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International Conference on Emerging Technologies, ICET

作者： Huzaifa Javed Usama Arshad Zain ul Abideen Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

Despite the significance of music genre classification in audio identification, it remains under-explored within AI research. This tool is crucial for personalized music recommendations and similar music detection. We have developed an efficient AI model that leverages Convolutional Neural Networks (CNNs), offering high-precision genre identification when integrated into a graphical user interface. The model effectively extracts audio features like Mel Frequency Cepstral Coefficients (MFCCs), zero-crossing rate, and tempo. Testing results reveal strong performance in genre prediction across diverse tracks, affirming the model's ability to discern unique characteristics of various music genres. This performance not only attests to the model's capability in discerning the unique characteristics inherent to different music genres but also suggests that it can effectively generalize to novel, unseen data. Our model lays the groundwork for future enhancements and demonstrates the potential of AI in transforming the music industry - from personalized music playlists to exploratory recommendation systems. The success of this model paves the way for more intricate applications of AI within music analysis.

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A Lightweight and Enhanced Semantic Segmentation Network for Mapping of Retrogressive Thaw Slumps from Sentinel-2 Images

A Lightweight and Enhanced Semantic Segmentation Network for...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yi Yuan Guiyun Zhou Zhonghua Su Ziyin Liu Weiwei Sun Xiangchao Meng Jie Wen Zhiqiang Wu School of Resources and Environment University of Electronic Science and Technology of China Chengdu China School of Computer and Software Engineering Xihua University Chengdu China Department of Geography and Spatial Information Techniques Ningbo University Ningbo China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Sichuan Provincial Public 7 Security Department Communications and Information Technology Headquarters Chengdu China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Fine mapping of retrogressive thaw slumps (RTSs) holds paramount significance in the study of permafrost degradation and carbon exchange. We propose a lightweight and enhanced semantic segmentation network (LessNet) for automatically mapping the RTSs from Sentinel-2 images. LessNet is constructed on the encoder-decoder framework with innovative incorporation of attention mechanism and dual-level semantic features fusion. The lightweight architecture of LessNet eliminates the need for pre-training, and the network hyperparameters are automatically updated based on the training dataset, which allows for fast convergence of supervised learning. Experiments conducted in the Beiluhe region of the Tibetan Plateau highlight the robustness and competitive performance of the model.

关键词： Training Semantic segmentation Supervised learning Transfer learning Semantics Robustness Permafrost

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Avast-CTU Public CAPE Dataset

arXiv

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arXiv 2022年

作者： Bošanský, Branislav Kouba, Dominik Maňhal, Ondřej Sick, Thorsten Lisý, Viliam Křoustek, Jakub Somol, Petr Avast Software Czech Republic Artificial Intelligence Center Dept. of Computer Science Faculty of Electrical Engineering Czech Tecnical University in Prague Czech Republic

There is a limited amount of publicly available data to support research in malware analysis technology. Particularly, there are virtually no publicly available datasets generated from rich sandboxes such as Cuckoo/CAPE. The benefit of using dynamic sandboxes is the realistic simulation of file execution in the target machine and obtaining a log of such execution. The machine can be infected by malware hence there is a good chance of capturing the malicious behavior in the execution logs, thus allowing researchers to study such behavior in detail. Although the subsequent analysis of log information is extensively covered in industrial cybersecurity backends, to our knowledge there has been only limited effort invested in academia to advance such log analysis capabilities using cutting edge techniques. We make this sample dataset available to support designing new machine learning methods for malware detection, especially for automatic detection of generic malicious behavior. The dataset has been collected in cooperation between Avast software and Czech Technical University - AI Center (AIC). Copyright © 2022, The Authors. All rights reserved.

关键词： Malware

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Why There Are Still Moral Reasons to Prefer Extended over Embedded: a (Short) Reply to Cassinadri

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Philosophy & Technology 2022年第3期35卷 1-7页

作者： Farina, Mirko Lavazza, Andrea Faculty of Humanities and Social Sciences Innopolis University Universitetskaya St 1 Innopolis Russian Federation Lab of Industrializing Software Production (LIPS) Faculty of Computer Science and Engineering Universitetskaya St 1 Innopolis Russian Federation Centro Universitario Internazionale Arezzo Italy

In a recent paper, Cassinadri (2022) raised substantial criticism about the possibility of using moral reasons to endorse the hypothesis of extended cognition (EXT) over its most popular alternative, the embedded view (EMB). In particular, Cassinadri criticized 4 of the arguments we formulated to defend EXT and argued that our claim that EXT might be preferable to EMB (on the grounds of its progressiveness and inclusiveness) does not stand close scrutiny. In this short reply, we point out—contra Cassinadri—why we still believe that there are moral reasons to prefer EXT over EMB, hence why we think that the former is more inclusive and more progressive than the latter.

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EHG Signal Analysis for Prediction of Term and Preterm using Variational Mode Decomposition and Artificial Neural Networks

EHG Signal Analysis for Prediction of Term and Preterm using...

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Frontiers of Information Technology (FIT)

作者： Muhammad Umar Khan Sumair Aziz Khushbakht Iqtidar Raul Fernandez Rojas Department of Electronics Engineering University of Engineering and Technology Taxila Taxila Pakistan Department of Computer Software Engineering National University of Sciences and Technology Islamabad Pakistan Faculty of Science & Technology University of Canberra Canberra Australia

ISBN: (纸本)9798350345940

Preterm deliveries are an important cause of mortality and morbidity in newborns. Accurate and early prediction of a premature delivery can prove helpful in providing proper medication and treatment. Recording of electrical activity known as Electrohysterogram (EHG) from the abdominal surface of pregnant women corresponds to the uterus contractions. A new direction is open using EHG signals for the diagnosis of preterm births. In this research, we present a new method for the accurate classification of preterm and term EHG signals. The proposed method first filters a three-channel EHG signal using bandpass filters. Next, we combined the filtered three-channel EHG into one signal using an accumulation operation. The accumulated EHG signal was post-processed through variational mode decomposition (VMD). VMD algorithm splits the input signal into finite modes using center frequencies known as intrinsic mode functions (IMFs). An energy-based intelligent signal reconstruction approach is designed to combine IMFs having an energy level above the computed threshold. Next, the reconstructed EHG signals were split into continuous windows, and time, frequency, and Hjorth features were extracted. These features were fused to construct a distinct feature representation and were reduced using the ReliefF algorithm. We trained an artificial neural network (ANN) to obtain 98.8 % average accuracy using 10-fold cross-validation.

关键词： Band-pass filters Time-frequency analysis Artificial neural networks Filtering algorithms Feature extraction Signal reconstruction Classification algorithms

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Genetic Algorithm-Based Feature Selection for Accurate Breast Cancer Classification

Genetic Algorithm-Based Feature Selection for Accurate Breas...

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IT and Industrial Technologies (ICIT), International Conference on

作者： Attia Shabbir Raja Hashim Ali Muhammad Zeeshan Shabbir Zain Ul Abideen Talha Ali Khan Ali Zeeshan Ijaz Nisar Ali Muhammad Imad Muhammad Abu Bakar Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Department of Electrical Engineering University of Engineering & Technology (UET) Lahore Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Faculty of Electronic Systems Engineering University of Regina Regina Canada

Breast cancer is a significant global healthcare challenge, particularly in developing and underdeveloped countries, with profound physical, emotional, and psychological consequences, including mortality. Timely diagnosis and accurate treatment are crucial in addressing this issue. We propose the utilization of a feature selection technique to identify the most relevant features from among all features for breast cancer diagnosis, and show that Genetic Algorithms are impressive for this task. The study compares the results of GA with no selection and an alternative method, Principle Component Analysis (PCA). Three machine learning models, all based on supervised learning with data split into training and test data, are employed for binary classification using the selected feature subset. The evaluation metrics employed encompass accuracy, precision, recall, and F1-score. Among the selected models, Random Forest demonstrates the most favorable outcomes, achieving an accuracy score of 0.96, precision score of 0.96, recall value of 0.98, and an F1-score of 0.97. These results underscore the effectiveness of GA in feature selection for breast cancer diagnosis. Consequently, the integration of Genetic Algorithms (GA) with Random Forest showcases the superior performance among the evaluated models.

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An Enhanced Genetic Algorithm Framework for Efficient Solutions to Capacitated Vehicle Routing Problems

An Enhanced Genetic Algorithm Framework for Efficient Soluti...

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International Conference on Emerging Technologies, ICET

作者： Usama Arshad Hamza Hasan Ellahie Zain ul Abideen Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

Capacitated Vehicle Routing Problems (CVRPs), a widely acknowledged NP-hard issue, pertains to the optimal routing of a limited-capacity vehicle fleet to fulfill customer demand, aiming for the least possible travel distance or cost. Despite the presence of numerous heuristic and exact approaches, the combinatorial characteristic of CVRP renders it challenging, especially for large-scale instances. This research provides an in-depth exploration of utilizing Genetic Algorithms (GAs) to address Capacitated Vehicle Routing Problems (CVRPs), a recognized and intricate optimization issue in the realm of logistics and supply chain management. Our paper concentrates on the innovative usage of GAs, a category of stochastic search methodologies inspired by natural selection and genetics, to grapple with CVRP. We put forth a fresh framework grounded in GA that infuses unique crossover and mutation operations tailor-made for CVRP. Our comprehensive computational trials on benchmark datasets suggest that our GA-centric method is proficient in deriving high-standard solutions within acceptable computational durations, surpassing multiple contemporary techniques concerning solution quality and resilience. Our results also underscore the scalability of our proposed approach, marking it as a viable choice for tackling extensive, real-world CVRPs. This paper enriches the current knowledge bank by demonstrating the prowess of GAs in deciphering complicated combinatorial optimization issues, thus offering a novel viewpoint for future advancements in crafting more robust and efficient CVRP resolutions.

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AdaptiveCloset: Reinforcement Learning in Personalized Clothing Recommendations

AdaptiveCloset: Reinforcement Learning in Personalized Cloth...

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International Conference on Emerging Technologies, ICET

作者： Omar Kashif Majeed Zain ul Abideen Usama Arshad Raja Hashim Ali Abdullah Habib Rafay Mustafa Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

The fashion industry, with its myriad choices, often overwhelms consumers. Addressing this, AdaptiveCloset introduces a groundbreaking approach to tailoring clothing suggestions by harnessing the power of reinforcement learning (RL). Unlike conventional AI methodologies in a fashion that merely suggests based on past preferences, our system dynamically ad-justs using realtime user feedback, ensuring that recommendations remain relevant and personalized. Historically, AI's fusion into the fashion realm has witnessed multiple methodologies but conspicuously lacked the RL perspective. Our research stands at this juncture, aiming to redefine online shopping experiences. Through AdaptiveCloset, we envisage a scenario where online shoppers not only receive personalized recommendations but also feel a sense of involvement, thanks to the system's feedback-oriented adaptability. This responsiveness not only augments user engagement but provides actionable intelligence for businesses, bridging the gap between consumer desires and market offerings. In this study, our focus was to ensure the robustness and adaptability of the RL environment, positioning it as a potent tool for enhancing e-commerce interactions, optimizing sales strategies, and fortifying customer loyalty.

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Electrocoagulation-based AZO DYE (P4R) Removal Rate Prediction Model using Deep Learning

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Procedia computer Science 2024年 236卷 51-58页

作者： Meryem Akoulih Smail Tigani Fouzia Byoud Meryem El Rharib Rachid Saadane Samuel Pierre Abdellah Chehri Sanae El Ghachtouli IME Lab Faculty of Science Hassan 2 University 20250 Casablanca Morocco Research and Development Unit Accsellium LLC Fez Morocco Electrical Engineering Department SIRC-LaGeS Hassania School of Public Labors 20250 Casablanca Morocco Department of Computer and Software Engineering Polytechnique Montréal Montréal Québec Canada Department of Mathematics and Computer Science Royal Military College of Canada Kingston ON 11 K7K 7B4 Canada

This article presents a study on the effectiveness of electrocoagulation (EC) for the removal of azo dyes from wastewater. The analysis was performed using a combination of statistical methods, including density estimation, correlation analysis, and deep learning for electrocoagulation performance prediction. The results showed that electrocoagulation was able to effectively remove azo dyes from the wastewater, considering the energy consumption and the mass of flocs being important factors in the process. Deep Learning (DL) is used to build our predictive model using the datasets collected during the experimentation stage. Overall, the findings suggest that electrocoagulation is a promising technique for the treatment of wastewater containing azo dyes, and that the use of statistical and machine learning methods can aid in the optimization of the process.

关键词： Electrocoagulation Wastewater Azo dye Deep learning

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DSD-MatchingNet:Deformable sparse-to-dense feature matching for learning accurate correspondences

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Virtual Reality & Intelligent Hardware 2022年第5期4卷 432-443页

作者： Yicheng ZHAO Han ZHANG Ping LU Ping LI Enhua WU Bin SHENG Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China ZTE Corporation Shenzhen 518057China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen 518057China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China School of Design The Hong Kong Polytechnic UniversityHong Kong 999077China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

Background Exploring correspondences across multiview images is the basis of various computer vision ***,most existing methods have limited accuracy under challenging *** To learn more robust and accurate correspondences,we propose DSD-MatchingNet for local feature matching in this ***,we develop a deformable feature extraction module to obtain multilevel feature maps,which harvest contextual information from dynamic receptive *** dynamic receptive fields provided by the deformable convolution network ensure that our method obtains dense and robust ***,we utilize sparse-to-dense matching with symmetry of correspondence to implement accurate pixel-level matching,which enables our method to produce more accurate *** Experiments show that our proposed DSD-MatchingNet achieves a better performance on the image matching benchmark,as well as on the visual localization ***,our method achieved 91.3%mean matching accuracy on the HPatches dataset and 99.3%visual localization recalls on the Aachen Day-Night dataset.

关键词： Image matching Deformable convolution network Sparse-to-dense matching

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