IAES International Journal of Artificial Intelligence (IJ-AI)
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Recommendation method for selecting the rice seeds based on group decision support system
Get PDFIn this paper, we provide group recommendations based on each decision makers (DMs) in choosing the best type of rice for replanting. This group decision support system (GDSS) aims to guide stakeholders who have a role in selecting rice types. In this method, we propose using technique for order preference by similarity to ideal solution (TOPSIS) to rank each DM, Borda to rank in groups, and then test it using Spearman's rank correlation to measure the relationship between system results and the method applied. The results of this study show that DM1 ranks highest in selecting Gelagai rice seeds with a preference of 0.7786. Then DM2 ranked highest with Ekor Payau rice seeds in preference 0.6529. Meanwhile, DM3 ranked highest in Gelagai rice seeds with a selection of 0.7728. The final group voting system uses Borda, where Gelagai rice seeds occupy the highest rank with the most accumulated votes from each DMs. The best option or the highest rating based on the assessment of the three DMs, DM1 as a farmer is the first rank A10 Gelagai with a score of Borda 26 in the decision group selection of superior rice seeds
Advanced mask region-based convolutional neural network based deep-learning model for lung cancer detection
Get PDFMillions of individuals are affected each year by lung cancer, a serious global health concern. It may also cause numerous potentially fatal pulmonary problems, including infections, hemorrhage, or collapse. Finding a consistent and an effective way to ascertain lung cancer using medical imaging techniques is one of the primary issues in medical image processing. The difficulty of this task stems from the fact that the regions of the lungs that are affected by cancer might differ greatly in expressions of their size, location, shape, and aesthetics. Identifying whether the identified area is benign (non-cancerous) or malignant (cancerous) is another difficult task. Finding the appropriate course of treatment for the patient will depend on this. A critical stage in the identification of lung malignancy is identifying the knobs that are expected to be malevolent. To solve these issues, in this study work we employ a deep learning methodology based on Mask region-based convolutional neural network (Mask-RCNN). For the purpose of identifying and locating infected lung regions on computed tomography (CT) scan images, model is built utilizing the customized Mask-RCNN. In accordance with the evaluation's findings, the model scored 99.32% for accuracy and 99.45% for mean DICE, respectively
Predicting hepatitis C infection with machine learning algorithms: a prospective study
Get PDFGlobally, chronic hepatitis C virus (HCV) infection affects millions of people and leads to a high number of deaths annually. In 2019, the World Health Organization (WHO) recorded around 290,000 deaths related to HCV, a virus transmitted mainly through blood that causes liver damage. The virus has infected more than 169 million people worldwide. This study aims to compare the performance of machine learning (ML) models for HCV detection. ML models such as logistic regression (LR), random forest (RF), decision tree (DT), and catBoost classifier (CATBC) were used. To carry out this task, a dataset of 615 patient records, and 14 variables were used. This research process was carried out in multiple phases, encompassing model understanding, data analysis and cleaning, ML model training, and subsequent model evaluation. The results revealed that the gradient boosting (GB) model stood out by achieving the best performance and highest accuracy, achieving a rate of 94% in HCV detection, this demonstrates outstanding performance compared to the other models such as LR, RF, k-nearest neighbor (KNN), DT, and CATBC, which obtained accuracy rates of 89%, 93%, 85%, 93%, 93%, and 92%, respectively. It can be concluded that the GB model stands out as the best algorithm for this task
Hybrid adaptive neural network for remote sensing image classification
Get PDFThe proposed study employed a method for identifying the main contents (category/class) that a remote sensing image (RSI) belongs to, as well as the percentage contribution if the image comprises a significant number of different content types. Histogram based approach has been used to extract the pixel density distribution (PDD) and its normalized form helps to make solution independent from image physical characteristics. A multilayer feedforward artificial neural network (ANN) design has been used to address the classification problem. The architecture included an adaptive form of transfer function, whose slope characteristics changes along with weights as learning progresses. The approach of solution design is computation efficient because it doesn’t apply extensive pre-processing
Breast cancer detection through attention based feature integration model
Get PDFBreast cancer is detected by screening mammography wherein X-rays are used to produce images of the breast. Mammograms for screening can detect breast cancer early. This research focuses on the challenges of using multi-view mammography to diagnose breast cancer. By examining numerous perspectives of an image, an attention-based feature-integration mechanism (AFIM) model that concentrates on local abnormal areas associated with cancer and displays the essential features considered for evaluation, analyzing cross-view data. This is segmented into two views the bi-lateral attention module (BAM) module integrates the left and right activation maps for a similar projection is used to create a spatial attention map that highlights the impact of asymmetries. Here the module's focus is on data gathering through medio-lateral oblique (MLO) and bilateral craniocaudal (CC) for each breast to develop an attention module. The proposed AFIM model generates using spatial attention maps obtained from the identical image through other breasts to identify bilaterally uneven areas and class activation map (CAM) generated from two similar breast images to emphasize the feature channels connected to a single lesion in a breast. AFIM model may easily be included in ResNet-style architectures to develop multi-view classification models
Artificial intelligence and internet of things in manufacturing decision processes
Get PDFThis paper explores the influence of the internet of things (IoT) and artificial intelligence (AI) on the decision-making processes of modern manufacturing systems. With the proliferation of IoT devices and the development of AI technologies, manufacturing companies increasingly leverage these technologies to improve their decision-making abilities. This study aims to investigate the potential benefits, difficulties, and ramifications of integrating IoT and AI in manufacturing systems. The review employs the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method with a systematic mapping process with four research questions. A total of 1282 articles were collected between 2017 and 2023, reviewed in accordance with the inclusion and exclusion criteria, and 66 articles were chosen. The research on IoT and AI technologies influentially affects other research in the production control layer manufacturing area based on the top-ten cited articles. In contrast, the research in this area focused on the operations management layer, specifically manufacturing analytics processes. This paper’s findings contribute to a greater understanding of the impact of IoT and AI on decision-making in modern multi-domain manufacturing systems and provide direction for future research in this field
An artificial intelligence approach to smart exam supervision using YOLOv5 and siamese network
Get PDFArtificial intelligence has introduced revolutionary and innovative solutions to many complex problems by automating processes or tasks that used to require human power. The limited capabilities of human efforts in real-time monitoring have led to artificial intelligence becoming increasingly popular. Artificial intelligence helps develop the monitoring process by analysing data and extracting accurate results. Artificial intelligence is also capable of providing surveillance cameras with a digital brain that analyses images and live video clips without human intervention. Deep learning models can be applied to digital images to identify and classify objects accurately. Object detection algorithms are based on deep learning algorithms in artificial intelligence. Using the deep learning algorithm, object detection is achieved with high accuracy. In this paper, a combined model of the YOLOv5 model and network Siames technology is proposed, in which the YOLOv5 algorithm detects cheating tools in classrooms, such as a cell phone or a book, in such away that the algorithm detects the student as an object and cannot recognize his face. Using the Siames network, we compare the student’s face against the data base of students in order to identify the student with cheating tools
Segmentation and yield count of an arecanut bunch using deep learning techniques
Get PDFArecanut is one of Southeast Asia’s most significant commercial crops. This work aims at helping arecanut farmers get an estimate of the yield of their orchards. This paper presents deep-learning-based methods for segmenting arecanut bunch from the images and yield estimation. Segmentation is a fundamental task in any vision-based system for crop growth monitoring and is done using U-Net squared model. The yield of the crop is estimated using Yolov4. Experiments were done to measure the performance and compared with benchmark segmentation and yield estimation with other commodities, as there were no benchmarks for the arecanut. U-Net squared model has achieved a training accuracy of 88% and validation accuracy of 85%. Yolo shows excellent performance of 94.7% accuracy for segmented images, which is very good compared to similar crops
Multi platforms fake accounts detection based on federated learning
Get PDFIdentifying and mitigating fake profiles is an urgent issue during the age of widespread integration with social media platforms. this study addresses the challenge of fake profile detection on major social platforms-Facebook, Instagram, and X (Twitter). Employing a two-sided approach, it compares stacking model of machine learning algorithms with the federated learning. The research extends to four datasets, two Instagram datasets, one X dataset, and one Facebook dataset, reporting impressive accuracy metrics. Federated learning stands out for it is effectiveness in fake profile detection, prioritizing user data privacy. Results reveal Instagram fake/real dataset achieves 96% accuracy while Instagram human/bot dataset reaches 95% accuracy with federated learning. using the stacking model X’s fake/real dataset achieves 99.4% accuracy, and Facebook fake/real dataset reaches 99.8% accuracy using the same model. The study underscores the pivotal role of data privacy, positioning federated learning as an ethical choice. It compares the time efficiency of stacking and federated learning, with the former providing good performance in less time and the latter emphasizing data privacy but consuming more time. Results are benchmarked against related works, showcasing superior performance. The study contributes significantly to fake profile detection, offering adaptable solutions and insights
Towards a Docker-based architecture for open multi-agent systems
Get PDFIn open multi-agent systems (OMAS), heterogeneous agents in different environments or models can migrate from one system to another, taking their attributes and knowledge and increasing developing complexity compared to conventional multi-agent systems (MAS). Furthermore, the complexity of opening may be due to the uncertainties and dynamic behavior that the change of agents entails, needing to formulate techniques to analyze this complexity and understand the system’s global behavior. We used Docker to approach these problems and make the architecture flexible to handle distinct types of programming languages and frameworks of agents. This paper presents a Docker-based architecture to aid OMAS development, acting on agent migration between different models running in heterogeneous hardware and software scenarios. We present a simulation scenario with NetLogo’s Open Sugarscape 2 Constant Growback and JaCaMo’s Gold Miners to verify the proposal’s feasibility