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Colon and lung cancer classification from multi-modal images using resilient and efficient neural network architectures

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Heliyon 2024年第9期10卷 e30625页

作者： Uddin, A. Hasib Chen, Yen-Lin Akter, Miss Rokeya Ku, Chin Soon Yang, Jing Por, Lip Yee Department of Computer Science and Engineering Khwaja Yunus Ali University Enayetpur Chouhali Sirajganj 6751 Bangladesh Department of Computer Science and Information Engineering National Taipei University of Technology Taipei 106344 Taiwan Department of Computer Science Universiti Tunku Abdul Rahman Kampar 31900 Malaysia Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur 50603 Malaysia

Automatic classification of colon and lung cancer images is crucial for early detection and accurate diagnostics. However, there is room for improvement to enhance accuracy, ensuring better diagnostic precision. This study introduces two novel dense architectures (D1 and D2) and emphasizes their effectiveness in classifying colon and lung cancer from diverse images. It also highlights their resilience, efficiency, and superior performance across multiple datasets. These architectures were tested on various types of datasets, including NCT-CRC-HE-100K (set of 100,000 non-overlapping image patches from hematoxylin and eosin (H&E) stained histological images of human colorectal cancer (CRC) and normal tissue), CRC-VAL-HE-7K (set of 7180 image patches from N = 50 patients with colorectal adenocarcinoma, no overlap with patients in NCT-CRC-HE-100K), LC25000 (Lung and Colon Cancer Histopathological Image), and IQ-OTHNCCD (Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases), showcasing their effectiveness in classifying colon and lung cancers from histopathological and Computed Tomography (CT) scan images. This underscores the multi-modal image classification capability of the proposed models. Moreover, the study addresses imbalanced datasets, particularly in CRC-VAL-HE-7K and IQ-OTHNCCD, with a specific focus on model resilience and robustness. To assess overall performance, the study conducted experiments in different scenarios. The D1 model achieved an impressive 99.80 % accuracy on the NCT-CRC-HE-100K dataset, with a Jaccard Index (J) of 0.8371, a Matthew's Correlation Coefficient (MCC) of 0.9073, a Cohen's Kappa (Kp) of 0.9057, and a Critical Success Index (CSI) of 0.8213. When subjected to 10-fold cross-validation on LC25000, the D1 model averaged (avg) 99.96 % accuracy (avg J, MCC, Kp, and CSI of 0.9993, 0.9987, 0.9853, and 0.9990), surpassing recent reported performances. Furthermore, the ensemble of D1 and D2 reached 93 % accuracy (J, MCC, Kp, and

关键词： Cancer image classification Colon cancer CT-Scan imaging Dense neural networks (DNN) Histopathological imaging Lung cancer Multi-modal network

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Design and Implementation of an Instrumented walking cane for Detection of Freezing of Gait 3

Design and Implementation of an Instrumented walking cane fo...

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3rd IEEE International Multidisciplinary Conference on engineering Technology, IMCET 2021

作者： Haidar Ahmad, Joelle El-Asmar, Alain Abi Zeid Daou, Roy Hayek, Ali Boercsoek, Josef Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon MART Learning Education and Research Center Chananir Lebanon University of Kassel Faculty of Electrical Engineering/Computer Science Department of Computer Architecture and System Programming Germany

ISBN: (纸本)9781665405874

Independent mobility is an important feature of an individual's normal life and with the elderly population continually growing, assistive devices such as walkers and canes provide help and guidance to users. Freezing of Gait (FoG) is an episodic phenomenon that is a common symptom of an age-related disease known as Parkinson Disease (PD). This paper is headed toward implementing a detection, diagnosis and correction system for FoG episodes. The proposed cane will generate visual, auditory and tactile cues to help patients overcome this problem, therefore reducing fall risks. In addition, a vibrating calf cuff will be attached to the calf for muscle stimulation. A compatible android phone application is designed to display heart rate measurements of the patient as well as his exact location in case of fall detection. The system is considered to be an electrically safe, portable, comfortable and a low-cost device. The performance of this system is analyzed thoroughly and the accuracy of all methodologies is considered carefully and evaluated. The results have shown no faulty errors and good accuracy and detection percentages. © 2021 IEEE.

关键词： Diagnosis

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XoT (Xducation of Things): Harnessing AI and edge computing to educate all things

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IEEE Access 2024年

作者： Nurtantyana, Rio Hwang, Wu-Yuin Hariyanti, Uun Research Center for Data and Information Sciences Bandung Indonesia Telkom University School of Computing Indonesia Telkom University Center of Excellence Artificial Intelligence for Learning and Optimization Bandung Indonesia National Dong Hwa University College of Science and Engineering Department of Computer Science and Information Engineering Taiwan National Central University Graduate Institute of Network Learning Technology Taiwan Universitas Brawijaya Faculty of Computer Science Department of Information System Indonesia

Most English foreign language (EFL) studies focus solely on human beings. This research explores how edge computing can facilitate learning for all things. The XoT (Xducation of Things) framework was proposed to educate both human and all things. All things encompass two terms: AI-Agent and smartthings (covering physical and digital smart objects). At the core of this framework is Smart Question Answer Forwarding Mechanism (SQA-Forwarding), specifically designed to assist all things in building knowledge. To demonstrate this, the smartXoT environment was developed based on XoT framework, and its impacts on EFL learners was assessed. A quasi-experimental study involving 26 EFL learners, divided into an experimental group (EG) and a control group (CG), examined the differences in learning achievement of smartthings and EFL learners when using the smartXoT environment with/without SQA-Forwarding. Findings, on one hand, indicated that smartthings in the EG developed knowledge bases greater than those in the CG. On the other hand, the interaction between EFL learners and smartthings with SQA-Forwarding significantly improved learners' writing skills, with revisions playing a crucial role in enhancing writing quality. Thus, the XoT framework offers a novel and promising approach to educating both humans and all things. © 2013 IEEE.

关键词： Contrastive Learning

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Comparative analysis of network forensic tools and network forensics processes 2

Comparative analysis of network forensic tools and network f...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Ghabban, Fahad M Alfadli, Ibrahim M Ameerbakhsh, Omair Abuali, Amer Nizar Al-Dhaqm, Arafat Al-Khasawneh, Mahmoud Ahmad Taibah University College of Computer Science and Engineering Information System Department Madinah Saudi Arabia University Technology Malaysia Faculty of Engineering School of Computing Johor Malaysia Al-Madinah International University Faculty of Computer Information Technology Shah Alam Malaysia

ISBN: (纸本)9781665432221

Network Forensics (NFs) is a branch of digital forensics which used to detect and capture potential digital crimes over computer networked environments crime. Network Forensic Tools (NFTs) and Network Forensic Processes (NFPs) have abilities to examine networks, collect all normal and abnormal traffic/data, help in network incident analysis, and assist in creating an appropriate incident detection and reaction and also create a forensic hypothesis that can be used in a court of law. Also, it assists in examining the internal incidents and exploitation of assets, attack goals, executes threat evaluation, also by evaluating network performance. According to existing literature, there exist quite a number of NFTs and NTPs that are used for identification, collection, reconstruction, and analysing the chain of incidents that happen on networks. However, they were vary and differ in their roles and functionalities. The main objective of this paper, therefore, is to assess and see the distinction that exist between Network Forensic Tools (NFTs) and Network Forensic Processes (NFPs). Precisely, this paper focuses on comparing among four famous NFTs: Xplico, OmniPeek, NetDetector, and NetIetercept. The outputs of this paper show that the Xplico tool has abilities to identify, collect, reconstruct, and analyse the chain of incidents that happen on networks than other NF tools. © 2021 IEEE.

关键词： computer crime

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Digital forensics domain and metamodeling development approaches 2

Digital forensics domain and metamodeling development approa...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Ameerbakhsh, Omair Ghabban, Fahad M Alfadli, Ibrahim M Abuali, Amer Nizar Al-Dhaqm, Arafat Al-Khasawneh, Mahmoud Ahmad Taibah University College of Computer Science and Engineering Information System Department Madinah Saudi Arabia University Technology Malaysia School of Computing Faculty of Engineering Johor Malaysia Al-Madinah International University Faculty of Computer and Information Technology Shah Alam Malaysia

ISBN: (纸本)9781665432221

Metamodeling is used as a general technique for integrating and defining models from different domains. This technique can be used in diverse application domains, especially for purposes of standardization. Also, this process mainly has a focus on the identification of general concepts that exist in various problem domain and their relations and to solve complexity, interoperability, and heterogeneity aspects of different domains. Several diverse metamodeling development approaches have been proposed in the literature to develop metamodels. Each metamodeling development process has some advantages and disadvantages too. Therefore, the objective of this paper is to provide a comprehensive review of existing metamodeling development approaches and conduct a comparative study among them-eventually selecting the best approach for metamodel development in the perspective of digital forensics. © 2021 IEEE.

关键词： Digital forensics

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CIPM: Common identification process model for database forensics field 2

CIPM: Common identification process model for database foren...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Alfadli, Ibrahim M Ghabban, Fahad M Ameerbakhsh, Omair Abuali, Amer Nizar Al-Dhaqm, Arafat Al-Khasawneh, Mahmoud Ahmad Taibah University College of Computer Science and Engineering Information System Department Madinah Saudi Arabia University Technology Malaysia Faculty of Engineering School of Computing Johor Malaysia Al-Madinah International University Faculty of Computer and Information Technology Shah Alam Malaysia

ISBN: (纸本)9781665432221

Database Forensics (DBF) domain is a branch of digital forensics, concerned with the identification, collection, reconstruction, analysis, and documentation of database crimes. Different researchers have introduced several identification models to handle database crimes. Majority of proposed models are not specific and are redundant, which makes these models a problem because of the multidimensional nature and high diversity of database systems. Accordingly, using the metamodeling approach, the current study is aimed at proposing a unified identification model applicable to the database forensic field. The model integrates and harmonizes all exiting identification processes into a single abstract model, called Common Identification Process Model (CIPM). The model comprises six phases: 1) notifying an incident, 2) responding to the incident, 3) identification of the incident source, 4) verification of the incident, 5) isolation of the database server and 6) provision of an investigation environment. CIMP was found capable of helping the practitioners and newcomers to the forensics domain to control database crimes. © 2021 IEEE.

关键词： Digital forensics

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Predictive Multimodal Deep Learning-Based Sustainable Renewable and Non-Renewable Energy Utilization

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computer systems science & engineering 2023年第10期47卷 1267-1281页

作者： Abdelwahed Motwakel MarwaObayya Nadhem Nemri Khaled Tarmissi Heba Mohsen Mohammed Rizwanulla Ishfaq Yaseen Abu Sarwar Zamani Department of Information Systems College of Business Administration in Hawtat bani TamimPrince Sattam bin Abdulaziz UniversityHowtat Bani Tamim16622Saudi Arabia Department of Biomedical Engineering College of EngineeringPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Recently,renewable energy(RE)has become popular due to its benefits,such as being inexpensive,low-carbon,ecologically friendly,steady,and *** RE sources are gradually combined with non-renewable energy(NRE)sources into electric grids to satisfy energy *** energy utilization is highly related to national energy policy,energy prediction using artificial intelligence(AI)and deep learning(DL)based models can be employed for energy prediction on RE and NRE power *** energy consumption of RE and NRE sources using effective models becomes *** this motivation,this study presents a new multimodal fusionbased predictive tool for energy consumption prediction(MDLFM-ECP)of RE and NRE power *** data may influence the prediction performance of the results in prediction *** proposed MDLFMECP technique involves pre-processing,fusion-based prediction,and hyperparameter *** addition,the MDLFM-ECP technique involves the fusion of four deep learning(DL)models,namely long short-termmemory(LSTM),bidirectional LSTM(Bi-LSTM),deep belief network(DBN),and gated recurrent unit(GRU).Moreover,the chaotic cat swarm optimization(CCSO)algorithm is applied to tune the hyperparameters of the DL *** design of the CCSO algorithm for optimal hyperparameter tuning of the DL models,showing the novelty of the work.A series of simulations took place to validate the superior performance of the proposed method,and the simulation outcome emphasized the improved results of the MDLFM-ECP technique over the recent approaches with minimum overall mean absolute percentage error of 3.58%.

关键词： Sustainability renewable energy power source energy prediction deep learning fusion model metaheuristics

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A Machine Learning Model for Foreign Exchange Prediction 2nd

A Machine Learning Model for Foreign Exchange Prediction

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： San, Tan Jun Ajibade, Samuel-Soma M. Ajibade, Temiloluwa Iyanuoluwa Jasser, Muhammed Basheer Ayodele, Olamide Emmanuel Oyewopo, Abdulgafar Olorunleke Adediran, Anthonia Oluwatosin Majeed, Anwar P. P. Abdul Luo, Yang Department of Data Science and Artificial Intelligence Faculty of Engineering and Technology Sunway University Selangor Petaling Jaya Malaysia Department of Computer Engineering Istanbul Ticaret University Istanbul Turkey Department of Business Management Scholar School System Birmingham United Kingdom School of Engineering and Technology Sunway University Selangor Darul Ehsan Petaling Jaya47500 Malaysia Department of Banking and Finance Ekiti State University Ekiti State Ado Ekiti Nigeria Department of Business Administration Kwara State University Malete Nigeria Department of Real Estate Faculty of Built Environment Universiti Malaya Kuala Lumpur Malaysia Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

The rise of deep learning algorithms has sparked interest in the financial domain. The foreign exchange market (FOREX) is the globally leading financial market with millions of active stakeholders. Research in deep learning for forecasting price movements has shown its efficacy in financial markets. Notably, LSTM have empirically proven the best performance among other machine learning techniques. However, studies have shown the lack of deep learning hybrid comparisons to fortify empirical claims, especially in the FX market characterized for its high volatility and variance among currency pairs. This study develops a generalized LSTM model to forecast the major pairs and conducts a comparative analysis with a secondary hybrid deep learning model to assess and solidify the robustness of deep learning methods in forex forecasting. The empirical scenario simulates a classification problem employing cross-validation techniques to optimize the models’ generalization rigorously. Evaluation of the model utilized the accuracy, precision, recall, and f1-score metrics. Results indicated the performance of the models from best to worst were LSTM, SRNN and followed by CNN indicating the importance of testing deep learning approaches in different empirical setups. Conclusively, this study highlights the need challenges in forex forecasting and raises the need for more robust assessments on machine learning models forecasting capabilities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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"DOMINO - Stop Domestic Violence". An Educational Mobile Application

"DOMINO - Stop Domestic Violence". An Educational Mobile App...

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21st International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2023

作者： Sakellari, Evanthia Murto, Tiina Häsänen, Marjatta Turunen, Timo Modhwadia, Hitesh Sotto-Mayor De Carvalho Pinto, Maria Luisa Ferreira Dos Santos, Maria Anabela Lagiou, Areti Department of Public and Community Health Laboratory of Hygiene and Epidemiology University of West Attica Athens Greece Faculty of Health and wellbeing Turku University of Applied Sciences Turku Finland Master Degree of Social Science in Business Financial Management Riga Technical University Latvia Master Degree of Engineering in Computer System Riga Technical University Latvia Portugal

ISBN: (纸本)9781643684000

Domestic violence affects people of all socioeconomic backgrounds and education levels and can happen to anyone. It is a public health issue that needs to be addressed with health and social care professionals playing an essential role in prevention and early intervention. These professionals need to be prepared through proper education. A European funded project developed 'DOMINO - Stop domestic violence' educational mobile application which was piloted among 99 social and/or health care students and professionals. Most of the participants (n= 59, 59.6%) indicated that the DOMINO mobile application was easy to install and over half of them (n=61, 61.6%) would recommend the app. They found it easy to use, and quick access to useful materials and tools. Participants found case studies and the checklist good and useful tools for them. The DOMINO educational mobile application is available open access, in English, Finnish, Greek, Latvian, Portuguese and Swedish, for any stakeholder worldwide who is interested to learn more about domestic violence prevention and intervention. © 2023 The authors and IOS Press.

关键词： Mobile computing

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Digital forensics for the socio-cyber world (DF-SCW): A novel framework for deepfake multimedia investigation on social media platforms

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Egyptian Informatics Journal 2024年 27卷

作者： Ayub Khan, Abdullah Chen, Yen-Lin Hajjej, Fahima Ahmed Shaikh, Aftab Yang, Jing Soon Ku, Chin Yee Por, Lip Department of Computer Science and Information Technology Benazir Bhutto Shaheed University Lyari Sindh Karachi75660 Pakistan Department of Computer Science Sindh Madressatul Islam University Sindh Karachi74000 Pakistan Department of Computer Science and Information Engineering National Taipei University of Technology Taipei106344 Taiwan Department of Software Engineering College of Computer and Inf. Sci. Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Department of Computer Science Universiti Tunku Abdul Rahman Kampar31900 Malaysia

Owing to the major development of social media platforms, the usage of technological adaptation increases by means of editing software tools. Posting media in social communication environments has become one of our common daily routines. Before posting, various editing generators are used to manipulate pixel values, such as for enhancing brightness and contrast. Undoubtedly, this software helps bring posting media from ordinary to outstanding. But such a type of editing crosses the line in terms of creating fakes—anything that comes from anywhere and does not retain its originality anyway. It poses a series of issues in the process of multimedia forensics investigation and chain of custody. In order to restrict the attempts at deep faking and make the investigation hierarchy more effective, efficient, and reliable in the socio-cyber space (SCS), this paper presents a novel framework called DF-SCW. A digital forensics-enabled socio-cyber world with artificial intelligence (AI), especially deep neural networks (DNNs), for detecting and analyzing deep fake media investigations on social media platforms. It compares pixels with their neighboring values in the same media (such as images and videos) to identify information about the original one. There is a media flag designed to filter out malicious and dangerous attempts, like a powerful leader declaring war. Putting flags on such fakes helps digital investigators resist sharing the posts. In addition, the other prospect of this research is to make the digital forensics ecosystem more appropriate to take qualitative judgments in real-time while media is uploaded on social media platforms. The simulation of the proposed DF-SCW is tested on three different platforms, such as Instagram, Facebook, and Twitter. Through the experiment, the DF-SCW outperformed in terms of detection, identification, and analysis of deepfake media by an increased rate of 3.77%. © 2024

关键词： Social networking (online)

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