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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Sanjel, Arun Khanal, Bikram Rivas, Pablo Speegle, Greg Baylor University School of Engineering and Computer Science Department of Computer Science United States

ISBN: (纸本)9798350362480

Processing big data poses a significant challenge when transitioning from sequential to distributed code, primarily due to the extensive scale at which data is handled. This complexity arises from both syntax and semantic differences between the codes. Unfortunately, current methods are inefficient and require more effective automated solutions. To address this problem, we utilized the Transformer-based BERT model because of its exceptional capability to understand and capture deep contextual relationships in large datasets. Our method involved creating two comprehensive datasets containing 10k and 100k sequential code snippets paired with their corresponding PySpark API calls. We optimized the BERT model by fine-tuning it to predict distributed API calls for previously unseen sequential snippets. For the 10k dataset, the model demonstrated robustness, achieving a training accuracy of 99.36%, a test accuracy of 99.7%, a Balanced Accuracy of 99.78%, and an F1 Score of 0.997. In contrast, the 100k dataset metrics were equally impressive, with a training accuracy of 89.48%, a test accuracy of 99.99%, a Balanced Accuracy of 99.98%, and an F1 Score of 0.999. Our work demonstrates the feasibility of automating the sequential-to-distributed code transition with notable precision. © 2024 IEEE.

关键词： Large datasets

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Lung Cancer Classification Using Modified ResNet Architecture 8

Lung Cancer Classification Using Modified ResNet Architectur...

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8th International Conference on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2024

作者： Tianto, Eric Chandaka Samudera, Axel Adeta, Franz Nadia, Nadia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350368970

One of the most prevalent and deadly cancers in the world, lung cancer presents serious difficulties for both patients and medical professionals. Even with the quick progress in early diagnosis, differentiating benign from malignant tumors is still a difficult task for doctors. By modifying the architecture and improving hyperparameters, this study aims to increase the model's accuracy in detecting subtypes of lung cancer and distinguishing between benign, malignant, and normal lesions. A new method for classifying lung cancer is provided by modifying the ResNet 18 architecture. While utilizing the basic hierarchical features that ResNet learnt, the suggested model incorporates modifications unique to the complexity of lung cancer imaging data. To assess the performance of the suggested model, the outcomes of this study (Modified ResNet 18) are compared with ResNet 18 and ResNet 50. Modified ResNet 18 exhibits the highest accuracy of 97%, followed by ResNet 18 at 95% and ResNet 50 at 95%. © 2024 IEEE.

关键词： Lung cancer

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Language Translator Using Python 1

Language Translator Using Python

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Raj, Mayank Devang, Ashish Chanchal, Satya Saini, Kavita Galgotias University School of Computer Science and Engineering Department of Computer Science and Engineering India

ISBN: (纸本)9798350356816

The most well-known type of correspondence between people is Discourse and correspondence assumes a vital part for conveying data. The truth of the matter is that each specific person on the planet won't discuss the same language, numerous people will be communicating in various dialects. Summing up the required content is particularly vital to conveying the right message. On the off chance that the data required isn't conveyed appropriately then numerous entanglements might happen. If either side of the imparting people is uninformed about the language, then the pattern of discourse will be deficient. Thus, we want a framework that will settle our language obstruction issue. So, we concocted a framework for example "Language Convertor utilizing Python"which will change over discourse of one language into another dialect utilizing Python. project intends to foster a flexible and easy-to-use instrument that can consistently interpret text between different dialects. This undertaking uses the force of Python, alongside famous libraries like Google Decipher programming interface or DeepL API, to make a proficient language change device. © 2024 IEEE.

关键词： Python

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History Leap: Enhancing Historical Learning Through a Game-Based Approach

History Leap: Enhancing Historical Learning Through a Game-B...

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2024 International Conference on Information Technology and Computing, ICITCOM 2024

作者： William Hartoko, Gandhi Kenji, Timothy Fajar, Muhamad School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350379839

The development of 'History Leap: Indonesian Independence' integrates engaging 2D platformer gameplay with historical narratives to stimulate dwindling interest in Indonesian history. The game's urgency lies in addressing the decreasing engagement with traditional history education, making it vital to innovate educational methods. Our research utilizes a game development approach, specifically the Agile methodology, to create an educational game that elucidates Indonesia's journey to independence. Participants, including university and high school students, played the game and evaluated its educational impact. The results showed significant satisfaction with the game's learning effectiveness and enjoyment, despite lower scores in game presence. These findings were analyzed using T-Test and Mann-Whitney methods based on the data normality. This underscores the potential of game-based learning to enhance historical education across different educational stages. © 2024 IEEE.

关键词： Aseprite Education Game History Indonesian Independence Unity

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Salary Classification & Prediction based on Job Field and Location using Ensemble Methods

Salary Classification & Prediction based on Job Field and Lo...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Siswanto, Jocelyn Verna Castilani, Laurentia Alyssa Winata, Natasha Hartanti Nugraha, Nathania Christy Sagala, Noviyanti T M Bina Nusantara University Data Science Program School of Computer Science Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Statistics Department Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

The economy is one of the determinants of how a person can live their life. In this current economic situation, inflation occurs everywhere, causing the prices of necessities to rise. In order to have a decent life, people must find a job with the highest possible salary to fulfill their needs. Various job industries have their salary range. Obtaining the information of salary level for the respective job is helpful for employers and employees to estimate the expected salary. This work aims to classify the salary level of jobs available in Indonesia and determine whether those salaries are decent enough. The learning methods are logistic regression, decision tree, k-nearest neighbor, support vector machine, voting classifier, bagging classifier, random forest, and boosting classifier. Random Forest achieved the best result with an accuracy rate of 72%. Based on the analysis result, factors such as job field, educational background, working experience, working hours, and job location influence salary. © 2023 IEEE.

关键词： Decision trees

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Virtual and augmented reality games for stroke rehabilitation: A systematic literature review into the game design 9th

Virtual and augmented reality games for stroke rehabilitatio...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Taufiq, Milkhatussyafa'ah Computer Science Department School of Computer Science Bina Nusantara University 11480 Indonesia

Augmented Reality (AR) and Virtual Reality (VR) games possess the capacity to provide significant benefits for post-stroke rehabilitation by enhancing the healing process and reducing costs and time requirements. This work offers a practical answer to the constraints presented by the execution of rehabilitation games after a stroke. The approach involves evaluating the gaming elements, such as challenge and action, in game design. The objective is to identify a game design that aligns with the specific area of the body that needs improvement during the rehabilitation session. Employing the PRISMA framework to execute a Systematic Literature Review (SLR) involves the identification of pertinent data sources, assessment of abstracts, selection of eligible articles, and analysis of data using the meta-analysis method. During this method, we conducted an analysis of 198 articles from various database sources. From this analysis, we highlighted 24 papers that need further examination. This study discovered several game concepts that have been shown useful in aiding the rehabilitation of stroke sufferers. Nevertheless, there are other constraints, including a dearth of studies investigating the lower body and cognitive components, a lack of research addressing the rehabilitation of lip movements and pointing the foot to control objects, as well as a lack of assessment of the outcomes of gameplay. © 2024 The Authors.

关键词： Virtual environments

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Detection of Defects in Plastic Cup Production Using YOLO Method 7

Detection of Defects in Plastic Cup Production Using YOLO Me...

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7th International Conference on data science and Its Applications, ICoDSA 2024

作者： Adhisaputra, Stephanie Harefa, Jeklin School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350365351

The integration of computer vision and deep learning techniques offers a promising avenue for the real-time detection of defective plastic cups in the plastic cup industry. Automated inspection in plastic cup manufacturing overcomes the limitations of manual inspection, which is time-consuming, subjective, and susceptible to human error. However, it needs to be adapted to suit the specific conditions of machines and defects in real-world scenarios. This paper investigates the utilisation of the You Only Look Once (YOLO) for object detection method in plastic cup production. The study focuses on achieving high precision in defect detection with high processing speed, which is crucial for real-time applications in practical manufacturing environments. The dataset comprises images of defective and normal plastic cups, including some images taken from a plastic cup manufacturer, representing real-world production scenarios. These images were used to train YOLOv8 and Faster R-CNN models to find the most suitable method. The models were compared based on performance metrics, including precision, mean Average Precision at 50% intersection over union (mAP50), recall, and inference speed. Validation results demonstrate the efficacy of the YOLOv8s model, which achieved a mAP50 score of 0.984 after training for 100 epochs, making it suitable for real-time applications in practical manufacturing environments. © 2024 IEEE.

关键词： Deep learning

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Comparing Hate Speech Detection Machine Learning Models Trained on Authentic and Translated Indonesian data 14

Comparing Hate Speech Detection Machine Learning Models Trai...

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14th International Conference on System Engineering and Technology, ICSET 2024

作者： Oroh, Joshua Andrew Pieter Setiawan, Dominick Albert Lucky, Henry Suhartono, Derwin Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798331504526

Social media is growing exponentially, it has become a place where people can express themself freely. However, not often that hate speech can be found in the expressions. It is important to prevent hate speech as it is harmful for our society. There has been a lot of research about the detection of hate speech using artificial intelligence however most of this research focuses on the models, there is a few research that focuses on the data rather than the model. Our research aims to find the effects of the data itself towards the performance of the models. We achieve this by having two separate datasets: hate speech in Indonesian and hate speech in English which is translated into Indonesian using Google translate Service. We use these datasets separately into several machine learning models including Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The best result was achieved by the RF model with 75% accuracy and weighted average F1 score for the Indonesian dataset and 58% accuracy and weighted average F1 score for the translated dataset. The Indonesian dataset is proven to be superior than the translated dataset. © 2024 IEEE.

关键词： Adversarial machine learning

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Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

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Tsinghua science and Technology 2024年第4期29卷 985-1001页

作者： N.Meenakshi Sultan Ahmad A.V.Prabu J.Nageswara Rao Nashwan Adnan Othman Hikmat A.M.Abdeljaber R.Sekar Jabeen Nazeer SRM Institute of Science and Technology KattankulathurChennai 603203India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj 11942Saudi Arabia University Center for Research and Development(UCRD) Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali 140413India Department of ECE Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522502India Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering(A)NTR District 521230India Department of Computer Engineering College of EngineeringKnowledge UniversityErbil 44001Iraq Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmman 11937Jordan Department of Electronics and Communication Engineering(ECE) School of EngineeringPresidency UniversityBangalore 834001India

The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human *** widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military *** make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the *** data have to be picked up by the sensor,and then sent to the sink node where they may be *** nodes of the WSNs are powered by batteries,therefore they eventually run out of *** energy restriction has an effect on the network life span and environmental *** objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of *** lifespan of WSNs is being extended often using clustering and routing *** Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do *** cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node *** on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS *** overall experimentation is carried out under the MATLAB *** the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.

关键词： wireless sensor networks energy efficient engroove leach protocol meta inspired Hawks fragment optimization heuristic wing antfly optimization

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MediaPipe’s Pose-Based Human Activity Recognition with LSTM 6

MediaPipe’s Pose-Based Human Activity Recognition with LSTM

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Muliawan, Nicholas Hans Irmawan, Frederick Nathan Angky, Edbert Valencio Prabowo, Abram Setyo Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

It is difficult to comprehend and categorize human movements in Human Activity Recognition. Despite being widely utilized, convolutional neural networks (CNN) might not adequately represent the intricacy of motions. This work investigates an alternative method for classifying human motion: the LSTM model combined with MediaPipe. Sequential processing is used by the LSTM model for keypoint sequences, which improves its accuracy in identifying complex human movement features. Although OpenPose provides precise body mapping, MediaPipe is used in this research because of its computational efficiency and ease of integration. Using CV2 libraries, preprocessed video data is read at 15 frames per second. MediaPipe software is then used to extract 33 keypoints coordinates from each frame. Then, in order to make these keypoints compatible with the LSTM model, they are compressed into single-dimensional arrays. The keypoints from each picture frame are taken out and combined with other frames for a total of 15 frames into a single sequence. The aim of this study is to evaluate the usefulness of the LSTM model in human action classification by contrasting its performance with more conventional sequence modeling methods. Following evaluation, we employed the Recognition of Human Action dataset from Kungliga Tekniska Hogskolan (KTH) University, which is used by LSTM models. Over 99% of the metricsAccuracy, Precision, Recall, and F1-scorewere attained by the model. © 2024 IEEE.

关键词： computer vision

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