ability to understand, predict, and influence tourist choices, ultimately supporting goals like promoting specific destinations and fostering sustainable visitation patterns. Clustering algorithms like k-Means are commonly used to extract frequent patterns, requiring a tailored distance metric suited to the task. Since tourist trajectories combine spatial, temporal, and semantic features, defining a distance function that accurately captures these multifaceted aspects is essential. This paper examines various methods for encoding trajectory data and explores their effects on the clustering process. Finally, we compare and validate their suitability by using a real-world dataset of visits performed by tourists in Verona (Italy) from 2014 to 2022. © 2024 IEEE.

关键词： Spatio-temporal data

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Heart Disease Risk Assessment using Bayesian Networks

Heart Disease Risk Assessment using Bayesian Networks

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2025 International Conference on Artificial Intelligence and Data Engineering, AIDE 2025

作者： Reddy Karimireddy, Sai Sandeep Reddy, L Venkata Ramana Hemanth, Edam Srinivas, G Luhya Srinivas, M. Amrita School of Computing Dept. Computer Science Vijayawada India

ISBN: (纸本)9798331527518

This paper uses Bayesian Networks to display an extensive approach to heart disease risk assessment. Cardiovascular diseases (CVDs) are one of the main leading causes of death worldwide, making up 32% of all global fatalities [1]. We developed and analyzed different types of Bayesian Network models to predict and estimate the risk of heart disease based on patient data [8]. Our approach includes domain knowledge, structure learning algorithms, and feature selection techniques to create an accurate and understandable model. The final model achieves an ROC AUC score of 0.846, displaying its capability in risk prediction. We also explore practical applications of the model for medical decision support [2]. © 2025 IEEE.

关键词： Risk assessment

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Implementation of Fuzzy Logic Model to Solve Traffic Congestion Problem at Road Intersections

Implementation of Fuzzy Logic Model to Solve Traffic Congest...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Negi, Vinay Jha, Sundaram Kumar Behl, Rachna Mriirs Dept. of Computer Science & Engineering Faridabad India

ISBN: (纸本)9798350382723

This research aims to tackle traffic congestion at intersections, primarily caused by static traffic light timings that don't adapt to changing traffic conditions. The proposed solution involves using adaptive fuzzy controllers, which can adjust their decisions based on fuzzy IF-THEN rules. These controllers employ both Mamdani and Sugeno fuzzy logic. The Mamdani method considers factors like the number of vehicles, queue length, emergency vehicle access, and road width to optimize traffic light timings and reduce unnecessary green signals. The Sugeno-type fuzzy logic enhances controller efficiency and decreases waiting times. The study also explores the application of direct adaptive fuzzy controllers to manage unstable systems, which is relevant in optimizing traffic control. Ultimately, the goal is to develop a simulator capable of predicting the right traffic light timings in real-time. This involves extending green light timings for lanes with high vehicle volumes and also for the emergency vehicles to alleviate congestion at intersections. © 2024 IEEE.

关键词： Traffic congestion

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Hybrid Bi-LSTM and CNN for Clickbait News Detection on Indonesia News Media

Hybrid Bi-LSTM and CNN for Clickbait News Detection on Indon...

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2024 International Conference on Information Technology Systems and Innovation, ICITSI 2024

作者： Helen, Afrida Suryana, Ino Fernanda, Anne Audistya Padjadjaran University Dept. Computer Science Sumedang Indonesia

ISBN: (纸本)9798331511470

Clickbait is a special trick used to make readers open a news page repeatedly. In a negative meaning, clickbait is a trap visit deliberately made by the holder of a news media. The goal is to get profit from the number of pages visited. Sometimes what the news topic mentioned in the title, do not described in the content. To know this trap, this study proposes a model that can detect whether there is clickbait or not in a news article. This research uses the supervised learning method and take primary data from one of the most popular news media in Indonesia language. To get the characteristic of clickbait, we conducted simple research on each candidate dataset. We found six most influential characteristic that distinguish news containing clickbait or not. The number of data set each is 3,915 for news titles and also for content. Then put them into one as training dataset. The title is in the first sentence of the news content. In addition, we propose a hybrid deep learning method CNN- BiLSTM. The CNN method extract features of dataset and Bi- LSTM build a prediction model. We compare our model with pure Bi-LSTM. We test the models with several pattern of parameter: window size = 5, Bi-LSTM layers = 2, dropout rate = 0.3, f(Adamax), and batch size = 32. The F1-score of CNN- BiLSTM better then pure BiLSTM. © 2024 IEEE.

关键词： Deep learning

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Feathered Prognostics: A Deep Dive Into Poultry Disease Classification Models

Feathered Prognostics: A Deep Dive Into Poultry Disease Clas...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Reddy, Kotte Sai Dhanush Reddy, V. Keshav Guru Prasad, M.S. Yadav, Sahil Joshi, Prajjwal Pranay, Kanuri Dept. of Computer Science and Engg. Dehradun India

ISBN: (纸本)9798350379716

This study has introduced a pioneering endeavor in the realm of poultry farming, which aims to mitigate the burgeoning challenges of disease detection. With poultry farming on the rise, there is a concurrent escalation of the transmission of infectious maladies among poultry, posing not only economic ramifications but also a potential threat to human health. Manual inspection for poultry diseases is both labor-intensive and time-consuming. To address all these pressing concerns, the proposed work endeavors to construct a sophisticated deep-learning model that is proficient in disease detection within poultry populations, leveraging cutting-edge technologies such as CNNs in conjunction with an efficient net B3 optimizer. The developed model showcased exceptional performance metrics with a training loss of 0.524311 and an accuracy of 99.67%. Furthermore, rigorous validation and testing yielded robust results, demonstrating a validation accuracy of 96.90% and a testing accuracy of 97.52%. Through the amalgamation of state-of-the-art deep learning methodologies, this research project aspires to revolutionize disease identification in poultry. © 2024 IEEE.

关键词： Convolutional neural networks

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Enhancing Robustness in Audio Visual Speech Recognition: A preprocessing approach with Transformer and CTC Loss

Enhancing Robustness in Audio Visual Speech Recognition: A p...

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2024 International Conference on Advances in Modern Age Technologies for Health and Engineering science, AMATHE 2024

作者： Ithal, Bharath N. V. Lagan, T.G. Sudheer, Rhea Swathi Rupali, N.V. Mamatha, H.R. Pes University Dept. Of Computer Science Bengaluru India

ISBN: (纸本)9798350371567

Audio Visual Speech Recognition (AVSR) is a promising technology for speech recognition that is more robust to noise and other challenging conditions than traditional Audio Speech Recognition (ASR). AVSR systems work by fusing audio and visual features using machine learning algorithms. One of the key challenges in AVSR is developing effective fusion methods. AVSR systems are currently being used in many different applications, having made great strides in recent years despite these obstacles. In this paper, we introduce a preprocessing technique applied on a Transformer model with Connectionist Temporal Classification (CTC) loss that increases the robustness of the AVSR model against noise. We preprocess the training audio clips by adding noise to it, resulting in significant reductions in word error rates and a more efficient training process. The project significantly advances in the field of audio-visual speech recognition and highlights the potential for practical applications, particularly in noisy and challenging environments. © 2024 IEEE.

关键词： Speech recognition

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