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1st International Conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Jayashree Mallick, Ahmed Faraz Das, Ashutosh Singh, Abhiraj Shivaprakash, T. Preethi Salian, K. Nitte Meenakshi Institute of Technology Dept of Computer Science and Engineering Bangalore India Nitte Meenakshi Institute of Technology Dept of Computer Science and Engineering India Vijaya Vittala Institute of Technology Dept of Computer Science and Engineering Bangalore India Nmamit Dept of Information Science and Engineering Nitte Udupi India

ISBN: (纸本)9798331529246

An essential diagnostic technique for brain tumors, Magnetic Resonance Imaging (MRI) enables early detection and improved patient outcomes. However, manual interpretation of MRI scans can be time-consuming and subject to human error. Deep learning has revolutionized medical image analysis by providing automated and accurate classifications, significantly enhancing diagnostic processes. This study leverages the pre-trained Convolutional Neural Network (CNN) model Xception to classify MRI brain data, demonstrating its effectiveness in identifying brain tumors. The model was trained and validated using a diverse dataset, achieving a high test accuracy of 98.78% and training accuracy of 99.96%. These findings highlight the potential of the Xception model in supporting clinical decision-making and reducing diagnostic workloads. The study contributes to the field by showcasing Xception's implementation, dataset preparation, model design, training, and evaluation. Furthermore, it emphasizes the importance of using deep learning techniques for reliable and efficient brain tumor classification, setting the groundwork for future advancements in automated diagnostic systems. © 2025 IEEE.

关键词： Convolutional neural networks

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A Novel Ensemble-Based Framework for Feature Identification and Classification of COVID-19 Electronic Health Record Data

A Novel Ensemble-Based Framework for Feature Identification ...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Mahajan, Asmita Toshniwal, Durga Indian Institute of Technology Computer Science & Engineering Roorkee India

ISBN: (纸本)9798350324457

The COVID-19 pandemic has resulted in widespread illness and death around the world. Massive and complex Electronic health records (EHRs) hold important information to help COVID-19 patients get an accurate diagnosis and prognosis. In this research, we suggested a novel ensemble of decision trees method for analyzing and classifying COVID-19 EHR data. The proposed approach uses misclassified samples at each iteration to improve accuracy. Pneumonia, hypertension, diabetes, male gender, and higher age groups were the most important features in classifying patients into 'COVID-negative' 'COVID-positive and survived' and 'COVID-positive and died' To evaluate the efficacy of our suggested ensemble model, we contrasted it with well-established models. Compared to the baseline models, the experimental findings demonstrated that the ensemble model greatly outperformed in terms of accuracy, precision, recall, and F1 score. This research shows the significance of EHRs in COVID-19 patient classification and suggests an innovative method to improve classification accuracy. The most prominent traits in high-risk patients can serve as early warning indicators, allowing for quick intervention and better outcomes overall. The suggested ensemble model has practical applications in the early detection and efficient patient treatment of COVID-19. © 2023 IEEE.

关键词： big data analysis COVID-19 electronic health records ensemble learning machine learning

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A Novel Optimized Deep Convolutional Neural Network for Efficient Seizure Stage Classification

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computers, Materials & Continua 2024年第12期81卷 3903-3926页

作者： Umapathi Krishnamoorthy Shanmugam Jagan Mohammed Zakariah Abdulaziz S.Almazyad K.Gurunathan Department of Biomedical Engineering KIT-Kalaignarkarunanidhi Institute of TechnologyCoimbatore641402India Department of Computer Sciences and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai600062India Department of Computer Sciences and Engineering College of Applied ScienceKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science and Engineering Mount Zion College of Engineering and TechnologyPudukkottai622507India

Brain signal analysis from electroencephalogram(EEG)recordings is the gold standard for diagnosing various neural disorders especially epileptic *** signals are highly chaotic compared to normal brain signals and thus can be identified from EEG *** the current seizure detection and classification landscape,most models primarily focus on binary classification—distinguishing between seizure and non-seizure *** effective for basic detection,these models fail to address the nuanced stages of seizures and the intervals between *** identification of per-seizure or interictal stages and the timing between seizures is crucial for an effective seizure alert *** granularity is essential for improving patient-specific interventions and developing proactive seizure management *** study addresses this gap by proposing a novel AI-based approach for seizure stage classification using a Deep Convolutional Neural Network(DCNN).The developed model goes beyond traditional binary classification by categorizing EEG recordings into three distinct classes,thus providing a more detailed analysis of seizure *** enhance the model’s performance,we have optimized the DCNN using two advanced techniques:the Stochastic Gradient Algorithm(SGA)and the evolutionary Genetic Algorithm(GA).These optimization strategies are designed to fine-tune the model’s accuracy and ***,k-fold cross-validation ensures the model’s reliability and generalizability across different data *** and validated on the Bonn EEG data sets,the proposed optimized DCNN model achieved a test accuracy of 93.2%,demonstrating its ability to accurately classify EEG *** summary,the key advancement of the present research lies in addressing the limitations of existing models by providing a more detailed seizure classification system,thus potentially enhancing the effectiveness of real-time seizure prediction and management systems in clinic

关键词： Bonn EEG dataset cross-validation genetic algorithm batch normalization seizure classification stochastic gradient

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Memory leak detection using Heap Object Flow Graph (HOFG) 23

Memory leak detection using Heap Object Flow Graph (HOFG)

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16th Innovations in Software engineering Conference, ISEC 2023

作者： Haritha Madhav, C. Cheramangalath, Unnikrishnan Computer Science and Engineering Indian Institute of Technology Palakkad India

ISBN: (纸本)9798400700644

Unsafe programming languages like C/C++ lack efficient memory management module. A programmer is privileged to do explicit allocation and deallocation of heap memory blocks in C/C++ programming languages. Memory errors related to heap memory are difficult to capture using software testing in such programming languages. Program analysis can improve the reliability of software by identifying and repairing bugs related to dynamic memory management. An efficient static analyzer needs to look at the control flow graph of the whole program binary. Capturing context, path, and flow sensitivity is challenging using abstract interpretation and can lead to over approximated results. We propose Heap Object Flow Graph (HOFG), a program representation that eases program analysis to detect memory errors using static program analysis. An efficient program analysis on HOFG called HOFG-Analyser is defined, to capture memory leaks. We were able to detect memory leaks on different benchmarks written in C/C++ programming language with sizes up to 521K Lines of Code (LoC) with minimal false positive rates. Our experimental evaluation show that HOFG-Analyser is efficient and effective than an existing static analysis tool, INFER. In many cases, more number of leaks and less false positive rate is achieved by the HOFG-Analyser. © 2023 ACM.

关键词： Flow graphs

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ASL-OOD:Hierarchical Contextual Feature Fusion with Angle-Sensitive Loss for Oriented Object Detection

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computers, Materials & Continua 2025年第2期82卷 1879-1899页

作者： Kexin Wang Jiancheng Liu Yuqing Lin Tuo Wang Zhipeng Zhang Wanlong Qi Xingye Han Runyuan Wen Northwest Institute of Mechanical and Electrical Engineering Xianyang712099China School of Information Engineering Chang’an UniversityXi’an710064China School of Computer Science and Technology Xidian UniversityXi’an710071China

Detecting oriented targets in remote sensing images amidst complex and heterogeneous backgrounds remains a formidable challenge in the field of object *** frameworks for oriented detection modules are constrained by intrinsic limitations,including excessive computational and memory overheads,discrepancies between predefined anchors and ground truth bounding boxes,intricate training processes,and feature alignment *** overcome these challenges,we present ASL-OOD(Angle-based SIOU Loss for Oriented Object Detection),a novel,efficient,and robust one-stage framework tailored for oriented object *** ASL-OOD framework comprises three core components:the Transformer-based Backbone(TB),the Transformer-based Neck(TN),and the Angle-SIOU(Scylla Intersection over Union)based Decoupled Head(ASDH).By leveraging the Swin Transformer,the TB and TN modules offer several key advantages,such as the capacity to model long-range dependencies,preserve high-resolution feature representations,seamlessly integrate multi-scale features,and enhance parameter *** improvements empower the model to accurately detect objects across varying *** ASDH module further enhances detection performance by incorporating angle-aware optimization based on SIOU,ensuring precise angular consistency and bounding box *** approach effectively harmonizes shape loss and distance loss during the optimization process,thereby significantly boosting detection *** evaluations and ablation studies on standard benchmark datasets such as DOTA with an mAP(mean Average Precision)of 80.16 percent,HRSC2016 with an mAP of 91.07 percent,MAR20 with an mAP of 85.45 percent,and UAVDT with an mAP of 39.7 percent demonstrate the clear superiority of ASL-OOD over state-of-the-art oriented object detection *** findings underscore the model’s efficacy as an advanced solution for challenging remote sensing object detection tasks.

关键词： Oriented object detection transformer deep learning

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Debnath, A. Rao, K. Sreenivasa Das, Partha P. Engineering Entrepreneurship Indian Institute of Technology Kharagpur India Computer Science & Engineering Indian Institute of Technology Kharagpur India

ISBN: (纸本)9798350335095

Video digitization is considered as the emerging technologies essential for the applications of video compression and video recording. There are diverse approaches feasible for effectively retrieving the videos in the conventional research work. This research work presents a video retrieval scheme based on the processing of multimodal lecture videos by performing an efficient similarity checking between the query text with the appropriate videos. Initially, different types of lecture videos are obtained through the standard online resources. Then, in the training phase, the selection of optimal key frames takes place and also it gets indexed as each segment using Rat Swarm Optimizer (RSO) under the optimal video segmentation phase. Further, the feature pool generation is made with the extraction of deep features like text and image using BERT and Convolutional Neural Network (CNN) from the segmented video frames. These features are stored in the feature database. Further, in the testing phase, the text query is obtained and also its features is acquired and undergo cosine similarity checking for retrieving the appropriate videos for respective queries. The main objective is to maximize the variance of retrieved videos. The analysis outcomes demonstrate the effective performance of the developed video retrieval and indexing system by comparing with the conventional models. © 2023 IEEE.

关键词： Video recording

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The Future of Intelligence: Advancements and Challenges in AI and Machine Learning 15

The Future of Intelligence: Advancements and Challenges in A...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Pushpa, R. Rekha, H. Akshaya Institute of Technology Department of computer Science and Engineering Tumakuru India Shridevi Institute of Engineering and Technology Department of Information Science and Engineering Tumakuru India

ISBN: (纸本)9798331300579

The incorporation of Machine Learning (ML) and Artificial Intelligence (AI) technologies across diverse industries presents considerable potential for both economic expansion and societal ***, which includes both Narrow and General forms, in conjunction with ML, enables data-driven decision making and automation in numerous industries, including healthcare, finance, and ***, the extensive implementation of these technologies is beset by obstacles such as ambiguity in algorithms, ethical concerns, and *** paper studies the developments and obstacles in Al and ML, as well as their practical implementations in nanotechnology, medicine, governance, and *** the potential for economic benefits and societal advancements is apparent, it is imperative to prioritize responsible and equitable implementation of Al by addressing organizational shortcomings, personnel issues, and economic *** is imperative that governments, organizations, and academia work together in concert to surmount these challenges and fully exploit the capabilities of Al and ML for the betterment of *** paper emphasizes the significance of conducting a thorough analysis of the ethical, societal, and economic consequences in order to effectively achieve the potential of Al and ML. © Grenze Scientific Society, 2024.

关键词： Adversarial machine learning

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Agriconnect: Revolutionizing Farm Machinery Booking 2

Agriconnect: Revolutionizing Farm Machinery Booking

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2nd IEEE International Conference on Advances in information technology, ICAIT 2024

作者： Subrahmanya Pradhan, Trilochan Shaikh, Sauda Shabbir Shetty, Ruchitha N. Shreekumar, T. Department of Information Science & Engineering Mangalore Institute of Technology and Engineering Moodabidri India Department of Computer Science & Engineering Mangalore Institute of Technology and Engineering Moodabidri India

ISBN: (纸本)9798350383867

Crop assessment plays an important role in ensuring food safety, and recent technological advances such as machine learning and deep learning have revolutionized assessment, and crop and culture management. Agriconnect is the largest network combining image-based crop analysis with rental equipment to optimize agriculture. Agriconnect uses the latest image processing technology to identify crops from field images, allowing farmers to make informed decisions about their crops. In addition, the platform supports easy-to-assemble equipment, allowing farmers to adapt machines to their specific needs, thus reducing waste and improving resource allocation. Agriconnect simplifies the farming process and increases overall profitability by providing a user-friendly interface for product identification and equipment booking. By combining forward-thinking technology with powerful management tools, Agriconnect not only improves the accuracy of farming but also improves efficiency. He envisions a future in which technology will play a key role in making farming easier and more efficient for farmers around the world. Agriconnect is a prime example of how technology is changing agriculture, enabling farmers to make informed decisions, improving resource use, and ultimately increasing production to implement permaculture practices. © 2024 IEEE.

关键词： Algae control

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FinSecure: Utilizing IoT Sensors for Formaldehyde Detection and Fish Freshness Detection for Enhancing Safety in Fish Consumption Using Machine Learning and Deep Learning

FinSecure: Utilizing IoT Sensors for Formaldehyde Detection ...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Harish, S. Somesh, K. Suntharavadivelan Venkatesan, M. Prabhavathy, P. National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India Vellore Institute of Technology Department of Computer Science and Engineering and Information Systems Vellore India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540685

The global food supply heavily relies on fisheries, highlighting the crucial importance of ensuring the safety of fish products. However, the widespread application of antibiotics and the existence of compounds such as formaldehyde present significant hazards to both human consumers and the underwater ecosystem. These risks are compounded by processes like bio-magnification, further emphasizing the need for vigilant monitoring and regulation. This project presents a comprehensive approach to fisheries management and food safety monitoring through the integration of advanced machine learning, deep learning techniques and IoT sensor technology. The overall objective is to develop a model that can determine whether the given sample Fish Species is safe or unsafe for human consumption, by considering parameters: Fish Freshness Detection, using a HCHO Sensor to determine the Formaldehyde levels in the given Fish and finally using these parameters to determine whether the fish can be consumed or not. For the Fish Freshness Detection, utilizing Transfer Learning (pre-trained MobileNetV2 model), the system achieves an impressive accuracy of 99.02%. Thus, integrating all these models, determine whether the supplied fish is safe for consumption or not. By integrating these functionalities, the system contributes to proactive fisheries and food safety monitoring and protection. The holistic approach of this project, combining accurate positions the developed model and IoT application as valuable assets for sustainable aquaculture practices and the preservation of aquatic ecosystems. The project's outcomes contribute to the advancement of several interdisciplinary domains such as machine learning, and fisheries management, laying the groundwork for continued innovation in this interdisciplinary domain. © 2024 IEEE.

关键词： Fish

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A Multi-Texture Based Feature Vector for Classification of MR Images of Patients Suffering from Dementia 2

A Multi-Texture Based Feature Vector for Classification of M...

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2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Soni, Samarth Garg, Khushi Srivastava, Varun Jaypee Institute of Information Technology Computer Science and Engineering Uttar Pradesh Noida India Jaypee Institute of Information Technology Computer Science Department Uttar Pradesh Noida India

ISBN: (纸本)9798350372847

Dementia is a prevalent neurological disorder affecting millions globally which poses significant challenges in early detection and classification. In this research work, a hybrid texture based feature extraction method is presented for classification of Magnetic Resonance Imaging brain images having dementia using machine learning techniques. The study investigates diverse methodologies, including extensive image pre-processing and feature extraction methods based on multiple textures to enhance structural details crucial for effective analysis. Results indicate that while Support Vector Classifier exhibits a moderate performance with 50.4% accuracy, Gradient Boosting Classifier and Random Forest Classifier showcase commendable accuracies of 92.7% and 99.0%, respectively. Remarkably, Simple Neural Network Classifier outperforms all classifiers with a remarkable accuracy of 99.7%. Comparative analysis highlight Simple Neural Network's superior capability, emphasizing its potential as a reliable tool for precise image classification is also presented. © 2024 IEEE.

关键词： Feature extraction

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