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AI-Driven Personalized Dietary Recommendation system Leveraging Regional Cuisine 3

AI-Driven Personalized Dietary Recommendation System Leverag...

引用

3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Divya, J. Aakash, G. Bevincent Edward, E. St. Joseph’s College of Engineering Chennai India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

Diet related health issues are a growing concern, with individuals often struggling to make informed dietary choices. Existing dietary recommendation systems rarely include regional foods, making it difficult for users to access or prepare the recommended meals. Despite the availability of a diverse array of nutritious local foods, this gap necessitates the development of a system that provides culturally relevant and accessible dietary recommendations. In this project, an AI based dietary recommendation system is designed to cater to regional needs. Using machine learning algorithms, the system calculates daily caloric requirements based on factors such as age, BMI, and individual health goals. Personalized meal plans are generated from regional foods, ensuring they are culturally appealing, nutritionally balanced, and aligned with seasonal availability. The outcome of this research is to bridge the gap in existing systems by generating meal plans rooted in regional foods, empowering users to achieve healthier diets through accessible and culturally resonant recommendations. © 2025 ieee.

关键词： Food ingredients

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A Hybrid Intrusion Detection system using Explainable AI for Enhanced Accuracy and Transparency 3

A Hybrid Intrusion Detection System using Explainable AI for...

引用

3rd international conference on Electronics and Renewable systems, ICEARS 2025

作者： Hemalatha, A. Vimal Kumar, M.N. Graf, F. Twinkle Anlin Sahaya Infant Tinu, M. Pavithra, P. Suresh, R. E.G.S. Pillay Engineering College Department of Information Technology Nagapattinam611002 India Sona College of Technology Department of Mechatronics Engineering Salem636005 India Madanapalle517325 India Rohini College of Engineering and Technology Department of Biomedical Engineering Anjugramam629401 India Tjs Engineering College Department of Computer Science and Engineering Kavaraipettai Peruvoyal Gummidipoondi601201 India Tjs Engineering College Department of Electronics and Communication Engineering Kavaraipettai Peruvoyal Gummidipoondi601201 India

ISBN: (纸本)9798331509675

Intrusion detection system is highly effective and easy to understand in the situation of ubiquitous cyber threats. The trustworthiness and interpretability of traditional intrusion detection systems are balanced due to their dependence on transparent deep learning (DL) and Machine Learning (ML)methods. To overcome these issues, this research proposes a hybrid IDS which makes use of Explainable AI(XAI). An integration of ML methods like random forest and support vector machine with DL methods like CNN ensure efficient detection accuracy in the system. The decision-making process can be better efficient with the help of XAI methods like SHAP and LIME, which increases transparency. The CICIDS 2017 dataset is used to validate the feature extraction of framework, model training and interpretability components. The result show that proposed method performs better with 98.5% accuracy rate and better precision, recall and F1-socre when compared to existing methods. This method provides useful data for securing networks while reducing the number of false positives and negatives. Through the integration of advanced machine learning/deep learning methods with XAI, the proposed IDS ensures dependability, openness and practicality. © 2025 ieee.

关键词： Adversarial machine learning

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Detection of Brain Tumors from MRI scans using Deep Learning Model 14

Detection of Brain Tumors from MRI scans using Deep Learning...

引用

14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Surendra, Yellanuri Ketan, S. Hupesh Naga Naaz, Mohebba Department of CSE G Pullaiah College of Engineering and Technology Andhra Pradesh Kurnool India Department of ECE Ravindra College of Engineering for Women Andhra Pradesh Kurnool India

ISBN: (纸本)9798331531935

The diagnosis of brain tumors dominates the medical sector and great accuracy along with efficiency in their methods to detect the tumor is required. The ability to diagnose brain cancers from MRI data has been greatly aided by recent developments in computer-aided diagnosis (CAD) systems. Recent years have seen improvements in approaches used, for instance with CNN or tumor images being automated. In this work was proposed a model that is based on deep learning using Inception-V3 for the classification and localization of brain tumors. Our model was validated on a publicly available dataset and it was shown that the proposed model is accurate and robust in distinguishing among different kinds of brain tumors. The paper also addresses issues such as data imbalance and model interpretability and the feasibility of integrating the system to be used clinically for real time applications. © 2025 ieee.

关键词： Deep learning

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A MEMS Pressure Sensor Array for Sleep Apnea Recognition and Monitoring Based on The Principle of Traditional Chinese Medicine 38

A MEMS Pressure Sensor Array for Sleep Apnea Recognition and...

引用

38th ieee international conference on Micro Electro Mechanical systems, MEMS 2025

作者： Qin, Lin Cheng, Long Zeng, Xianzhang Xia, Yuanlin Wang, Zhuqing Sichuan University School of Mechanical Engineering China Sichuan University Med + X Center for Manufacturing West China Hospital Chengdu China

ISBN: (纸本)9798331508890

This paper reports a novel MEMS-based pressure sensor array, inspired by traditional Chinese medicine, for detecting and identifying pulse signals. A multi-dimensional pulse acquisition array was developed by emulating traditional Chinese pulse diagnosis techniques to capture pulse signals at the cun, guan, and chi positions along the radial artery. A database was created with pulse signals recorded under normal breathing and breath-holding conditions. These features were used to train a support vector machine (SVM) classification model. The experimental results show that the model achieved high accuracy (99.33%) and sensitivity (98%) in detecting sleep apnea. Overall, this system could offer innovative technical support and promising applications for modernizing traditional Chinese diagnostic methods and developing smart healthcare devices. © 2025 ieee.

关键词： Sleep research

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Multi-Microphone Speech Emotion Recognition Using the Hierarchical Token-Semantic Audio Transformer Architecture

Multi-Microphone Speech Emotion Recognition Using the Hierar...

引用

2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Cohen, Ohad Hazan, Gershon Gannot, Sharon Faculty of Engineering Bar-Ilan University Ramat-Gan Israel

ISBN: (纸本)9798350368741

The performance of most emotion recognition systems degrades in real-life situations ("in the wild" scenarios) where the audio is contaminated by reverberation. Our study explores new methods to alleviate the performance degradation of Speech Emotion Recognition (SER) algorithms and develop a more robust system for adverse conditions. We propose processing multi-microphone signals to address these challenges and improve emotion classification accuracy. We adopt a state-of-the-art transformer model, the Hierarchical Token-semantic Audio Transformer (HTS-AT), to handle multi-channel audio inputs. We evaluate two strategies: averaging mel-spectrograms across channels and summing patch-embedded representations. Our multi-microphone model achieves superior performance compared to single-channel baselines when tested on real-world reverberant environments. © 2025 ieee.

关键词： human-robot interaction speech emotion recognition

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A Deep Learning Based Posture Correction system

A Deep Learning Based Posture Correction System

引用

2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Bhatlawande, Shripad Shilaskar, Swati Mhetre, Mantesh Moralwar, Sandesh Mor, Aayush Muley, Samiran Vishwakarma Institute of Technology Dept of E&tc Engineering Pune India

ISBN: (纸本)9798331509828

A computerized system developed using machine learning to monitor correct posture for an exercise being performed. The posture for exercise is important for the physical benefit of it. The system for detecting exercise is implemented with a combination of a Convolutional Neural Network (CNN) model, Long Short-Term Memory(LSTM) model and Recurrent Neural Networks(RNN) model which is trained on a different set of exercise dataset. The machine learning models identify the exercise type which is being performed in the input source. The posture of the exercise is being estimated using the OpenPose's BlazePose library. The correctness of the exercise is being evaluated by comparing the body joint angles with the predefined pose threshold joint angles and real-time feedback is provided. Additionally, if the posture of the exercise is found correct the repetitions are being counted to ensure accuracy and consistency in the workouts. The system helps in enhancing the exercise performance and minimize the chances of injury by providing precise posture analysis and form correction during exercise routines. © 2025 ieee.

关键词： Convolutional neural networks

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Enhanced Feedback Residual Attention Network with Slanted Scarce Auto-Encoder and IoT Based Industrial Waste Water Treatment system for Effective Waste Management

Enhanced Feedback Residual Attention Network with Slanted Sc...

引用

2025 international conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Prasad, K.V.S. Gavaskar, T. Ravivarman, G. Chinniah, P. Sophia, S. Sathyakala, S. Gmr Institute of Technology Department of Basic Sciences and Humanities Andhra Pradesh Vizianagaram India St. Joseph's College of Engineering Department of Mechanical Engineering Tamil Nadu Chennai India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore India ST. Joseph College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Kanchipuram India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sona College of Technology Department of Management Studies Tamil Nadu Salem India

ISBN: (纸本)9798331509828

In the last few decades, rapid industrialization has increased the problem of wastewater treatment and management because of the increasing complexity and toxicity of wastewater that is difficult to treat by any conventional approach. Commonly used waste water treatment systems are unable to treat a variety of pollutants because of deficiency of good classification and optimization processes which results in wastage of resources and harming the environment. Furthermore, existing systems are inflexible, and an ability to change the nature of treatment in real time is impossible, which is crucial for the timely and efficient delivery and adjustment of therapies. To overcome these challenges, there is a need for smart monitoring systems enabled by IoT and effective treatment processes utilizing machine learning for better waste management. This paper comes up with an IoT and Machine Learning based system for the treatment of industrial wastewater that uses an Enhanced Feedback Residual Attention Network with Slanted Scarce Autoencoder to achieve accuracy in pollutant classification and quantification. Fick's Law Optimization is incorporated for optimization which makes the treatment process more efficient by allowing the treatment to be applied only where necessary and in the right amounts. IoT sensors are also part of the system to ensure that the data is collected in real time and the waste is continuously monitored to allow quick reactions to changes in waste. The introduced approach attains higher accuracy as 99%. © 2025 ieee.

关键词： Wastewater treatment

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Smart Grid Management with AI Driven Analytics in Solar Energy 2

Smart Grid Management with AI Driven Analytics in Solar Ener...

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2nd international conference on Machine Learning and Autonomous systems, ICMLAS 2025

作者： Timande, Devanshi Agade, Nishant Gudadhe, Amit Faculty of Engineering & Technology Department of Artificial Intelligence and Data Science Wardha India Faculty of Engineering & Technology Wardha India

ISBN: (纸本)9798331505745

This paper presents an AI-driven framework for real-time smart grid optimization tailored specifically for solar energy integration. The proposed system features a multi-layered architecture that integrates data acquisition from solar irradiance sensors, smart meters, and meteorological sources with advanced pre-processing techniques to ensure data quality and fusion. Using advanced deep learning architectures, such as Long Short-Term Memory networks for forecasting solar irradiance and regression methodologies for predicting load, our proposed framework dynamically adjusts energy dispatch and demand response measures to address the inherent variability associated with solar power. Additionally, a combined strategy that incorporates reinforcement learning is implemented to improve real-time grid stability and operational efficiency, and explainable artificial intelligence methods are used to provide clarity in decision-making processes. Simulation studies conducted on an enhanced ieee 24-bus test system have shown to reduce forecast error by up to 30% and prevent the occurrence of grid instability events by about 40% in contrast with traditional methods. These results are promising for AI-based analytics since it promises to enhance smart grid functionality and enable more sustainable use of renewable resources. Further research will develop this model with additional renewable energy sources and increase cybersecurity. © 2025 ieee.

关键词： Reinforcement learning

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Machine Learning-Based Crop Recommendation system for Sustainable Agriculture 4

Machine Learning-Based Crop Recommendation System for Sustai...

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4th international conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Sriharsha, Kantheti Chandan Joshi, Kotapuri Rahul Reddy, Vummadi Ravi Sankar Chowdary, Vadlapudi Kalyan Hanuman Hajarathaiah, Koduru Rajak, Shaik School of Computer Science and Engineering VIT-AP University Amaravati India Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Amaravati India

ISBN: (纸本)9798331523923

This research paper addresses the critical challenges in agricultural crop recommendation systems through machine learning approaches. Current systems face significant limitations including limited integration of real-time environmental data, inconsistent prediction accuracy across diverse geographical regions, difficulty in handling multiple conflicting parameters simultaneously, and lack of interpretability in recommendation decisions. Recent techniques like Random Forest, Support Vector Machines, and Deep Neural Networks have attempted to address these issues but with varying degrees of success. This paper proposes an enhanced crop recommendation system that leverages multiple machine learning algorithms to overcome these limitations. The system implements real-time soil and weather parameter integration, while providing interpretable recommendations to farmers. Our approach achieves 99.54% accuracy in crop recommendations, significantly improving upon existing systems. The proposed model serves as a comprehensive solution for sustainable agriculture, contributing to improved crop yield and farmer decision-making processes. The system's effectiveness is validated across different agricultural zones, demonstrating its practical applicability and scalability in diverse farming conditions. © 2025 ieee.

关键词： Adversarial machine learning

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Adaptive Prescribed Time Path Following Controller for Underactuated Surface Vessels 13

Adaptive Prescribed Time Path Following Controller for Under...

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13th international conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Ghommam, Jawhar Saad, Maarouf Zhu, Quanmin Rahman, Mohammed H. Dept. of Electrical and Computer Engineering Sultan Qaboos University Muscat Oman Dept. of Electrical Engineering Ecole de Technologie Superieure Montreal Canada Dept. of Engineering Design and Mathematics University of the West of England Bristol United Kingdom Mechanical Engineering Department University of Wisconsin-Milwaukee Wisconsin United States

ISBN: (纸本)9798331516147

Heading control of marine vehicles is an important issue in navigation and control engineering. This paper investigates the design of a prescribed-time robust adaptive path following control problem of an underactuated surface vessel with parametric uncertainties, unknown disturbances and control input constraints. The establishment of prescribed-time property consists of a prescribed-time regulation of the steering control systems and novel prescribed-time Line of Sight (LOS) guidance control, both of which satisfy the prescribed-time requirement. The feasibility of separate independent control design for the steering and the guidance control systems is guaranteed through the development of new results on nonlinear prescribed-time cascade systems with non-vanishing uncertainties. The Lyapunov method is employed to show that the equilibrium point of the whole system is prescribed-time input to state stable. Numerical simulations are carried out to validate the performance of the proposed control strategy. © 2025 ieee.

关键词： Steering

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