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Scalable Brain Stroke Prediction Using PySpark: A Big Data Approach 5

Scalable Brain Stroke Prediction Using PySpark: A Big Data A...

引用

5th IEEE international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Nair, Rohit S. Varma, Kallepalli Rahul Raj, Kalagara Venkata Sampath Ramu Keerthana, P. Ramesh, T.K. Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electronics & Communication Engineering Bengaluru India

ISBN: (纸本)9798350356236

Brain stroke affects a large number of the population and is one of the primary causes of death and permanent disability, therefore the identification of risk factors is paramount for timely prevention. These interactions are difficult to capture because stroke risk factors are demographics, life style and medical history all of which cannot be modeled using statistical solution. To tackle these and similar issues, the present paper employs PySpark, a distributed computing system, for the formulation of small-scale and big data with various machine learning algorithms including, Logistic Regression, Gradient Boosting, Decision Trees, Naive Bayes, and Random Forests, for predicting brain stroke. The used models were derived from a great amount of healthcare data, though the hyperparameter optimization was applied to improve the results. Among those algorithms, Gradient Boosting showed the best result with the accuracy of 92.2, Random Forest is 90.5, which indicates that these algorithms can give good results at the time of risk assessment of stroke. Measuring techniques like precision, recall and F1 score were employed to measure model accuracy and test ability to control between False Positives and False Negatives. This multiple model approach recognizes the most efficient algorithms for possible implementation in real-life, mass, clinical practice for early detection, patient control, and individual prevention. © 2025 IEEE.

关键词： Distributed computer systems

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Bovitrack:Animal behavior monitoring using Machine learning and IoT 5

Bovitrack:Animal behavior monitoring using Machine learning ...

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5th international conference on Advanced Research in Computing, ICARC 2025

作者： Viraj, Harsha Wijesekara, Sumuduni Tharuka, Kuluni Fernando, Shenika Jayakody, Anuradha Wijesiri, Pipuni Sri Lanka Institute of Information Technology Dept of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331530983

Analyzing dairy cattle behavior and anomalies is a critical component of precision livestock farming, allowing farmers to remotely monitor animals for health and behavior. In order to accomplish this task better, the use of IoT technology and machine learning algorithms is more appropriate as per the time. The YOLO (you only look once) object recognition algorithm is more suitable for that, and the use of this algorithm allows these processes to be performed automatically and in real time with high accuracy. YOLO's ability to recognize multiple objects in images or videos makes Yolo ideal for cattle detection and tracking. © 2025 IEEE.

关键词： Learning algorithms

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Generative Waveform Design Methods in Complex Electromagnetic Spectrum Scenarios 24

Generative Waveform Design Methods in Complex Electromagneti...

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4th international conference on Signal Processing and Communication Technology, SPCT 2024

作者： Hu, Yuhao Hu, Weidong Du, Xiaoyong Lei, Gang Xi’an Institute of Electronic Engineering Shaanxi Xi’an China College of Electronic Science and Technology National University of Defense Technology Hunan Changsha China

ISBN: (纸本)9798400710636

With the development of complex spectrum environments such as 5G and 6G, the utilization of electromagnetic spectrum resources has become increasingly intensive, imposing higher demands on the performance and adaptability of traditional radar systems. The fixed waveform design approach commonly used in traditional radar systems is easily identifiable and prone to interference in complex spectrum environments, making it difficult to meet dynamically changing mission requirements. To address this challenge, this paper proposes a generative waveform design method suitable for complex spectrum environments. This method constructs a performance envelope based on radar mission requirements under multiple constraints and generates a feasible waveform set through constrained rule-based searches, thereby enhancing the flexibility and adaptability of waveform design to the environment. Experiments demonstrate that the proposed generative waveform design exhibits greater robustness and anti-interference capabilities in complex spectrum environments. Comparative analysis using classic clustering and sorting algorithms further validates the effectiveness and superiority of this method over traditional fixed waveforms, providing new insights for the design and optimization of radar systems in complex spectrum environments. © 2024 Copyright held by the owner/author(s).

关键词： Radar interference

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Estimating the cost of dining for two: An Integration of Sentiment Analysis and Feature Based Approach 5

Estimating the cost of dining for two: An Integration of Sen...

引用

5th international conference on Emerging systems and Intelligent Computing, ESIC 2025

作者： Shashank, Salabh Behera, Rajat Kumar Amity University Auup Amity School of Engineering and Technology Uttar Pradesh Noida201301 India KIIT Deemed to be University School of Computer Engineering Odisha Bhubaneswar751024 India

ISBN: (纸本)9798331522100

In rapidly growing food industry, where more than 1200 orders are processed in a minute, it is essential for owners to optimize their services. This study focuses into the food industry and analyzes the approximate cost for two people along with the sentimental analysis using the data taken from Zomato. The approximate cost for two is based on various factors like restaurants location, availability of online ordering, table booking facilities, restaurant ratings and, cuisines offered. Since there is huge competition among restaurants, so some fake reviews can also be posted. Sentimental analysis is applied to check if the review posted is genuine or fake. Results found that there are around 10k fake reviews out of 51k total reviews. Various regression algorithms like Decision Tree, Random Forest, and others are used to calculate approximate cost for two. Also, various classification algorithms like Naïve Bayes, Support Vector, and others are used to verify sentiment analysis. The regression problem is analyzed using two different approaches: first by considering all the features and second by reducing the features by using Principal Component Analysis. Various computation metrics for regression as well as classification are computed, and the results are incorporated. © 2025 IEEE.

关键词： Food industry

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Enhancing Dogo Rover Capabilities with Integrated Aerial Surveillance and Autonomy 2

Enhancing Dogo Rover Capabilities with Integrated Aerial Sur...

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

作者： Raja, M. Sakthi Adhavan, K. Pt, Sakthiathavan Savitha Dhivya, P. Harikrishna, N. Kongu Engineering College Department of Electronics and Instrumentation Engineering Tamil Nadu Erode638060 India

ISBN: (纸本)9798331505745

This project aims to enhance the capabilities of the DOGO Rover, a robotic platform used for security and reconnaissance, by integrating advanced features for both ground-based and aerial surveillance. The upgraded system employs a Raspberry Pi 3B paired with a 5MP camera module, enabling real-time image processing for autonomous pathway monitoring and facial recognition. This allows the rover to detect and alert the control room of any unrecognized individuals. Additionally, aerial surveillance is achieved through the incorporation of a drone, which is remotely controlled and stabilized with two servo motors to ensure reliable performance over rough terrains. The drone extends the system's surveillance capabilities by providing a broader field of view from above, complementing the ground-level operations of the rover. The project also focuses on enhancing the rover's autonomy through intelligent path-following algorithms, enabling adaptive navigation in dynamic environments. By integrating ground and aerial perspectives with advanced autonomy, this upgraded DOGO Rover presents a comprehensive solution for modern security challenges, offering improved situational awareness, operational reliability, and versatility. The combination of robotics, aerial technology, and artificial intelligence demonstrates a robust approach to addressing complex security requirements in various settings. © 2025 IEEE.

关键词： Motion analysis

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Quantum Resistance Blockchain Algorithm using QHF and Novel Consensus Mechanism

Quantum Resistance Blockchain Algorithm using QHF and Novel ...

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2025 IEEE international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Paul, Soumyadip Mazumdar, Mausumi Maitra Chakraborty, Sarit Government College of Engineering & Ceramic Technology Dept. of Information Technology Kolkata700010 India Government College of Engineering and Leather Technology Department of Computer Science and Engineering Kolkata700106 India

ISBN: (纸本)9798331529833

The emergence of quantum computing poses a significant threat to the security of traditional blockchain systems, which rely heavily on classical cryptographic algorithms. To safeguard the integrity and reliability of blockchain technology in the upcoming era, this work proposes a novel quantum-resistant blockchain architecture. This approach integrates a robust consensus mechanism, with advanced quantum cryptographic primitives, including quantum digital signatures based on a Quantum Hash Function (QHF). Incorporating a quantum hash function enhances the blockchain resistance, providing an additional layer of security against quantum attacks. By combining these elements, our method creates a blockchain system fortified against the potential vulnerabilities introduced by quantum computers. The proposed solution leverages the power of QHFs to safeguard transaction integrity, while the consensus mechanism ensures the reliability and decentralization of the network. This paper also delves into the theoretical underpinnings of quantum blockchains, exploring their design, feasibility, and potential applications, emphasizing their promising future, particularly in areas such as decentralized identity authentication. Finally, it contributes to the development of a secure and resilient blockchain ecosystem capable of thriving in the coming era. © 2025 IEEE.

关键词： Quantum computers

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Evaluating Agricultural Soil GHG Emission Models: A Critical Analysis of Identification and Future Challenges

Evaluating Agricultural Soil GHG Emission Models: A Critical...

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2025 international conference on Pervasive Computational Technologies, ICPCT 2025

作者： Pathak, Arnima Sharma, Monika Kumar, Devendra Amity School of Engineering Greater Noida India Amity University Noida India Abes Engineering College Ghaziabad India

ISBN: (纸本)9798331508685

This study focuses on the increasing global challenge of the need for efficacy assessment of techniques and the critical problem of GHG emissions from agrosystems. It addresses their processes, input variables, and simulation results in different cropping systems to present a comprehensive analysis of four well- known process models: DNDC, DayCent, STICS, and APSIM. These may include model structure, management practices, weather conditions, and the level of data available at the time the simulation was built that influence the model's accuracy in the study. Development of appropriate measurement and reduction tools in agriculture is quite essential because, as the IPCC noted, agriculture plays a vital role in global anthropogenic emissions of greenhouse gases. This study puts forward an integrated hybrid framework, which includes both process-based models and geospatial techniques to increase the precision of emission assessments over varied agricultural systems. The proposed method showed promising improvement in the scalability and robustness of models to enable effective tailoring of mitigation strategies toward sustainable agricultural practices. This paper aims to enhance model accuracy and applicability by offering a methodology focusing on increasing the model's applicability area, unifying the data analysis, and improving the model's algorithms. This project aims to facilitate sustainable food production methods and climate change adaptation by providing better tools and methods to stakeholders by tackling the problem of identifying and managing soil GHG emissions. © 2025 IEEE.

关键词： Greenhouse gas emissions

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Exploration of the generalization and hallucination in deep learning: a study based on imaging through scattering medium 17

Exploration of the generalization and hallucination in deep ...

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17th international conference on Photonics and Imaging in Biology and Medicine, PIBM 2024

作者： Liu, Honglin Zhang, Xuyu Xie, Gengchen Shanghai Institute of Optics and Fine Mechanics Chinese Academy of Sciences Shanghai201800 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Science Beijing100049 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (数字)9781510688070

ISBN: (纸本)9781510688063

With fast developments of computational power and algorithms, deep learning has made breakthroughs and been applied in many fields. However, generalization remains to be a major challenge, and the limited generalization capability severely constrains applications of deep learning in practice. The hallucinations issue is another unresolved conundrum faced by deep learning and large models. By leveraging a physical model of imaging through scattering media, we studied the lack of generalization to datasets and system response functions in deep learning respectively, identified their causes, and proposed universal solutions. The research also provides an explanation for hallucinations. In general, it enhances the interpretability of deep learning from a physics-based perspective. It will pave a way for direct interaction between deep learning and the physical world, facilitating the transition of deep learning from a demo model to a practical tool. © 2025 SPIE.

关键词： Education and training Deep learning Image restoration Imaging systems Associative arrays Image quality Scattering Scattering media Artificial intelligence Light sources and illumination

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Binary Stochastic Flip Optimization for Training Binary Neural Networks

Binary Stochastic Flip Optimization for Training Binary Neur...

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

作者： Shibuya, Tatsukichi Inoue, Nakamasa Kawakami, Rei Sato, Ikuro Department of Computer Science Institute of Science Tokyo Tokyo Japan Department of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan Denso IT Laboratory Tokyo Japan

ISBN: (纸本)9798350368741

For deploying deep neural networks on edge devices with limited resources, binary neural networks (BNNs) have attracted significant attention, due to their computational and memory efficiency. However, once a neural network is binarized, finetuning it on edge devices becomes challenging because most conventional training algorithms for BNNs are designed for use on centralized servers and require storing real-valued parameters during training. To address this limitation, this paper introduces binary stochastic flip optimization (BinSFO), a novel training algorithm for BNNs. BinSFO employs a parameter update rule based on Boolean operations, eliminating the need to store real-valued parameters and thereby reducing memory requirements and computational overhead. In experiments, we demonstrated the effectiveness and memory efficiency of BinSFO in finetuning scenarios on six image classification datasets. BinSFO performed comparably to conventional training algorithms with a 70.7% smaller memory requirement. Code is released at https://***/TatsukichiShibuya/ICASSP2025_BinSFO. © 2025 IEEE.

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Decoding Tumor Gene Expression for Multiclass Cancer Classification 3

Decoding Tumor Gene Expression for Multiclass Cancer Classif...

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

作者： Jyothi, V. Srujana, Medipally Himasreeja, Yelam Sanjana, Sevana Sowmya Fatima, Bushra Gokaraju Rangaraju Institute of Engineering and Technology Cse Department Nizampet Road Bachupally Kukatpally Telangana Hyderabad500090 India

ISBN: (纸本)9798331509675

Cancer, a multifactorial and complex disease, is caused by genetic mutations and environmental influences leading to uncontrolled cell proliferation and the possibility of tumor spread. Recent developments in genomic analysis with the use of next-generation sequencing technologies like Whole Genome Sequencing (WGS) and Whole Exome Sequencing (WES) have greatly enhanced detection and classification of cancer. Machine learning (ML) has become a useful tool in the diagnosis of cancer, providing better accuracy in the classification of cancer types from high-level genomic and histopathological information. The following is a review of the existing machine learning methods used in cancer classification, comprising deep learning (DL) techniques, and addresses some of the major challenges like data imbalance, feature selection, and model interpretability. The paper also points towards the possibility of combining multi-modal data sources, federated learning, and explainable AI (XAI) to further improve cancer classification systems. In addition, the solution proposed in this review describes a machine learning-based framework for cancer classification, involving different ML algorithms, cross-validation methods, and sophisticated feature selection techniques for robust and reliable results. © 2025 IEEE.

关键词： Federated learning

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