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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Kumar, K. Ankith Garg, Mohan Shah, Manita D. Ahmed, Kalyandurg Rafeeq Agarwal, Trapty Saraswat, Vivek Department of Computer Science and Information Technology Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India

ISBN: (纸本)9798331542375

Deep Learning-Based Forecasting of Solar Photovoltaic Generation for Micro grid Control is a new approach to accurately predicting solar photovoltaic generation in micro grids. These feature renewable energy such as Solar PV and are a responsive approach for providing efficient, sustainable power solutions. In contrast, solar PV power production is not dispatch able because it depends on the changing weather and other factors. The new method applies deep learning algorithms, mainly artificial neural networks, to historical data to accurately forecast solar PV output. The latter sorts the variation due to a broad array of meteorological and operational parameters, including temperature, humidity level, cloud cover, and shading Moreover, the model can forecast results online and keep learning with new data for real-time micro grid control. The deep learning-based method has shown superior performance to traditional forecasting methods in improving the precision of PV generation forecasts. Benefits include more efficient energy management, better integrating renewable sources into the micro grid, and lower costs. This means that the Deep Learning-Based Forecasting of Solar Photovoltaic Generation for Micro grid Control could significantly improve the reliability and efficiency of micro grid operations and promote a more sustainable and resilient energy system. © 2025 IEEE.

关键词： Marketing

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Deep Learning for Precision Medicine: Advancing Personalized Treatment Plans in Oncology

Deep Learning for Precision Medicine: Advancing Personalized...

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Computational, Communication and Information technology (ICCCIT), International Conference on

作者： Anurag Shrivastava Arnav Kotiyal Alok Jain V.S Anusuya Devi Bolleddu Devananda Rao Saloni Bansal Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Lovely Professional University Phagwara India Department of Applied Sciences New Horizon College of Engineering Bangalore India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India Department of Computer Engineering and Applications GLA University Mathura India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

A new approach to cancer care, precision medicine tailors treatments to each patient's specific genetic, molecular, and clinical characteristics. In order to analyze complicated, high-dimensional data and create individualized treatment plans, deep learning—a potent subfield of AI—has arisen as a game-changing tool in this area. Discover how deep learning methods are being used in precision oncology to improve cancer diagnosis, prognosis, and treatment planning in this groundbreaking article. A few examples of notable developments are the use of CNNs to medical image tumor identification and segmentation, RNNs to treatment outcome prediction, and LSTM models to patient survival. More effective and less harmful treatments are possible because too deep learning models that integrate multimodal data, such as genetic sequences, histopathological pictures, and electronic health records. These models can detect new biomarkers and forecast how different medications will affect individual patients. The clinical translation of deep learning models is hindered by a number of obstacles, despite these encouraging improvements. Problems including data scarcity, unequal distribution of classes, and unintelligibility are major roadblocks. Also, to make sure these models are accurate and reliable, they need to be standardized and validated thoroughly before they can be used in clinical processes. Fostering trust and acceptance in therapeutic contexts also requires addressing ethical problems, such as data security, possible biases in algorithmic predictions, and patient privacy. This work offers a thorough analysis of existing approaches, delves into the difficulties and moral concerns, and suggests avenues for further study to improve the use of deep learning in precision oncology. Personalized treatment regimens, made possible by deep learning, have the potential to revolutionize cancer care by vastly increasing patient outcomes.

关键词： Deep learning Biological system modeling Precision medicine Predictive models Oncology Prediction algorithms Genetics Natural language processing Medical diagnostic imaging Cancer

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SpDiff: A Speech Sensing System with Diffusion Model Based on mm Wave Radar

SpDiff: A Speech Sensing System with Diffusion Model Based o...

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IEEE Conference on Wireless Communications and Networking

作者： Can Jin Yulong Zhang Xuanheng Li Yi Sun Jie Wang Yuguang Fang School of Information and Communication Engineering Dalian University of Technology Dalian China National Mobile Communications Research Laboratory Southeast University Nanjing China School of Information Science and Technology Dalian Maritime University Dalian China Department of Computer Science City University of Hong Kong Hong Kong China

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Voice control has become an indispensable interaction method in smart devices. Compared to traditional microphones, mm Wave radar offers a promising solution for speech sensing in noisy environments. However, most current research relies on single-view information, such as vocal cord vibrations or lip movements, to classify speech, which overlooks important details like timbre, speech rate, and intonation, limiting the application of speech sensing. To address these issues, we develop a high-quality speech sensing method based on mm Wave radar, named SpDiff. This method accurately localizes the vocalizing target and, based on the human vocal mechanism, extracts multi-view speech features according to the movement characteristics of the vocal cords, lips, and face. Additionally, to generate high-quality speech signals, we design a conditional latent diffusion model (CLDM), which uses multi-view radar information as conditional guidance, accurately capturing the complex mapping relationships between radar and speech signal distributions. To evaluate the SpDiff method, we build a mmWave system using IWR1443Boost and recruit 14 volunteers to construct a dataset. Experimental results show that SpDiff achieves high standards in speech sensing, with the generated speech directly input into existing recognition models, achieving an average character and word error rate (CER/WER) of only 2.33% and 3.05%.

关键词： Vibrations Lips Spaceborne radar Speech recognition Diffusion models Feature extraction Sensors Doppler radar Speech processing Standards

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Efficient Abstraction of Healthcare Data for Real-Time Decision Making Using Logistic Regression and Linear Regression 3

Efficient Abstraction of Healthcare Data for Real-Time Decis...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Rathour, Abhinav Singh, Debabrata Kalra, Hitesh Bajpai, Anila Yadav, Dhananjay Kumar Sabarivani, A. Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Applications Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Sathyabama Institute of Science and Technology Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331542375

Data analytics can help drive decision-making and improve patient outcomes in healthcare. But of course, healthcare data is complex, and the process to access & analyze it can be slow. Here, researchers suggest a way to generalize healthcare data at scale and do so quickly enough for real-time decision support. Logistic regression is used to categorize patient data into various groupings using established standards, which could create a targeted patient population that is at high risk. We then perform linear regression on patients' demographics and medical history to predict their outcomes. This information can help with real-time decisions for patient care. Our method will decrease the time and cost of collecting/analyzing healthcare data for faster decision-making. This is particularly crucial in a rapidly moving healthcare environment where every second counts. Also, logistic and linear regression techniques have been used together to perform a complete data analysis so that the predictions are as accurate as possible. The methodology allows us to effectively abstract healthcare data for healthcare providers' (HCP) real-time decision-making. This process can deliver advances in patient care and efficiencies in effectuating decisions-and it could provide immense benefits across healthcare. © 2025 IEEE.

关键词： Patient treatment

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A Comprehensive Study of Deep Learning and Machine Learning Methods for Land Application and Cover Segmentation

A Comprehensive Study of Deep Learning and Machine Learning ...

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Advances in computer science, Electrical, Electronics, and Communication Technologies (CE2CT), International Conference on

作者： Kimmi Gupta Abhinav Raghav Heena Khera R. Srinivasan School of Computer Science Engineering & Technology Bennett University Greater Noida India IILM University Greater Noida India IILM University Greater Noida Galgotias University Greater Noida

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Research in the Delhi NCR area from 2000 to 2024 is compared in this study. Firstly, a flash flood occurred due to the heavy rain in July 2013. Grasslands are among the vegetative types in L ULC, which cannot be univocally classified as either natural in-land or artificial urban. But some areas of grassland, when it came time to classify L ULC, three different machine learning techniques were considered: random forest, support vector machine, and feedforward neural networks. Optimization The model best for random forest performed better than those two others. The study used statistical analysis tools, the “sustainable development prism” and “patch-corridor matrix” models, a landscape index, and time series data to explore how BGS changed in new urban areas. The research made use of Landsat datasets and optimized machine learning algorithms to classify L ULC, extract built-up areas, analyse landscape fragmentation and investigate built-up expansion in Delhi NCR by means of frequency analysis. In the EFPCNNM ensemble deep learning model, convolutional neural networks, feature pyramids, and ensemble learning are combined to increase the accuracy of remote sensing picture classification. The EFPCNNM process includes everything necessary for image analysis: pre-processing, feature extraction, feature pyramid creation, and ensemble learning. Exciting new possibilities for environmental monitoring, urban planning and property mapping emerge from this study. Sustainable urban development and resource management require effective monitoring and interpretation of the changes in L ULC, as well as employing remote sensing data alongside machine learning and deep learning technology.

关键词： Deep learning Grasslands Machine learning algorithms Feature extraction Classification algorithms Ensemble learning Data mining Convolutional neural networks Remote sensing Random forests

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Enhanced Fault and Attack Detection in IoT Systems with Naive Bayes and Long Short-Term Memory Models

Enhanced Fault and Attack Detection in IoT Systems with Naiv...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Sathish Srinivasan Vidhyadar Unni Krishnan A. S Mahesh Kumar B K Madhu Harshal Shah Amol Sapatnekar Oracle Cloud Infrastructure Division Machine Learning & AI Development California USA Visa Commercial Solutions division Visa Inc Foster City California USA Department of Electronics and Communication Engineering Maharaja Institute of Technology Srirangapatna India Department of Computer Science and Engineering Vidya Vikas Institute of Engineering and Technology Alanahalli India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul institute of Technology Parul University Vadodara India Faculty of Law Symbiosis Law School Pune Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

As the number of IoT-dependent gadgets increases in the market, stringent security and fault detection solutions are required. The characteristics and variability of IoT systems may not fully capture classical detection techniques, leading to the use of more advanced machine learning-based techniques. This research employs a dual detection model consisting of Naive Bayes and Long Short-Term Memory (LSTM). The approach analyzes byte streams originating from IoT networks and device patterns, normalizes and encodes the data, and divides the classification process into two phases: screening through Naive Bayes and detailed analysis through LSTM. The proposed hybrid model achieved an overall fault detection accuracy of 97.3 % and an attack detection accuracy of 96.8 % for various IoT scenarios. The system achieved 95.2% precision and 94.8% recall, as well as a 2.1% false-positive rate. The results demonstrated that the proposed hybrid approach is 12 and 15 percent faster than the standalone implementations. In critical scenarios, particularly those where attackers employ multi-vector attacks against IoT systems and hard-to-detect faults in the communication and runtime environments, the proposed framework offers a viable solution to address these issues and challenges.

关键词： Runtime environment Accuracy Fault detection Network security Bayes methods Internet of Things Circuit faults Integrated circuit modeling Long short term memory Streams

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Optimizing Battery Energy Efficiency and Power Consumption for Extended Range in Electric Vehicles 3

Optimizing Battery Energy Efficiency and Power Consumption f...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Bhatnagar, Tarang Mohanty, Prithviraj Sood, Gourav Krishnan, Gopala Jalall, Kehkeshan S Agarwal, Ankita Centre for Interdisciplinary Research for Business and Technology Centre of Research Impact and Outcome Chitkara University Punjab Rajpura140417 India Department of Computer Science and Information Technology Odisha Bhubaneswar India Chitkara Centre for Research and Development Chitkara University Himachal Pradesh Baddi174103 India Karnataka Bangalore India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331542375

Electric-powered motors (EVs) are gaining a reputation as an alternative to conventional fuel-powered motors because of their environmental advantages and rising fuel charges. However, their restrained battery range and excessive energy intake are giant limitations to their substantial adoption. To deal with those problems, this venture aims to optimize the battery power performance and energy consumption of EVs for a prolonged variety. First of all, the battery utilization may be optimized through superior electricity control algorithms. This will involve predicting and managing the energy needs of the car via sensible, manipulated structures. By optimizing battery utilization, the overall strength performance of the vehicle will be expanded, making an allowance for an extended range. Secondly, the power intake of the EV can be optimized by using lightweight materials, green motor and drivetrain designs, and advanced regenerative braking systems. This technology will lessen the strength required to strengthen the automobile, further increasing its variety. Moreover, the project will develop clever charging strategies for EV proprietors to price their motors at some stage in off-height hours while energy is cheaper and the grid is less congested. This may reduce energy consumption and improve battery life. © 2025 IEEE.

关键词： Energy utilization

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CrossFuse: Learning Infrared and Visible Image Fusion by Cross-Sensor Top-K Vision Alignment and Beyond

arXiv

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arXiv 2025年

作者： Shi, Yukai Shi, Cidan Weng, Zhipeng Tian, Yin Xian, Xiaoyu Lin, Liang School of Information Engineering Guangdong University of Technology Guangzhou510006 China CRRC Academy Co. Ltd. Beijing China School of Computer Science Sun Yat-sen University Guangzhou510006 China

Infrared and visible image fusion (IVIF) is increasingly applied in critical fields such as video surveillance and autonomous driving systems. Significant progress has been made in deep learning-based fusion methods. However, these models frequently encounter out-of-distribution (OOD) scenes in real-world applications, which severely impact their performance and reliability. Therefore, addressing the challenge of OOD data is crucial for the safe deployment of these models in open-world environments. Unlike existing research, our focus is on the challenges posed by OOD data in real-world applications and on enhancing the robustness and generalization of models. In this paper, we propose an infrared-visible fusion framework based on Multi-View Augmentation. For external data augmentation, Top-k Selective Vision Alignment is employed to mitigate distribution shifts between datasets by performing RGB-wise transformations on visible images. This strategy effectively introduces augmented samples, enhancing the adaptability of the model to complex real-world scenarios. Additionally, for internal data augmentation, self-supervised learning is established using Weak-Aggressive Augmentation. This enables the model to learn more robust and general feature representations during the fusion process, thereby improving robustness and generalization. Extensive experiments demonstrate that the proposed method exhibits superior performance and robustness across various conditions and environments. Our approach significantly enhances the reliability and stability of IVIF tasks in practical applications. © 2025, CC BY.

关键词： Image fusion

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Reinforcement Learning-Based Optimal Power Flow Control in Islanded Microgrids 3

Reinforcement Learning-Based Optimal Power Flow Control in I...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Agarwal, Ankita Kumar, Amit Maity, Dipankar Ojha, Varun Nalini Sunil, M. Dore, Aruna Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Science and Information Technology Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798331542375

One of the popular paradigms for control problems in different applications is Reinforcement Learning. In this paper, we develop an RL-based technique for OPF control in islanded micro grids. Micro grids are small power systems for remote or rural regions that can function independently of the national grid and serve as a dependable alternative. The dependence on their decentralized power generation over DERs is a major complication for micro grid system stability and noteworthy optimization of reactive power flow. The operation of micro grids is both dynamic and stochastic;as a result, operational models based on traditional control methods at the model level or rule-based conflicts with optimized controllers that need to work in an environment where the future long-term forecasts remain uncertain. RL is suggested as a data-driven and adaptive control method to address these issues and optimize power flow in islanded micro grids. The RL-based control proposed in this paper uses realtime data from the micro grid to learn an optimal policy. The presented micro grid environment is for the reinforcement learning agent with which it interacts, and on success or failure of its actions, it will be given a reward signal to learn. The RL agent, through its interactions and learning process by taking actions during the operation of islanded micro grids, can optimize power flow in a distributed manner given DERs availability, load demand dynamics, and network constraints. © 2025 IEEE.

关键词： Reinforcement learning

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Intelligent Energy Management System for Microgrids with Energy Storage Integration 3

Intelligent Energy Management System for Microgrids with Ene...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Nanda, Simranjeet Kumar, Maddu Naveen Baxi, Paramjit John, Monoo Manaswini, R. Yadav, Dhananjay Kumar Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140417 India Department of Computer Science and Information Technology Odisha Bhubaneswar India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh Baddi174103 India Karnataka Bangalore India Presidency University Department of Electronics and Communication Engineering Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (纸本)9798331542375

The items are a software solution, it is designed for micro grids with energy storage integration to monitor, control, and best utilize available grid resources. Also called a mini-grid, the micro grid is a distributed energy system consisting of local small-scale generation and storage facilities that could operate independently from the main grid in an emergency. Micro grids are frequently paired with solar panels and wind turbines, making them more sustainable. The capacity of energy storage systems in micro grids has been playing an important role, as renewable energy in devices with temporal availability generates the need for their more efficient use. Based on real-time data, the items apply energy transmission and distribution management algorithms to control where the power is used in a micro grid. The items continuously monitor the demand and supply of energy to investigate the ideal combination of renewable generation and stored energy for peak shaving. In doing so, the micro grid can run optimally and minimize energy waste without running up bills. The items can also predict how and when individuals use energy, thus allowing better scheduling of the available resources. This enables the items to conduct predictive maintenance on those micro grid components to long and function as efficiently as possible. © 2025 IEEE.

关键词： Marketing

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