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Research Square 2025年

作者： Hamad, Dana Rasul Rashid, Tarik A. Computer Science Department Faculty of Science Soran University Erbil SoranKRG Iraq Computer Science and Engineering Department AIIC University of Kurdistan Hewler KRG Erbil Iraq

Cancer diseases are remaining a leading cause of death in all over the world, this is encouraging the development of advanced diagnostic systems for reliable and accurate detection. The current study investigates the models of traditional machine learning and nature-inspired algorithms for classifying liver, breast and lung cancer cases utilizing structured datasets. More focuses of this research will be on the performance evaluation of five nature-inspired optimizer algorithms like Learner Performance-Based Behavior with Simulated Annealing (LPBSA), Learner Performance-Based Behavior (LPB), FOX Algorithm (FOX), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT). Among these models LPBSA performing the best, consistently obtained the highest accuracy of 92%, sensitivity 94% and specificity 90%, with confusion matrices reflecting minimal false negative and positives. This work contributes to growing adoption of popular algorithms for enhancing healthcare diagnosis and LPBSA can be utilized in other field investigations. © 2025, CC BY.

关键词： Lung cancer

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IR780/Gemcitabine-conjugated metal-phenolic network enhanced photodynamic cancer therapy

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Chinese Chemical Letters 2024年第1期35卷 305-309页

作者： Songtao Zhou Hao Tian Jie Yan Zhan Zhang Guohao Wang Xinying Yu Wei Sang Bei Li Greta S.P.Mok Jie Song Yunlu Dai Cancer Center and Institute of Translational Medicine Faculty of Health SciencesUniversity of MacaoMacao 999078China MoE Frontiers Science Center for Precision Oncology University of MacaoMacao 999078China Institute of Basic Medicine and Cancer(IBMC) Chinese Academy of SciencesThe Cancer Hospital of the University of Chinese Academy of SciencesHangzhou 310022China Biomedical Imaging Laboratory(BIG) Department of Electrical and Computer EngineeringFaculty of Science and TechnologyUniversity of MacaoMacao 999078China

Photodynamic therapy (PDT) is a clinically approved cancer treatment that uses energy of light to generate active substances that cause damage to the cancer. Photosensitizers are employed to absorb light and generate toxic reactive oxygen species (ROS) to damage biomolecules like DNA. At the same time, some chemotherapy drugs like nucleotide analogues can provide mechanism-guided promotion in the treatment efficacy of PDT. However, the photosensitizer and chemotherapy drugs used in PDT is usually organic molecules, which suffers from bad solubility, fast clearance, and acute toxicity. To achieve targeted treatment, a reasonable delivery system is necessary. Therefore, we reported a metal-phenolic network where IR780 and gemcitabine were coupled chemically to overcome these shortcomings. The enhanced PDT effects can be realized by the promoted cell death both in vitro and in vivo. Moreover, the synergistic therapy also induced T-cell mediated anti-tumor immune response, which was significant for the inhibition of distant tumor growth. This work expanded the biomedical application of metal-phenolic materials and contribute to the wider application of photodynamic cancer therapy.

关键词： Metal phenolic network IR780 Photodynamic therapy Breast cancer Gemcitabine

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Decision Tree-Based IoT Botnet Attack Detection

Decision Tree-Based IoT Botnet Attack Detection

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Cyber Resilience (ICCR), International Conference on

作者： Hazem Al-Najjar Nadia Al-Rousan Department of Computer Science College of Information Technology University of Petra Amman Jordan Department of Computer Engineering Faculty of Electrical Engineering and Information Systems German Jordanian University

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

This paper investigated the predictive capabilities of three decision tree models for IoT botnet attack prediction using packet information while minimizing the number of predictors. The study employed three decision tree models (C5, CHAID, and Random Forest) and two additional models (Logistic Regression and Bayesian Network) for comparison purposes. The IoT botnet attack dataset comprised various devices, mainly two types of attacks (Gafgyt and Mirai) and 23 feature engineering variables. Simulation results from all models showed that Accuracy, Precision, Recall, and False Omission Rate (FOR) values are approximately one, with an F1 score of around 0.5. CHAID and C5 models outperform other models in predicting IoT botnet attacks, as they are developed using only four and two variables, respectively. These results demonstrated that decision tree models with fewer variables can perform better than models that utilize all predictors.

关键词： Training Logistic regression Botnet Predictive models Bayes methods Internet of Things Decision trees

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Food Horticultural Supply Chain Performance Efficiency using Hybrid Model: SCOR-system Dynamic Simulation

Food Horticultural Supply Chain Performance Efficiency using...

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2022 International Seminar on Application for Technology of Information and Communication, iSemantic 2022

作者： Yusianto, Rindra Casnan, Casnan Sudibyo, Usman Suprihatin, Marimin Hardjomidjojo, Hartrisari Dian Nuswantoro University Faculty of Engineering Department of Industrial Engineering Central Java Semarang53192 Indonesia STKIP Muhammadiyah Kuningan Department of Mathematics Education West Java Kuningan45552 Indonesia Dian Nuswantoro University Faculty of Computer Science Department of Information System Central Java Semarang53192 Indonesia Faculty of Agricultural Engineering and Technology Department of Agro-Industrial Technology West Java Bogor16002 Indonesia

ISBN: (数字)9781665488396

ISBN: (纸本)9781665488396

Today's highly competitive and dynamic business environment has forced horticultural food commodity farmers to implement better supply chain management (SCM) strategies. An essential aspect of SCM is how farmers and collectors can detect changes in supply chain (SC) behavior;these changes affect the efficiency of SC performance. This study aims to improve food horticultural SC performance efficiency. We propose a hybrid method using the supply chain operation reference (SCOR) and system dynamics simulation. To illustrate the methodology, we use a case study of the potato agroindustry in Wonosobo District, Central Java, Indonesia. The results show that the SC performance measurement consists of six perspectives, namely Plan, Source, Make, Deliver, Return, and Enable, with 23 KPIs. The SC value in January-March 2022 is 71.01%, meaning that the average measurement results are in a suitable category. The best scenario we propose is Scenario I, which is a reduction in indirect performance labor costs of IDR 600,000 in three months and an increase in efficiency of 11.08%. For further research, researchers can use this method to increase efficiency in the food processing industry made from food horticultural commodities. © 2022 IEEE.

关键词： Supply chain management

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The Impact of Stuttering Event Representation on Detection Performance

The Impact of Stuttering Event Representation on Detection P...

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Cyber Resilience (ICCR), International Conference on

作者： Abedal-Kareem Al-Banna Hui Fang Ahmad F Shubita Mohammad Arafah Amani Eqteeshat Saif B Abuaisheh Department of AI and Data Science University of Petra Department of Computer Science Loughborough University Department of Software Engineering University of Petra Department of Computer Science University of Petra Department of Information System Malaya University

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

Stuttering or stammering is a common neurogenic, psychogenic fluency disorder wherein people have uncontrolled blocks on the natural flow of speech. Automatic Stuttering Event Detection (SED) is a widely recognised challenge due to the heterogenic and overlapped nature of stuttering speech. Therefore, this comparative study investigates the impact of stuttering event representation on detection performance. The paper evaluates the performance of SED and shows the impact of employing ASR pre-trained feature extractions on each stuttering event. Moreover, the paper provides different groups of experiments that evaluate the impact of three acoustic features, the Zero Crossing Rate (ZCR), the Spectral Flux Onset Strength Envelope (SFO), and Fundamental Frequency (FF) features, on SED performance. The paper demonstrates that the fusion of spectral and ASR features improves SED performance across all stuttering events by 8%. The F1 score of the Block class increased by approximately 10%, from 15% to 25%, while the word repetition and interjection increased by 8%. In addition, our experiments proved that the fusion of temporal features increased the average F1 score of SED by 2% across all stuttering events. The F1 score of the prolongation and sound repetition classes increased by 6% and 3%, respectively.

关键词： Event detection Speech recognition Feature extraction Acoustics Robustness Time-domain analysis Mel frequency cepstral coefficient

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A Framework for Deepfake Detection using Convolutional Neural Network and Deep Features

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Procedia computer science 2025年 258卷 3640-3648页

作者： Soundarya B C Gururaj H L Naveen Kumar C M Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Business System Malnad College of Engineering Hassan India

With the advancement of Artificial Intelligence, facial recognition has become a crucial biometric feature. Deepfake technology leverages AI and can create hyper-realistic digitally manipulated videos of people appearing to say or do things that never occurred. The emergence of Generative Adversarial Networks (GANs) has further enabled the creation of fake visual content with astonishing realism. This technology has diverse applications, such as in the film industry, where it allows for video recreation without reshooting, creating awareness videos, restoring the voices of those who have lost them, and updating movie scenes at low cost. However, this rapid advancement also presents significant challenges. The proliferation of synthetic images raises severe concerns about their societal impact, particularly in terms of potential misuse for harassment and blackmail. Therefore, developing robust deepfake detection models is imperative. This study evaluates the performance of a proposed ResNet34 model in deepfake detection. We utilize the FaceForensics++ dataset to train and assess the model, incorporating images generated by four popular deepfake techniques. Our experimental results demonstrate that integrating linear ternary patterns (LTP) and edge detection-based features with the modified ResNet34 model achieves superior performance, attaining 97.5% accuracy and surpassing other approaches.

关键词： Deepfake Artificial intelligence Deep learning

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Jin, Dongyang Fan, Chao Chen, Weihua Yu, Shiqi Department of Computer Science and Engineering Southern University of Science and Technology China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Alibaba Group China

ISBN: (纸本)157735897X

The gait, as a kind of soft biometric characteristic, can reflect the distinct walking patterns of individuals at a distance, exhibiting a promising technique for unrestrained human identification. With largely excluding gait-unrelated cues hidden in RGB videos, the silhouette and skeleton, though visually compact, have acted as two of the most prevailing gait modalities for a long time. Recently, several attempts have been made to introduce more informative data forms like human parsing and optical flow images to capture gait characteristics, along with multi-branch architectures. However, due to the inconsistency within model designs and experiment settings, we argue that a comprehensive and fair comparative study among these popular gait modalities, involving the representational capacity and fusion strategy exploration, is still lacking. From the perspectives of fine vs. coarse-grained shape and whole vs. pixel-wise motion modeling, this work presents an in-depth investigation of three popular gait representations, i.e., silhouette, human parsing, and optical flow, with various fusion evaluations, and experimentally exposes their similarities and differences. Based on the obtained insights, we further develop a C2Fusion strategy, consequently building our new framework MultiGait++. C2Fusion preserves commonalities while highlighting differences to enrich the learning of gait features. To verify our findings and conclusions, extensive experiments on Gait3D, GREW, CCPG, and SUSTech1K are conducted. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Intrusion Detection systems in Internet of Things and Mobile Ad-Hoc Networks

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computer systems science & engineering 2022年第3期40卷 1199-1215页

作者： Vasaki Ponnusamy Mamoona Humayun NZJhanjhi Aun Yichiet Maram Fahhad Almufareh Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKamparMalaysia Department of Information Systems College of Computer and Information SciencesJouf UniversityAl-JoufSaudi Arabia School of Computer Science and Engineering(SCE) Taylor’s UniversitySelangorMalaysia

Internet of Things(IoT)devices work mainly in wireless mediums;requiring different Intrusion Detection system(IDS)kind of solutions to leverage 802.11 header information for intrusion ***-specific traffic features with high information gain are primarily found in data link layers rather than application layers in wired *** survey investigates some of the complexities and challenges in deploying wireless IDS in terms of data collection methods,IDS techniques,IDS placement strategies,and traffic data analysis *** paper’s main finding highlights the lack of available network traces for training modern machine-learning models against IoT specific ***,the Knowledge Discovery in Databases(KDD)Cup dataset is reviewed to highlight the design challenges of wireless intrusion detection based on current data attributes and proposed several guidelines to future-proof following traffic capture methods in the wireless network(WN).The paper starts with a review of various intrusion detection techniques,data collection methods and placement *** main goal of this paper is to study the design challenges of deploying intrusion detection system in a wireless *** detection system deployment in a wireless environment is not as straightforward as in the wired network environment due to the architectural *** this paper reviews the traditional wired intrusion detection deployment methods and discusses how these techniques could be adopted into the wireless environment and also highlights the design challenges in the wireless *** main wireless environments to look into would be Wireless Sensor Networks(WSN),Mobile Ad Hoc Networks(MANET)and IoT as this are the future trends and a lot of attacks have been targeted into these *** it is very crucial to design an IDS specifically to target on the wireless networks.

关键词： Internet of Things MANET intrusion detection systems wireless networks

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Hybrid Machine Learning Approach of "KNN – LSTM" for detection of Brain Tumour in MR images 15

Hybrid Machine Learning Approach of "KNN – LSTM" for detect...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Uganya, G. Gunale, Anuja Govindrao Kulkarni, Shruthi V. Rajalakshmi, D. Valsaraj, Vani Srividhya, B. Department of ECE Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Computer Engineering UPL University of Sustainable Technology Block no 402 Ankleshwar-Valia Road Gujarat Vataria India Department of AIML HKBK College of Engineering opposite manyata tech park Nagawara Karnataka Bengaluru India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Application Faculty of Humanities and Sciences SRM Institute Of Science and Technology Tamil Nadu Chengalpattu India

ISBN: (纸本)9798350370249

Now a days, more existing techniques are available to found tumours on brain from MR images. But still the researchers are concentrated on to detect the brain tumour accurately with less time. So, this work proposed the hybrid machine learning algorithm of "K-Nearest Neighbour – Long Short-Term Memory" ("KNN-LSTM") with increased ‘accuracy’, ‘sensitivity’, ‘specificity’, with less ‘time consumption’. In this work, the brain tumour is identified by predicting the MR images from huge dataset with usage of machine learning approach of "KNN-LSTM". Brain tumour detection is difficult when to compare the abnormal and typical tissues of large capacity. In order to enable a speedy recovery following therapy, brain tumours should be identified as soon as feasible. If the brain tumour is discovered early on, patients can benefit from improved care. Due to the irregular spreading of the abrasions, brain tumours are still difficult to identify even with intensive study. Sometimes the tiny tumours look like normal, but it is challenging to find it. So, the techniques are need to increase the classification accuracy, and selecting the useful features. This paper proposes a hybrid machine learning model "KNN-LSTM" for categorizing the tumours with predicting through MR images. MRI brain imaging collection is used for our experimentation. The "KNN" algorithm usage is to extract the important characteristics from pictures once the data has been pre-processed effectively. The proposed work predicts the brain tumour with 99.15% of ‘accuracy’, 98% of ‘sensitivity’, 97.12% of ‘specificity’ and testing time consumption of 0.5 minutes. © 2024 IEEE.

关键词： Image segmentation

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Creating Resilient Digital Asset Management Frameworks in Financial Operations Using Blockchain Technology 2

Creating Resilient Digital Asset Management Frameworks in Fi...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Geetha, B.T. Kafila Ram, S Tulasi Amar Prabhakar Narkhede, Dr. Bani Ahmad, Ahmad Y. A. Tiwari, Mohit Saveetha School of Engineering Simats Department of Ece Tamil Nadu India Sr University School of Business Telangana Warangal India Chaitanya Bharathi Institute of Technology School of Management Studies Hyderabad India Isms Sankalp Business School Vadgon Pune India Middle East University Faculty of Business Department of Accounting and Finance Amman11831 Jordan Applied Science Private University Applied Science Research Center Jordan Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350384277

Using blockchain technology to build strong digital asset management tools for financial operations is a revolutionary way to bring financial systems up to date. Blockchain technology is changing the way money works by making transfers decentralised, open, and safe. Digital Asset Management (DAM) systems help businesses easily store and share digital assets like cryptocurrencies and blockchain-based assets. Using blockchain tools like smart contracts and tokenization makes financial deals safer and more trustworthy by using cryptographic encryption and ledger systems that can't be changed. Streamlining processes and payment makes financial operations more efficient. But problems like scaling and following the rules must be dealt with. Blockchain technology has many uses in finance, such as cross-border payments, tokenizing assets, and smart contracts. It is also changing how financial services are regulated. This study uses secondary data from academic research papers, peer-reviewed journals, and industry reports to look the use of blockchain technology to financial transactions, learn about DAM basics, look into blockchain tools in financial services, and judge how blockchain is used in financial transactions. This study helps us understand and use strong digital asset management systems in financial transactions that use blockchain technology by doing a thorough analysis. © 2024 IEEE.

关键词： Decentralized finance

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