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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Hafsath, C.A. Jereesh, A.S. Bio-informatics Lab Department of Computer Science Cochin University of Science and Technology Kerala 682022 India

Combinations of drugs have demonstrated potential therapeutic results in controlling the progression of cancer, with a possible decrease in toxicity and unfavorable side efects. Experimental screening is no longer feasible due to the vast number of possible medication combinations. As a result, the scientific community is becoming more and more interested in creating computational models that can quickly and precisely identify prospective drug combinations. Our methodology employs therapeutically significant drugs and cell line properties in machine learning techniques. Our suggested stacked machine learning model outperforms all other machine learning models taken into consideration. It employs Random Forest and XGBoost as base learners and Logistic Regression as a meta learner. The results of our research underscore the remarkable efficacy of our approach. Not only does it address the complexities of the task at hand, but it also showcases its potential to enhance predictive accuracy within this domain. The comparative analysis reveals that our model exhibits a noteworthy performance improvement across multiple evaluation metrics. Our findings represent a significant step forward in the quest for improved cancer treatment strategies. © 2024 Elsevier B.V.. All rights reserved.

关键词： Diseases

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An Ensemble-Based Machine Learning Approach to Identify SARS-CoV-2 Virus Infection by Analyzing S Protein Sequences 2nd

An Ensemble-Based Machine Learning Approach to Identify SARS...

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2nd International Conference on Big Data, IoT and Machine Learning, BIM 2023

作者： Moni, Raka Zahid Hasan, Md. Shahriar Shakil, Md. Ferdous, Most. Jannatul Arefin, Mohammad Shamsul Bhuiyan, Touhid Health Informatics Research Lab Computer Science and Engineering Daffodil International University Dhaka Bangladesh Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong Bangladesh

ISBN: (纸本)9789819989362

Coronavirus disease (COVID-19), an effect of the SARS-CoV-2 virus, is an emerging infectious disease that infects humans due to interspecies transmission. Typically, Spike (S) protein plays a crucial role in entering host cells and beginning infection in contrast to the Membrane (M) and Envelope (E) proteins which are primarily involved in virus assembly. The purpose of this research is to examine the S protein sequences of the human virus to forecast SARS-CoV-2 infection potential. Using computational modeling and bioinformatics approaches, we analyzed the dataset taken from the Virus Pathogen Database except for 47 SARS2-CoV sequences from the SARS2-CoV Database containing aligned sequences of lengths 2396. This study suggests using several machine learning methods to determine infectivity based on the sequence of the coronavirus spike protein. The classical machine learning approach such as k-nearest neighbors (KNNs), Gaussian Naive Bayes (GaussianNB), support vector machine (SVM), decision tree (DT) is used. Bagging Classifier, Gradient Boosting algorithm, and Random Forest are used as the ensemble machine learning algorithm. Artificial Neural Network (ANN) is also applied as a deep learning model. The Gradient Boosting algorithm demonstrates the best accuracy in determining infectivity status. According to our research, the outcome of this study has a chance to accurately forecast coronavirus infectivity from genetic information. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Coronavirus

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Disentangling Prosody Representations With Unsupervised Speech Reconstruction

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IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 32卷 39-54页

作者： Qu, Leyuan Li, Taihao Weber, Cornelius Pekarek-Rosin, Theresa Ren, Fuji Wermter, Stefan Institute of Artificial Intelligence Zhejiang Lab Hangzhou311121 China Department of Informatics University of Hamburg Hamburg20148 Germany School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

Human speech can be characterized by different components, including semantic content, speaker identity and prosodic information. Significant progress has been made in disentangling representations for semantic content and speaker identity in speech recognition and speaker verification tasks respectively. However, it is still an open challenging question to extract prosodic information because of the intrinsic association of different attributes, such as timbre and rhythm, and because of the need for supervised training schemes to achieve robust speech recognition. The aim of this article is to address the disentanglement of emotional prosody based on unsupervised reconstruction. Specifically, we identify, design, implement and integrate three crucial components in our proposed model Prosody2Vec: (1) a unit encoder that transforms speech signals into discrete units for semantic content, (2) a pretrained speaker verification model to generate speaker identity embeddings, and (3) a trainable prosody encoder to learn prosody representations. We first pretrain Prosody2Vec on unlabelled emotional speech corpora, then fine-tune the model on specific datasets to perform Speech Emotion Recognition (SER) and Emotional Voice Conversion (EVC) tasks. Both objective and subjective evaluations on the EVC task suggest that Prosody2Vec effectively captures general prosodic features that can be smoothly transferred to other emotional speech. In addition, our SER experiments on the IEMOCAP dataset reveal that the prosody features learned by Prosody2Vec are complementary and beneficial for the performance of widely used speech pretraining models and surpass the state-of-the-art methods when combining Prosody2Vec with HuBERT representations. Audio samples can be found on our demo website. © 2023 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

关键词： Speech recognition

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Malware incident response(IR) informed by cyber threat intelligence(CTI)

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science China(Information sciences) 2022年第7期65卷 256-258页

作者： Ying HE Ellis INGLUT Cunjin LUO School of Computer Science and Informatics De Montfort University Key Lab of Medical Electrophysiology Ministry of Education The Health Informatics Group Institute of Cardiovascular Research Southwest Medical University School of Computer Science and Engineering Northeastern University

Dear editor, Security experts have been fighting against cybercriminals for many years and existing research shows that this battle will continue. Malicious software has no remorse when it targets different organizations, regardless of its forms [1]. Ransomware [2] has caused serious issues in different industries, especially in healthcare. The existing report shows that 34% of ransomware is targeting healthcare organizations. Nowadays, criminals prefer crypto-jacking over ransomware (which also relies on cryptocurrency for anonymous ransom payments).

关键词： Malware Analysis Cyber Threat Intelligence Crypto Jacking Incident Response Malware Incident Response

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SUSTEM: An Improved Rule-based Sundanese Stemmer

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ACM Transactions on Asian and Low-Resource Language Information Processing 2024年第6期23卷 1-29页

作者： Setiawan, Irwan Kao, Hung-Yu Department of Computer and Informatics Engineering Politeknik Negeri Bandung Bandung Indonesia Intelligent Knowledge Management Lab Department of Computer Science and Information Engineering National Cheng Kung University No. 1 Daxue Rd. East Dist. Tainan City701 Taiwan

Current Sundanese stemmers either ignore reduplication words or define rules to handle only affixes. There is a significant amount of reduplication words in the Sundanese language. Because of that, it is impossible to achieve superior stemming precision in the Sundanese language without addressing reduplication words. This article presents an improved stemmer for the Sundanese language, which handles affixed and reduplicated words. With a Sundanese root word list, we use a rules-based stemming technique. In our approach, all stems produced by the affixes removal or normalization processes are added to the stem list. Using a stem list can help increase stemmer accuracy by reducing stemming errors caused by affix removal sequence errors or morphological issues. The current Sundanese language stemmer, RBSS, was used as a comparison. Two datasets with 8,218 unique affixed words and reduplication words were evaluated. The results show that our stemmer’s strength and accuracy have improved noticeably. The use of stem list and word reduplication rules improved our stemmer’s affixed type recognition and allowed us to achieve up to 99.30% accuracy. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Sundanese language reduplication word affixed word stemming

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RecStitchNet:Learning to stitch images with rectangular boundaries

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Computational Visual Media 2024年第4期10卷 687-703页

作者： Yun Zhang Yu-Kun Lai Lang Nie Fang-Lue Zhang Lin Xu Key Lab of Film and TV Media Technology of Zhejiang Province College of Media EngineeringCommunication University of ZhejiangHangzhou 310018China School of Computer Science and Informatics Cardiff UniversityCardiff CF244AGUK Institute of Information Science Beijing Jiaotong UniversityBeijing 100091China School of Engineering and Computer Science Victoria University of WellingtonWellington 6012New Zealand STEM University of South AustraliaAdelaide 5095Australia

Irregular boundaries in image stitching naturally occur due to freely moving *** deal with this problem,existing methods focus on optimizing mesh warping to make boundaries regular using the traditional explicit ***,previous methods always depend on hand-crafted features(e.g.,keypoints and line segments).Thus,failures often happen in overlapping regions without distinctive *** this paper,we address this problem by proposing RecStitchNet,a reasonable and effective network for image stitching with rectangular *** that both stitching and imposing rectangularity are non-trivial tasks in the learning-based framework,we propose a three-step progressive learning based strategy,which not only simplifies this task,but gradually achieves a good balance between stitching and imposing *** the first step,we perform initial stitching by a pre-trained state-of-the-art image stitching model,to produce initially warped stitching results without considering the boundary ***,we use a regression network with a comprehensive objective regarding mesh,perception,and shape to further encourage the stitched meshes to have rectangular boundaries with high content ***,we propose an unsupervised instance-wise optimization strategy to refine the stitched meshes iteratively,which can effectively improve the stitching results in terms of feature alignment,as well as boundary and structure *** to the lack of stitching datasets and the difficulty of label generation,we propose to generate a stitching dataset with rectangular stitched images as pseudo-ground-truth labels,and the performance upper bound induced from the it can be broken by our unsupervised *** and quantitative results and evaluations demonstrate the advantages of our method over the state-of-the-art.

关键词： image stitching boundaries convolutional neural network

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A Stacked Ensemble Approach For Enhancing Anti Cancer Drug Synergy Prediction

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Procedia computer science 2024年 235卷 2567-2576页

作者： C.A. Hafsath A.S. Jereesh Bio-informatics Lab Department of Computer Science Cochin University of Science and Technology Kerala 682022 India

关键词： drug synergy cancer machine learning drug combination

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Improving Big Data Recommendation System Performance Using NLP Techniques With Multi-attributes

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Informatica (Slovenia) 2024年第5期48卷 63-70页

作者： Omar, Hoger K. Frikha, Mondher Jumaa, Alaa Khalil ENETCOM ATISP Research Lab University of Sfax Sfax Tunisia Department of Computer Science College of Computer Science and Information Technology University of Kirkuk Kirkuk Iraq Department of Electronics National School of Electronics and Telecommunications of Sfax University of Sfax Tunisia Database Technology Department Technical College of Informatics Sulaimani Polytechnic University Kurdistan Region Sulaimani46001 Iraq

Due to the wide availability of big data, institutions and companies are currently concentrating on developing highly effective recommender systems for their users. Traditional recommender systems use standard information such as user, item, and ratings. However, this data may not be sufficient for precise results. To enhance accuracy, it is recommended to include additional information such as textual data in the recommendation system. When dealing with large textual data, employing Natural Language Processing (NLP) techniques is essential for effective data analysis. Therefore, this work proposed a novel big data recommender system that enhances collaborative filtering (CF) results by leveraging NLP techniques and dealing with multiple attributes. The study constructs two big data recommendation system models using a machine learning algorithm. In both models, the Alternating Least Squares (ALS) algorithm within the Apache Spark big data tool has been used. The first model did not incorporate NLP techniques, while the second model considered the novel NLP techniques by taking into account the user's review comments. A dataset of more than 3 million ratings and reviews was gathered from the Amazon website with a size of 3.1 GB. The results showed significant improvement after incorporating the suggested NLP-based techniques with multiple attributes. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Recommender systems

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Evaluation of a Contactless Accelerometer Sensor System for Heart Rate Monitoring During Sleep

Evaluation of a Contactless Accelerometer Sensor System for ...

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International Conference on Applications in Electronics Pervading Industry, Environment and Society, APPLEPIES 2023

作者： Boiko, Andrei Gaiduk, Maksym Madrid, Natividad Martínez Seepold, Ralf Ubiquitous Computing Lab Department of Computer Science HTWG Konstanz—University of Applied Sciences Konstanz78462 Germany School of Informatics Reutlingen University Reutlingen72762 Germany

ISBN: (纸本)9783031481208

The monitoring of a patient's heart rate (HR) is critical in the diagnosis of diseases. In the detection of sleep disorders, it also plays an important role. Several techniques have been proposed, including using sensors to record physiological signals that are automatically examined and analysed. This work aims to evaluate using a contactless HR monitoring system based on an accelerometer sensor during sleep. For this purpose, the oscillations caused by chest movements during heart contractions are recorded by an installation mounted under the bed mattress. The processing algorithm presented in this paper filters the signals and determines the HR. As a result, an average error of about 5 bpm has been documented, i.e., the system can be considered to be used for the forecasted domain. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Accelerometers

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ZKP Protocols for Usowan,Herugolf,and Five Cells

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Tsinghua science and Technology 2024年第6期29卷 1651-1666页

作者： Daiki Miyahara Léo Robert Pascal Lafourcade Takaaki Mizuki Graduate School of Informatics and Engineering The University of Electro-CommunicationsTokyo 1828585Japan MIS Lab University of Picardy Jules VerneAmiens 80000France Laboratory of Computer Science Modelling and Systems OptimisationUniversity of Clermont AuvergneClermont-Ferrand 63178France Cyberscience Center Tohoku UniversitySendai 9808578Japan

A Zero-Knowledge Proof (ZKP) protocol allows a participant to prove the knowledge of some secret without revealing any information about it. While such protocols are typically executed by computers, there exists a line of research proposing physical instances of ZKP protocols. Up to now, many card-based ZKP protocols for pen-and-pencil puzzles, like Sudoku, have been designed. Those games, mostly edited by Nikoli, have simple rules, yet designing them in card-based ZKP protocols is non-trivial. In this work, we propose a card-based ZKP protocol for Usowan, a Nikoli game. In Usowan, for each room of a puzzle instance, there is exactly one piece of false information. The goal of the game is to detect this wrong data amongst the correct data and also to satisfy the other rules. Designing a card-based ZKP protocol to deal with the property of detecting a liar has never been done. In some sense, we propose a physical ZKP for hiding of a liar. This work extends a previous paper appearing in Ref. [1]. In this extension, we propose two other protocols, for Herugolf and Five Cells. The puzzles are specifically chosen because each of those three puzzles shares a common constraint, connectivity. However, showing the connected configuration cannot be done with generic approach and brings new construction to the existing connectivity ZKP protocol. Indeed, in Herugolf, the connectivity is handled with a given length of cell which is decremental (i.e., the length of each connected cell decreases by one at each step). For Five Cells, there is an additional step in the setup allowing to encode all the information needed to ensure a valid ZKP protocol.

关键词： Zero-Knowledge Proof(ZKP)protocol playing cards card-based cryptography physical assumptions Usowan Herugolf FiveCells

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