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Intelligent Image Captioning with InceptionV3, LSTM, and PySpark Integration 3

Intelligent Image Captioning with InceptionV3, LSTM, and PyS...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Kurian, Thanu Mathew, Jimsha K. Syam Dev, R.S. Anand, P.K. Vignesh, A. Rakshith, B.S. New Horizon College of Engineering Dept of Artificial Intelligence and Machine Learning Bengaluru India

ISBN: (纸本)9798331515911

Image captioning is a challenging task in artificial intelligence that involves generating descriptive captions for images automatically. In this project, we propose a novel approach leveraging advanced technologies such as PySpark, LSTM, and InceptionV3 to develop an effective image captioning system. We harness the power of PySpark, a distributed computing framework, to efficiently process large-scale image datasets and extract high-level features from images using the InceptionV3 convolutional neural network (CNN) model. These features capture the semantic information present in the images and serve as input to the caption generation model. The caption generation model utilizes LSTM neural network as the decoder component. LSTM is well-suited for sequential data processing and is capable of generating coherent and contextually relevant captions based on the extracted image features. The InceptionV3 model acts as the encoder, extracting meaningful visual features from input images, while the LSTM decoder generates captions by decoding these features into natural language descriptions. This multimodal approach enables the model to understand and describe the content of images accurately. Through experimentation and evaluation on diverse image datasets, our proposed system demonstrates promising results in generating accurate and human-like captions for a wide range of images. By integrating PySpark, LSTM, and InceptionV3, our image captioning system achieves state-of-the-art performance, highlighting the effectiveness of leveraging advanced technologies in solving complex AI tasks. © 2025 ieee.

关键词： Convolutional neural networks

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Design and Computational Study of a Two Dimensional Photonic Crystal Based Ethanol sensor 3

Design and Computational Study of a Two Dimensional Photonic...

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3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Sreekanth, D. Soma, Savita Sonth, Mahesh V. Guru Nanak Dev Engineering College Bidar Visvesvaraya Technological University Karnataka Belagavi India CMR Technical Campus Hyderabad India Guru Nanak Dev Engineering College Department of Ece Bidar India Secs Jspm University Department of E&tc Pune India

ISBN: (纸本)9798331508456

This research article provides a computational study of light interaction with two dimensional photonic crystal (PhC) structure. This PhC structure is used to measure ethanol percentage in liquid water by identifying and computing mid gap wavelength shift. Analysis of the Maxwell's equations that govern the photon behaviour in the periodic structure is discussed in detail for PhC structures. The photonic band gap (PBG) which prohibits the photon momentum in certain directions in the crystal is computed using plane wave expansion (PWE) method by deriving solutions to the eigenvalue equation. A two dimensional (2D) PhC structure is designed by taking 18 × 17 number of holes on a dielectric slab. This holes on the dielectric configuration is used to sense the ethanol level in liquid water. The PBG width for the design in transverse electric mode is [ωa/2πc] ranging from 0.3841 to 0.4428. The band gap size Δλ 480 nm and mid gap wavelength λc= 2391.2 nm is achieved from the structure. The sensor achieves a sensitivity of 10645.24 nm/RIU and a transmission ratio more than 94%. This structure is a suitable candidate for ethanol sensing applications as it offers a wide bandgap for light propagation. © 2025 ieee.

关键词： Eigenvalues and eigenfunctions

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Design and Development of Smart Monitoring System for Reducing Early Chicken Mortality 3

Design and Development of Smart Monitoring System for Reduci...

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3rd international conference on Intelligent systems, Advanced computing, and Communication, ISACC 2025

作者： Oeishee, Moslema Hoque Ahammad Muyeed, Munyem Mehedi, Rejoan Nayan Molla, Md Alvi, Mishkat Brac University Electrical and Electronic Engineering Department Dhaka Bangladesh United International University Computer Science and Engineering Department Dhaka Bangladesh

ISBN: (纸本)9798331523893

This paper presents an IoT-based autonomous farming method for lowering the mortality rate of 7-10-day-old chickens by preventing the flow of infection and continuously monitoring and controlling some very pivotal environmental parameters. In this system, an ESP32 microcontroller is used as the central unit which manages and controls all the sensors, ensuring healthier environmental conditions within the farm. The system uses several sensors like the temperature and humidity sensor DHT11. To create a user-friendly interface, real-time data is displayed via a 16x2 LCD monitor presenting the consumers with instant status of the crucial parameters. Moreover, this system will integrate two specialized robotic arms, identifying inactive or dead chickens and separating them to a designated place from the hatching area. This is proven to be significantly efficient in minimizing infection rates and ensuring a healthier surrounding for the remaining chickens. The incorporation of a buck converter allows high-power sources to step down so that low-power devices can run smoothly. In the implementation phase, we bought 250 chickens which from 222 chickens survived yielding a favorable cost benefit ratio of 0.48 indicating the economic validity of the system. The results of this study show that advanced sensor systems and robotic technology can be used in poultry farming to improve farm productivity reducing mortality rates. © 2025 ieee.

关键词： Robotic arms

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GCN-Integrated Multi-Agent Reinforcement Learning (MARL) with Modified Firefly Algorithm for Cross-Layer Optimization in Cognitive sensor Networks 3

GCN-Integrated Multi-Agent Reinforcement Learning (MARL) wit...

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3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Jakranpally, Karthik Babu, Nagabathula Ramesh Babu, Doddavarapu Sreenu Rakesh, K. Barve, Amit Shelke, Atmaram Valiant It Services 4000 Stanwick ave SacramentoCA95835 United States J.B. Institute of Engineering & Technology Department of Electronics and Communication Engineering Hyderabad India Bharatratna Indira Gandhi College of Engineering Department of Electronics and Communication Engineering Solapur India Malla Reddy Engineering College for Women Department of Electronics and Communication Engineering Hyderabad India Parul University Parul Institute of Technology Faculty of Engineering and Technology Department of Computer Science and Engineering P.O.Limda Gujarat India Pune India

ISBN: (纸本)9798331508456

CSN s are integrating cognitive abilities, making it challenging to achieve optimized cross-layer performance with limited resources. Non-coordinated techniques typically demonstrate lower proficiency in dynamic spectrum management and network synchronization. We introduce Graph Convolutional Networks (GCN) and Multi-Agent Reinforcement Learning (MARL) in our architectural solution, with the help of the specially designed Firefly Algorithm. The framework implements a distributed learning process, utilizing GCN for local topology perception and MARL for cooperative spectrum and optimal routing decisions. The results of experimental analysis show large gains compared to traditional methods. The proposed model provided a 94.2% recognition rate in spectrum hole identification, an 89.7% enhancement in network throughput, and a 78.5% diminution in end-to-end delay. The energy efficiency surpasses baseline techniques by 67.3%, while the raw packet delivery reliability remains at 92.8%. Integrating GCN with MARL and the improved Firefly Algorithm can significantly improve cross-layer optimization, offering a promising solution for the next generation of sensor networks. © 2025 ieee.

关键词： Reinforcement learning

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Patient Health Internet of Things Wearable Device sensor with Machine Learning for Tracking System 3

Patient Health Internet of Things Wearable Device Sensor wit...

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

作者： Nancy, R. Jonathan, R. Moorthy, Jagadeesan Latha, R. Thangavelu, Raja Srm Institute of Science and Technology Srm College of Pharmacy Kattankulathur Chengalpattu India Srm Institute of Science and Technology Srm College of Pharmacy Department of Pharmacy Practice Kattankulathur Chengalpattu603203 India Department of Commerce Nagapattinam India University of Technology and Applied Sciences College of Engineering and Technology Shinas Oman

ISBN: (纸本)9798331509675

Monitoring of patients and updates on their health status are important, especially in pandemic situations. Hospitalization in India is very costly for locals with average incomes because medical equipment is very costly and the population is also increasing, thus reducing the number of hospital beds. Moreover, hospitals in rural areas are mostly far from the locations where patients live. To overcome such challenges, this paper suggests an efficient and economical IoT-based system with sensors and a mobile application to monitor patients' health information from their location continuously. Doctors can also access real-time updates of patients' health through the mobile application. Utilizing improved machine learning processes for better accuracy and real-time response, the current research brings forth a new wearable IoT-integrated device targeted for stuttering monitoring and correction. The gadget combines different hardware and software elements, such as sensors, microcontrollers, and communication protocols, to accurately record the patient's health status. © 2025 ieee.

关键词： Electronic health record

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Predictive Analytics in Mental Health: Machine Learning Models for Major Depressive Disorder Detection using sensor Data 3

Predictive Analytics in Mental Health: Machine Learning Mode...

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

作者： Padmakala, S. Gopukumar, S.T. Saveetha University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai602 105 India Saveetha Medical College and Hospital Tamil Nadu Chennai602 105 India

ISBN: (纸本)9798331509675

The recent integration of Machine Learning (ML) and sensor technologies in healthcare, particularly for diagnosing Major Depressive Disorder (MDD), has paved the way for advanced predictive analytics in mental health. This study aimed to develop ML models to detect MDD using sensor-derived data, including accelerometer measurements, temperature, pulse rates, and SpO2 levels. The proposed methodology involved collecting and preprocessing a comprehensive dataset, imputing missing values, and splitting the data into training and test sets. Feature scaling and PCA were applied to enhance model efficiency. The performance of various ML models such as Random Forest, SVM, Gradient Boosting, XGBoost, LightGBM, and Logistic Regression was evaluated based on accuracy, precision, recall, and F1-score. Random Forest and XGBoost models performed exceptionally well, with accuracy rates nearing 97%. Ensemble methods, in particular, demonstrated superior predictive power. Logistic Regression showed significantly lower performance, highlighting the complexity of MDD beyond linear classification methods. Future directions include developing real-time analysis models, personalizing predictive algorithms, integrating ML into clinical practice, and conducting longitudinal studies to validate and refine these tools. Ethical and privacy considerations, as well as expanding predictive factors, are critical for advancing this research field. The study concluded that ML models are potent tools for early detection of MDD, which can significantly impact patient outcomes. The potential for these models to be integrated into clinical settings for personalized patient care could revolutionize mental health diagnostics and treatment strategies. © 2025 ieee.

关键词： Patient treatment

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A Flexible Hybrid Resource Allocation Model for Enhanced Efficiency using AI in Wireless sensor Networks 3

A Flexible Hybrid Resource Allocation Model for Enhanced Eff...

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

作者： Endla, Purushotham Mohandas, R. Saikia, Achinta Lakshmi, C. Karthikeyan, G. Moharekar, Tejashree Tejpal Sr University School of Sciences and Humanities Department of Physics Telangana Warangal506371 India Chennai Institute of Technology Department of Ece Chennai India Mariani College Department of Statistics Mariani Assam Jorhat785634 India Saveetha Engineering College Chennai India Karpagam College of Engineering Department of Artificial Intelligence & Data Science Coimbatore India Shivaji University Yashwantrao Chavan School of Rural Development Maharashtra Kolhapur India

ISBN: (纸本)9798331509675

Wireless sensor Networks (WSNs) have become a cornerstone technology in various fields, including environmental monitoring, industrial automation, and healthcare. This paper presents a novel hybrid resource allocation model designed to enhance efficiency in Wireless sensor Networks (WSNs) using advanced Artificial Intelligence (AI) techniques. The proposed model integrates static and dynamic resource allocation strategies with machine learning and reinforcement learning to optimize key network resources such as energy, bandwidth, and processing power. Comprehensive evaluations demonstrate that the proposed model outperforms nine existing models across multiple metrics, including energy efficiency, network lifetime, throughput, delay, latency, and accuracy of resource allocation. The proposed model achieved an energy efficiency of 0.98 J/bit, extended network lifetime to 620 days, and maintained a high throughput of 620 Kbps. Notably, the model demonstrated the lowest delay (90 ms) and latency (180 ms) while achieving the highest resource allocation accuracy of 97.8%. These results highlight the model's capability to dynamically adjust resource allocation in response to real-time network conditions, ensuring optimal performance and resource utilization. The proposed model's success in efficiently managing WSN resources marks a significant advancement in the field, offering a robust solution for various WSN applications requiring high efficiency and reliability. © 2025 ieee.

关键词： Artificial Intelligence Energy Efficiency Machine Learning Network Lifetime Reinforcement Learning Resource Allocation Wireless sensor Networks

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Data Fusion Algorithm in Agricultural Internet of Things System 3

Data Fusion Algorithm in Agricultural Internet of Things Sys...

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3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Wang, Fang School of Business Changchun College of Electronic Technology Changchun China

ISBN: (纸本)9798331508456

Agricultural production relies on a large number of sensors to obtain environmental data, and how to effectively integrate this data has become a major challenge in current agricultural IoT applications. Therefore, this article proposes a novel data fusion algorithm. Firstly, the weighted average method is used to preliminarily fuse data from different types of sensors;Then, deep neural networks (DNNs) are used to further extract potential features from the fused data. Finally, the weighting strategy and network model were optimized through experiments. The experimental results indicate that the research method performs well in terms of accuracy, spatiotemporal consistency, and real-time performance. Compared with the weighted average method, this research method reduced the mean square error (MSE) by about 29% and increased the dynamic time warping (DTW) score by about 40.7%. The results verified that the method is practical in improving data quality and suitable for large-scale data processing in agricultural IoT. © 2025 ieee.

关键词： sensor data fusion

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Optimized Low-Power Mixed-Signal sensor systems: Improved Cross-Domain Approach to Maintain Classification Accuracy 3

Optimized Low-Power Mixed-Signal Sensor Systems: Improved Cr...

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3rd international conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Kumar, M. Kiran Musnmusawi, Muntather Kodati, Sarangam Suresh, G.R. Suresh, T. Anurag University Department of Electronics and Communication Engineering Hyderabad India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Al Diwaniyah Iraq Cvr College of Engineering Department of Information Technology Hyderabad India Dhanalakshmi Srinivasan College of Engineering Technology Department of Electronics and Communication Engineering Mamallapuram India New Prince Shri Bhavani College of Engineering and Technology Department of Mechanical Engineering chennai India

ISBN: (纸本)9798331508456

Optimizing the mixed-signal systems-on-Chips (SoCs) is a difficult task, particularly while including analog developing blocks and Machine Learning (ML)-based algorithms that make overall performance hard to predict. These designs for minimizing energy consumption increases the lifetime from harvested energy from the environment. In this manuscript, a method is developed for optimizing difficult mixed-signal sensor system that includes three phases. Initially, Pareto-optimal local performance of individual circuits is modelled, developing the macro-model of mixed-signal system performance involving local nonidealities and identifying optimum group of system-level parameters utilizing numerical optimization. The method is employed for Electrocardiogram (ECG) sensor system with integrated arrhythmia classification with minimizing system energy consumption and constraints on inference accuracy. Various numerical optimization algorithms are compared to evaluate its performance for minimizing task are Bayesian Optimization and Gradient-based optimization. The experimental results obtained optimization in reasonable execution time of 9.5 h while compared to existing algorithms on complex mixed-signal system. © 2025 ieee.

关键词： Pareto principle

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Voice Command Fingerprinting: An in-Depth Examination of Challenges and Issues 3

Voice Command Fingerprinting: An in-Depth Examination of Cha...

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3rd ieee international conference on Industrial Electronics: Developments and Applications, ICIDeA 2025

作者： Singh, Paras Nath Mathews, Ann Antu, Anisha Suryanarayana, Ch.V.S. GMR Institute of Technology Department of CSE Rajam532127 India CMR Institute of Technology Department of CSE Bengaluru560037 India Federal Institute of Sc. & Technolgy Department of CSE Kerala Angamaly683577 India Mallareddy Engg. College Maisammaguda Department of CSE-AIML Secundrabad500100 India

ISBN: (纸本)9798331533205

Voice assistant systems have gained widespread adoption across diverse sectors, including electronics, healthcare, automotive, and retail. Spoken Language Understanding is essential for voice-text interaction systems. Security researchers have already identified vulnerabilities in encrypted network traffic, particularly in the context of traffic analysis, enabling the network-level adversaries to infer the websites visited by users. This security threat, common in traditional web communication, extends to Website Fingerprinting attacks in voice query communication, giving rise to the concept of Voice Command Traffic Fingerprinting. This paper analyses the features, challenges and linguistic properties of VCF(Voice Command Fingerprinting) considering various features. A detailed analysis reveals that the proposed models for voice command categories and factors are more capable to handle the issues. The results and methods in this paper pave the way for further research on the secured models in voice command processing in. The Python tools like Tensor-flow, Scikit-learn and specially Pyttsx3 have been used for generating and filtering datasets. An CTC-Attention-based hybrid model recurrent unit which effectively combine the two models. The proposed model achieves over 99.7% accuracy. © 2025 ieee.

关键词： distributed computer systems

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