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A Stacked Ensemble Approach For Enhancing Anti Cancer Drug Synergy Prediction

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

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.

关键词： drug synergy cancer machine learning drug combination

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ClassLIE: Structure- and Illumination-Adaptive Classification for Low-Light Image Enhancement

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4765-4775页

作者： Wei, Zixiang Wang, Yiting Sun, Lichao Vasilakos, Athanasios V. Wang, Lin University of Warwick National Automotive Innovation Centre Iv Sensor Group Wmg CoventryCV4 7AL United Kingdom Lehigh University Department of Computer Science and Engineering BethlehemPA18015 United States Grimstad4879 Norway Lab of Artificial Intelligence Thrust Guangzhou511457 China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong SAR Hong Kong

Low-light images often suffer from limited visibility and multiple types of degradation, rendering low-light image enhancement (LIE) a nontrivial task. Some endeavors have been made to enhance low-light images using convolutional neural networks (CNNs). However, they have low efficiency in learning the structural information and diverse illumination levels at the local regions of an image. Consequently, the enhanced results are affected by unexpected artifacts, such as unbalanced exposure, blur, and color bias. This article proposes a novel framework, called ClassLIE, that combines the potential of CNNs and transformers. It classifies and adaptively learns the structural and illumination information from the low-light images in a holistic and regional manner, thus showing better enhancement performance. Our framework first employs a structure and illumination classification (SIC) module to learn the degradation information adaptively. In SIC, we decompose an input image into an illumination map and a reflectance map. A class prediction block is then designed to classify the degradation information by calculating the structure similarity scores on the reflectance map and mean square error (MSE) on the illumination map. As such, each input image can be divided into patches with three enhancement difficulty levels. Then, a feature learning and fusion (FLF) module is proposed to adaptively learn the feature information with CNNs for different enhancement difficulty levels while learning the long-range dependencies for the patches in a holistic manner. Experiments on five benchmark datasets consistently show our ClassLIE achieves new state-of-the-art performance, with 25.74 peak signal-to-noise ratio (PSNR) and 0.92 structural similarity (SSIM) on the LOw-Light (LOL) dataset. © 2020 IEEE.

关键词： Image enhancement

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Two-dimensional Parameter Relationships for W UMa-type Systems Revisited

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Research in Astronomy and Astrophysics 2024年第1期24卷 51-58页

作者： Atila Poro Ehsan Paki Ailar Alizadehsabegh Mehdi Khodadadilori Selda Ranjbar Salehian Mahya Hedayatjoo Fatemeh Hashemi Yasaman Dashti Fatemeh Mohammadizadeh Astronomy Department of the Raderon AI Lab. BurnabyBCCanada Faculty of Electrical and Computer Engineering Department of PhotonicsUniversity of TabrizTabriz 51664Iran Department of Electrical Engineering Amirkabir University of TechnologyTehranIran Astronomy Students'Scientific Association University of TabrizTabriz 51664Iran Department of Physics Iran University of Science and TechnologyTehranIran Institute for the Intellectual Development(Kanoon) Astronomy GroupBojnordIran Department of Mechanical Engineering Amirkabir University of TechnologyTehranIran Aerospace Engineering Department Science and Research Branch of Islamic Azad UniversityTehranIran

Reviewing the empirical and theoretical parameter relationships between various parameters is a good way to understand more about contact binary *** this investigation,two-dimensional(2D)relationships for P–MV(system),P–L1,2,M1,2–L1,2,and q–Lratiowere *** sample used is related to 118 contact binary systems with an orbital period shorter than 0.6 days whose absolute parameters were estimated based on the Gaia Data Release 3 *** reviewed previous studies on 2D relationships and updated six parameter ***,Markov chain Monte Carlo and Machine Learning methods were used,and the outcomes were *** selected 22 contact binary systems from eight previous studies for comparison,which had light curve solutions using spectroscopic *** results show that the systems are in good agreement with the results of this study.

关键词： (stars:)binaries(including multiple):close stars:fundamental parameters methods:data analysis

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A Neuro-Fuzzy Approach to Road Traffic Congestion Prediction

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computers, Materials & Continua 2022年第10期73卷 295-310页

作者： Mohammed Gollapalli Atta-ur-Rahman Dhiaa Musleh Nehad Ibrahim Muhammad Adnan Khan Sagheer Abbas Ayesha Atta Muhammad Aftab Khan Mehwash Farooqui Tahir Iqbal Mohammed Salih Ahmed Mohammed Imran BAhmed Dakheel Almoqbil Majd Nabeel Abdullah Omer Department of Computer Information System(CIS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Pattern Recognition and Machine Learning Lab Department of Software EngineeringGachon UniversityInchonKorea Department of Computer Science National College of Business Administration and EconomicsLahorePunjab54000Pakistan Department of Computer Science Government College University(GCU)LahorePunjab54000Pakistan Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia College of Business Administration Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Networks and Communications(NC) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The fast-paced growth of artificial intelligence applications provides unparalleled opportunities to improve the efficiency of various *** as the transportation sector faces many obstacles following the implementation and integration of different vehicular and environmental aspects *** congestion is among the major issues in this regard which demands serious attention due to the rapid growth in the number of vehicles on the *** address this overwhelming problem,in this article,a cloudbased intelligent road traffic congestion prediction model is proposed that is empowered with a hybrid Neuro-Fuzzy *** aim of the study is to reduce the delay in the queues,the vehicles experience at different road junctions across the *** proposed model also intended to help the automated traffic control systems by minimizing the congestion particularly in a smart city environment where observational data is obtained from various implanted Internet of Things(IoT)sensors across the *** due preprocessing over the cloud server,the proposed approach makes use of this data by incorporating the neuro-fuzzy ***,it possesses a high level of accuracy by means of intelligent decision making with minimum error *** results reveal the accuracy of the proposed model as 98.72%during the validation phase in contrast to the highest accuracies achieved by state-of-the-art techniques in the literature such as 90.6%,95.84%,97.56%and 98.03%,*** far as the training phase analysis is concerned,the proposed scheme exhibits 99.214% accuracy. The proposed prediction modelis a potential contribution towards smart cities environment.

关键词： Neuro-fuzzy machine learning congestion prediction AI cloud computing smart cities

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Accountability of Immersive Technologies in Healthcare: A Review 5

Accountability of Immersive Technologies in Healthcare: A Re...

引用

5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Sukhija, Ashna Goyal, Hitesh Sharma, Bhanu Angra, Sheena Chitkara University Institute of Engineering & Technology Chitkara University Department of Computer Science and Engineering Punjab India Chitkara University Institute of Engineering & Technology Chitkara University Immersive and Interactive Technology Lab Punjab India

ISBN: (纸本)9798350383522

Immersive Technologies have been conventionally used in almost every research area such as medical field, educational domain, tourism, history and heritage and many more. But it has immensely contributed in the medical field for the betterment of society by aiding people who are facing serious medical issues. These medical issues could be related to either physical health or mental well-being. The findings provide light on the most recent developments in the field of immersive technology applications for medical training and practice. They also serve as a crucial resource for academicians working in the fields of medical practice. Hence, this paper discusses the work done by several authors in the medical domain by throwing some light on the parameters used, techniques and tools used to provide relaxation to physically or mentally ill patients. © 2024 IEEE.

关键词： Augmented Reality Healthcare immersive opportunity technology Virtual Reality

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A novel approach to deriving adaboost classifier weights using squared loss function for overlapping speech detection

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Multimedia Tools and Applications 2025年 1-28页

作者： Asbai, Nassim Bounazou, Hadjer Zitouni, Sihem Department of Electronics Faculty of Sciences and Technology Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj City34030 Algeria Speech Com. & Signal Proc. Lab. Faculty of Electrical Engineering USTHB Bab Ezzouar City16111 Algeria Department of Computer Science Faculty of Mathematics and Computer Science Mohamed El Bachir El Ibrahimi University Bordj Bou Arreridj City34030 Algeria

In this paper, we address the complex problem of detecting overlapping speech segments, a key challenge in speech processing with applications in speaker diarization, automatic transcription, and multi-speaker recognition systems. Traditional approaches, often relying on exponential loss functions within the AdaBoost framework, struggle to maintain robustness in noisy or imbalanced data environments. To enhance detection accuracy, we propose a novel AdaBoost variant—SLFAdaBoost—utilizing a squared loss function specifically tailored for overlapping speech detection. This model demonstrates superior stability and convergence, significantly outperforming conventional methods. Experimental evaluation on the NIST 2005 corpus revealed that SLFAdaBoost achieves an accuracy range of 92.5% to 98.6%, with F1 scores between 95.4% and 99.2%, notably surpassing standard AdaBoost and Random Forest classifiers. Additionally, our model shows resilience in noisy conditions, maintaining precision and recall metrics at higher noise levels than other classifiers. These results underscore SLFAdaBoost's capacity to handle intricate data patterns, offering a more robust solution for real-world speech processing applications where overlapping segments are prevalent. This contribution provides an efficient, high-performing model, advancing the capabilities of ensemble methods in complex audio environments. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Speech recognition

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AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis of the Femoral Head Detection 37

AsT: An Asymmetric-Sensitive Transformer for Osteonecrosis o...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Chen, Haoyang Liu, Shuai Lu, Feng Li, Wei Sheng, Bin Li, Mi Jin, Hai Zomaya, Albert Y. National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China Centre for Distributed and High Performance Computing School of Computer Science The University of Sydney Australia Department of Computer Science and Engineering Shanghai Jiao Tong University China Tongji Hospital Tongji Medical College Huazhong University of Science and Technology China

ISBN: (纸本)9781577358800

Early diagnosis of osteonecrosis of the femoral head (ONFH) can inhibit the progression and improve femoral head preservation. The radiograph difference between early ONFH and healthy ones is not apparent to the naked eye. It is also hard to produce a large dataset to train the classification model. In this paper, we propose Asymmetric-Sensitive Transformer (AsT) to capture the uneven development of the bilateral femoral head to enable robust ONFH detection. Our ONFH detection is realized using the self-attention mechanism to femoral head regions while conferring sensitivity to the uneven development by the attention-shared transformer. The real-world experiment studies show that AsT achieves the best performance of AUC 0.9313 in the early diagnosis of ONFH and can find out misdiagnosis cases firmly. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Large dataset

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A novel chaotic logistic-sine map-based quantum cryptosystem for secure video communication

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Multimedia Tools and Applications 2025年 1-33页

作者： El-Shafai, Walid Almomani, Iman Computer Science Department College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Automated Systems and Computing Lab (ASCL) Prince Sultan University Riyadh Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Computer Science Department King Abdullah II School of Information Technology The University of Jordan Amman 11942 Jordan

Video encryption is crucial for ensuring the confidentiality of sensitive video data, especially in finance, healthcare, and government industries. With the increasing use of video conferencing and online video streaming, there is a growing need to protect video data from unauthorized access and hacking attempts. Encryption prevents sensitive information from being exposed during transmission and unauthorized access to stored video data. This paper introduces and details a novel quantum encryption algorithm designed for secure video communication, combining chaotic logistic-sine maps and quantum techniques. The proposed cryptosystem recruits the security benefits of chaos theory and quantum encryption’s efficiency to ensure high security for video communications. Specifically, it employs a logistic-sine map to generate secret chaotic keys that encrypt video data, while quantum properties ensure the secure transmission of the encrypted video. The cryptosystem has been implemented and rigorously evaluated using various performance metrics to determine its security and efficiency. The results demonstrate that the introduced cryptosystem provides strong encryption and robust defense against various multimedia attacks, outperforming existing and traditional encryption methods in terms of encryption/decryption speed and all evaluated security parameters. Consequently, this cryptosystem presents a promising and efficient security solution for multimedia streaming across various applications, including military communications, online entertainment, and healthcare. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Channel attacks Chaotic logistic-sine maps Countermeasures Multimedia security Quantum cryptosystems Security analysis Threats Video communication

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Intelligent wearable vision systems for the visually impaired in Saudi Arabia

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Neural Computing and Applications 2025年第14期37卷 8571-8591页

作者： Talaat, Fatma M. El-Shafai, Walid Soliman, Naglaa F. Algarni, Abeer D. El-Samie, Fathi E. Abd Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh33516 Egypt Faculty of Computer Science and Engineering New Mansoura University Gamasa35712 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Navigating the world with visual impairments presents unique challenges, often limiting independence and safety. This research introduces SafeStride, a novel algorithm designed to empower visually impaired individuals through real-time obstacle detection and navigation assistance. SafeStride functions through five interconnected phases: sensor data acquisition, data preprocessing, obstacle detection, path planning, and feedback and guidance. A combination of ultrasonic sensors, cameras, and inertial measurement units (IMUs) feed comprehensive environmental data into the system, enabling accurate obstacle detection and safe path planning. Evaluating SafeStride's performance involved the MNIST dataset for image recognition and a dedicated indoor object detection dataset. Additionally, comparisons were made against three deep learning models—convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). The results showcased SafeStride's high accuracy and effectiveness in both obstacle detection and classification. Beyond its impressive performance, SafeStride represents a significant leap forward in navigation aids for the visually impaired. By offering a comprehensive solution that enhances safety and independence in diverse environments, it holds immense potential to improve the lives of countless individuals. Future efforts will focus on further optimizing the algorithm and testing its real-world capabilities, paving the way for a future where safe and independent navigation becomes a reality for all. All three deep learning models (CNN, LSTM, GRU) excelled in the experiment, achieving high accuracy above 0.98, strong recall and F1-scores near 0.99, and exceptional AUC-ROC scores exceeding 0.9998. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Motion planning

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Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

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computers, Materials & Continua 2024年第10期81卷 271-307页

作者： Nahed Tawfik Heba M.Emara Walid El-Shafai Naglaa F.Soliman Abeer D.Algarni Fathi EAbd El-Samie Computers and Systems Department Electronics Research InstituteEl NozhaHuckstep Cairo12622Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Security Engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia

Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins,including hereditary factors and various clinical *** stands as the deadliest type of cancer and a significant cause of cancer-related deaths *** diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately,leading to improved prognosis and higher survival *** significant increase in both the incidence and mortality rates of lung cancer,particularly its ranking as the second most prevalent cancer among women worldwide,underscores the need for comprehensive research into efficient screening *** in diagnostic techniques,particularly the use of computed tomography(CT)scans,have revolutionized the identification of lung *** scans are renowned for their ability to provide high-resolution images and are particularly effective in detecting small,calcified areas,crucial for identifying earlystage lung ***,there is growing interest in enhancing computer-aided detection(CAD)*** algorithms assist radiologists by reducing false-positive interpretations and improving the accuracy of early cancer *** study aims to enhance the effectiveness of CAD systems through various ***,the Contrast Limited Adaptive Histogram Equalization(CLAHE)algorithm is employed to preprocess CT scan images,thereby improving their visual *** refinement is achieved by integrating different optimization strategies with the CLAHE *** CutMix data augmentation technique is applied to boost the performance of the proposed model.A comparative analysis is conducted using deep learning architectures such as InceptionV3,ResNet101,Xception,and *** study evaluates the performance of these architectures in image classification tasks,both with and without the implementation of the CLAHE *** empirical findings of the study demonst

关键词： Lung cancer detection CLAHE algorithm optimization deep learning classification feature extraction healthcare applications

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