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An Efficient Brain tumor classification using CNN and transfer learning

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An Efficient Brain tumor classification using CNN and transf...

Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： P. Sasikumar Srikanth Cherukuvada P Balmurugan P Vijay Anand S. Brindasri R. Nareshkumar Department of Artificial Intelligence and Machine Learning Sphoorthy Engineering College Nadergul Hyderabad Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Bowrampet Hyderabad Telangana Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Department of Artificial Intelligence and Data Science CMR Institute of Technology Bengaluru India Department of Computer Science Hindustan Institute of Technology and Science Padur Chennai India Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The proper detection of brain tumors as quickly as possible and the administration of efficient therapy are both necessary steps in the process of curing patients. The complexities of tumor morphology, such as size, location, and texture, as well as heteromorphic appearance in medical pictures, make tumor analysis a difficult *** three most frequent types of brain cancer are brain tumors, meningiomas, and pituitary gland tumors. The purpose of this study is to investigate a classification issue known as a triple-class problem in order to correctly identify these *** a pre-trained version of the vgg16 network, the proposed classification method pulls characteristics from brain MRI scans. Classifier models that have shown their worth in practice are given to help put the retrieved attributes into meaningful groups. The presented method is carried out on the Python platform, and its efficacy is evaluated with the use of performance *** to the results, the conventional neural network (CNN) technique was outperformed by the Transfer Learning method, which resulted in the model having a higher overall performance. This can be observed by demonstrating that the model had a stronger overall performance.

关键词： Accuracy Magnetic resonance imaging Transfer learning Brain cancer Brain modeling Feature extraction Pituitary gland

A Method of Constructing and Automatically Labeling Radio Frequency Signal Training dataset for UAV

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A Method of Constructing and Automatically Labeling Radio Fr...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chao Liu Ruipeng Ma Zheng Si Mingmin Chi School of Computer Science Shanghai Key Laboratory of Data Science Fudan University Zhengzhou Zhongke Institute of Integrated Circuit and System Application School of Cyber Science and Engineering Zhengzhou University Henan Institute of Advanced Technology Zhengzhou University

The problem of signal detection and classification of multiple UAVs can be solved using object detection techniques in computer vision. However, this requires collecting and labeling a large amount of reliable raw data. Since the UAV signal dataset cannot be directly applied to object detection, we propose a method using time-frequency domain filtering and automatic labeling to construct a large-scale time-frequency spectrogram dataset. Experimental results show that the average recognition accuracies of image transmission signals and remote control signals under interference conditions are 97% and 82%, respectively, while the average errors of signal parameters are 0.93% and 5.57%.

关键词： Training Time-frequency analysis Object detection Manuals Labeling Reliability Speech processing

Solving Large-Scale Two-Echelon Location Routing Problems in City Logistics

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arXiv 2025年

作者： Dereli, Banu Ulusoy Hiermann, Gerhard Schiffer, Maximilian TUM School of Management Technical University of Munich Germany Institute of Production and Logistics Management Johannes Kepler University Linz Austria Munich Data Science Institute Technical University of Munich Germany

Logistic service providers increasingly focus on two-echelon distribution systems to efficiently manage thousands of deliveries in urban environments. Effectively operating such systems requires designing cost-efficient delivery networks while addressing the challenges of increasing e-commerce demands. In this context, we focus on a two-echelon location routing problem with mobile depots and direct shipment, where decisions involve locating micro-depots, and designing first and second-level routes. Our model also incorporates the flexibility of direct shipments from the main depot to customers. To solve such large-scale problems efficiently, we propose a metaheuristic approach that integrates a set cover problem with an adaptive large neighborhood search (ALNS). Our ALNS approach generates a set of promising routes and micro-depot locations using destroy and repair operators while using a local search for intensification. We then utilize the set cover problem to find better network configurations. Additionally, we present a decomposition-based cluster-first, route-second approach to solve large-scale instances efficiently. We show the efficacy of our algorithm on well-known benchmark datasets and provide managerial insights based on a case study for the city of Munich. Our decomposition approach provides comparable results while reducing computational times by a factor of 15. Our case study results show that allowing direct shipment can reduce total costs by 4.7% and emissions by 11%, while increasing truck utilizations by 42%. We find that integrating both stationary and mobile micro-depots, along with allowing direct shipments, can reduce total costs by 5.9% compared to traditional two-echelon delivery structures. Copyright © 2025, The Authors. All rights reserved.

关键词： Location

Maximum and Minimum Causal Effects of Physical Processes

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PRX Quantum 2025年第2期6卷 020335-020335页

作者： Giulio Chiribella Kaumudibikash Goswami QICI Quantum Information and Computation Initiative School of Computing and Data Science The University of Hong Kong Pokfulam Road Hong Kong Department of Computer Science Parks Road Oxford OX1 3QD United Kingdom Perimeter Institute for Theoretical Physics Waterloo Ontario N2L 2Y5 Canada

We introduce two quantitative measures of the strength of causal relations in quantum theory and more general physical theories. These two measures, called the maximum and minimum causal effect, quantify the maximum and minimum changes in the output of a quantum process induced by changes in its input. The maximum and minimum causal effect possesses useful properties, such as continuity and data-processing inequality. In quantum theory, they have close connections with quantum information tasks. The maximum quantum causal effect can be used to detect quantum channels with nonzero capacity for transmitting classical information. The minimum causal effect can be used to guarantee the recoverability of quantum information: every quantum process with a high value of the minimum quantum causal effect can be approximately inverted. Moreover, we show that the quantum causal effects satisfy a duality relation: if the minimum causal effect of a quantum system A on another quantum system B is high, then A must have a low value of the maximum causal effect on any other quantum system B′ that is spacelike separated with B. This duality implies a monogamy relation for quantum causal effects, and represents a fundamental difference between quantum and classical causal relations. We illustrate the application of the maximum causal effect to the analysis of two paradigmatic examples, the first involving a coherent superposition of direct cause and common cause, and the second involving a coherent superposition of multiple quantum processes.

关键词： Sorting

Stress Prediction in Higher Education Students Using Psychometric Assessments and AOA-CNN-XGBoost Models 4

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Stress Prediction in Higher Education Students Using Psychom...

4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Sharma, Sarthak Vij, Suman Praveen, R.V.S. Srinivasan, S. Yadav, Dharmendra Kumar Raj Kumar, V.S. Shri Guru Ram Rai University Uttarakhand Dehradun India School of Management and Commerce Studies Shri Guru Ram Rai University Uttarakhand Dehradun India LTIMindtree Limited Telangana Hyderabad India Amet University Department of Advanced Computing Sciences Tamilnadu Chennai India Lalit Narayan Mithila University University Department of Mathematics Bihar Darbhanga India Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Erode India

ISBN: (纸本)9798331540364

Research backs up the common perception that the years preceding up to college are the most difficult, showing that students report higher levels of discomfort and worse self-esteem during this switch. Positive personality qualities such as optimism, hope, and happiness were examined in this system along with 215 first-semester Israeli university students' ratings of functional impairment, psychological distress, and self-esteem. The three stages that make up this suggested method are data preprocessing, model training, and feature selection. In order to process data that would be infeasible to process without data preprocessing, the data can be adjusted to meet the specifications of each data mining technique. Beginning with different forms of feature classification or aggregation and progressing to individual activity levels, several levels of feature granularity were investigated in order to employ a feature selection technique. The proposed approach used an AOA-CNN-XGBoost for the whole model training phase. This novel method outperforms CNN and AOA with an average accuracy of 89.27%. © 2024 IEEE.

关键词： Students

APPLYING TEXT MINING TO ANALYZE HUMAN QUESTION ASKING IN CREATIVITY RESEARCH

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arXiv 2025年

作者： Wróblewska, Anna Korbin, Marceli Kenett, Yoed N. Dan, Daniel Ganzha, Maria Paprzycki, Marcin Faculty of Mathematics and Information Sciences Warsaw University of Technology Warsaw Poland Faculty of Data and Decision Sciences Technion - Israel Institute of Technology Haifa Israel School of Applied Data Science Modul University Vienna Austria Systems Research Institute Polish Academy of Sciences Warsaw Poland

Creativity relates to the ability to generate novel and effective ideas in the areas of interest. How are such creative ideas generated? One possible mechanism that supports creative ideation and is gaining increased empirical attention is by asking questions. Question asking is a likely cognitive mechanism that allows defining problems, facilitating creative problem solving [15]. However, much is unknown about the exact role of questions in creativity. This work presents an attempt to apply text mining methods to measure the cognitive potential of questions, taking into account, among others, (a) question type, (b) question complexity, and (c) the content of the answer. This contribution summarizes the history of question mining as a part of creativity research, along with the natural language processing methods deemed useful or helpful in the study. In addition, a novel approach is proposed, implemented, and applied to five datasets. The experimental results obtained are comprehensively analyzed, suggesting that natural language processing has a role to play in creative research. Copyright © 2025, The Authors. All rights reserved.

关键词： Text mining

DLO: Dynamic Layer Operation for Efficient Vertical Scaling of LLMs

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arXiv 2024年

作者： Tan, Zhen Dong, Daize Zhao, Xinyu Peng, Jie Cheng, Yu Chen, Tianlong School of Computing and Augmented Intelligence Arizona State University United States Shanghai Artificial Intelligence Laboratory China Department of Computer Science Chinese University of Hong Kong Hong Kong School of Artificial Intelligence and Data Science University of Science and Technology of China China Department of Computer Science University of North Carolina Chapel Hill United States

In this paper, we introduce Dynamic Layer Operations (DLO), a novel approach for vertically scaling transformer-based Large Language Models (LLMs) by dynamically expanding, activating, or skipping layers using a sophisticated routing policy based on lay-erwise feature similarity. Unlike traditional Mixture-of-Experts (MoE) methods that focus on extending the model width, our approach targets model depth, addressing the redundancy observed across layer representations for various input samples. Our framework is integrated with the Supervised Fine-Tuning (SFT) stage, eliminating the need for resource-intensive Continual Pre-Training (CPT). Experimental results demonstrate that DLO not only outperforms the original unscaled models but also achieves comparable results to densely expanded models with significantly improved efficiency. Our work offers a promising direction for building efficient yet powerful LLMs. We will release our implementation and model weights upon acceptance. Copyright © 2024, The Authors. All rights reserved.

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Towards Scalable Kernel-Based Regularized System Identification

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Towards Scalable Kernel-Based Regularized System Identificat...

IEEE Conference on Decision and Control

作者： Lujing Chen Tianshi Chen Utkarsh Detha Martin S. Andersen Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs. Lyngby Denmark School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China MOSEK ApS Copenhagen Denmark

This paper proposes a methodology for scalable kernel-based regularized system identification based on indirect methods. It leverages stochastic trace estimation methods and an iterative solver such as LSQR for the efficient evaluation of hyperparameter selection criteria. It also uses a derivative-free optimization approach to hyperparameter estimation, which avoids the need for computing gradients or Hessians of the objective function. Moreover, the method is matrix-free, which means it only relies on a matrix-vector oracle and exploits fast routines for various structured matrix-vector products. Our preliminary numerical experiments indicate that the methodologygy scales significantly better than direct methods, especially when dealing with large datasets and slowly decaying impulse responses.

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Online Matching and Contention Resolution for Edge Arrivals with Vanishing Probabilities

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arXiv 2024年

作者： Ma, Will MacRury, Calum Nuti, Pranav Graduate School of Business Data Science Institute Columbia University United States Graduate School of Business Columbia University United States University of Chicago Booth School of Business United States

We study the performance of sequential contention resolution and matching algorithms on random graphs with vanishing edge probabilities. When the edges of the graph are processed in an adversarially-chosen order, we derive a new OCRS that is 0.382-selectable, attaining the "independence benchmark" from the literature under the vanishing edge probabilities assumption. Complementary to this positive result, we show that no OCRS can be more than 0.390-selectable, significantly improving upon the upper bound of 0.428 from the literature. We also derive negative results that are specialized to bipartite graphs or subfamilies of OCRS’s. Meanwhile, when the edges of the graph are processed in a uniformly random order, we show that the simple greedy contention resolution scheme which accepts all active and feasible edges is 1/2-selectable. This result is tight due to a known upper bound. Finally, when the algorithm can choose the processing order, we show that a slight tweak to the random order—give each vertex a random priority and process edges in lexicographic order—results in a strictly better contention resolution scheme that is 1 − ln(2 − 1/e) ≈ 0.510-selectable. Our positive results also apply to online matching on 1-uniform random graphs with vanishing (non-identical) edge probabilities, extending and unifying some results from the random graphs literature. Copyright © 2024, The Authors. All rights reserved.

关键词： Undirected graphs