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1st IEEE International Conference on Cryptography, Informatics, and Cybersecurity, ICoCICs 2023

作者： Yulianto, Semi Abdullah, Roni Reza Soewito, Benfano Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia National Institute of Science and Technology Faculty of Informatics Engineering Jakarta12630 Indonesia Bina Nusantara University Binus Graduate Program. Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350339437

The escalating visibility of secure direct object reference (IDOR) vulnerabilities in API security, as indicated in the compilation of OWASP Top 10 API Security Risks, highlights a noteworthy peril to sensitive data. This study explores IDOR vulnerabilities found within Android APIs, intending to clarify their inception while evaluating their implications for application security. This study combined the qualitative and quantitative approaches. Insights were obtained from an actual penetration test on an Android app into the primary reasons for IDOR vulnerabilities, underscoring insufficient input validation and weak authorization methods. We stress the frequent occurrence of IDOR vulnerabilities in the OWASP Top 10 API vulnerability list, highlighting the necessity to prioritize them in security evaluations. There are mitigation recommendations available for developers, which recognize its limitations involving a possibly small and homogeneous selection of tested Android applications, the testing environment that could cause some inaccuracies, and the impact of time constraints. Additionally, the study noted insufficient threat modeling and root cause analysis, affecting its generalizability and real-world relevance. However, comprehending and controlling IDOR dangers can enhance Android API security, protect user data, and bolster application resilience. © 2023 IEEE.

关键词： Sensitive data

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Target Driven Autoencoder: A Supervised Learning Approach for Tumor Segmentation 4

Target Driven Autoencoder: A Supervised Learning Approach fo...

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4th International Conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Das, Abhishek Mohapatra, Saumendra Kumar Pattanaik, Rakesh Kumar Tripathy, Balaram Patra, Gyana Ranjan Mohanty, Mihir Narayan Centurion University of Technology and Management Department of Computer Science Engineering Odisha Paralakhemundi India Shri Shri University Department of Computer Science Engineering Odisha Cuttack India Dnr College of Engineering and Technology Department of Electronics and Communication Engineering Andhra Pradesh Bhimavaram India S.K.D.A.V. Government Polytechnic Department of Electronics and Telecommunication Engineering Odisha Rourkela India Iter Department of Electronics and Communication Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350349856

A particular kind of neural network called an autoencoder (AE) is trained to discover a reduced and effective representation (encoding) of input data. In most of the applications of AE, it is found that this model has been used for generating the same images in compressed form. AE has also been used for encoding the images and signals. In this work, a supervised autoencoder is designed that is trained with both input images and corresponding targets that are also the images. This allows the model to learn a mapping from the input image to the target image specifically for segmentation. The proposed method is verified and trained with brain MRI images and segmented tumors. The dice score obtained in this method is 98.43% in producing segmented tumors. © 2024 IEEE.

关键词： Tumors

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Giant phonon-skyrmion coupling in ferromagnet/heavy metal heterostructures

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Physical Review B 2024年第6期110卷 064423-064423页

作者： Egor Savostin Vitaliy Lomakin Program in Materials Science and Engineering Center for Memory and Recording Research Department of Electrical and Computer Engineering

The strong impact of the strain-induced Dzyaloshinskii-Moriya interaction (SIDMI) on the magnetization dynamics of skyrmions in nanomagnetic structures is demonstrated. The effects of SIDMI are characterized by skyrmion equations (SEs) of motion and magnetoelastic (ME) equations. The study is performed on a model system of MgO/CoFe/Pt stacked on a piezoelectric substrate. The results demonstrate a major nonlinear amplification in both the first- and higher-harmonic magnitudes of the skyrmion breathing mode due to SIDMI. Remarkably, this enhancement can trigger a skyrmion collapse, enabling its deletion with ultraweak strain-induced excitations. The SIDMI effect is shown to be much more significant than the conventional ME effect. These findings open different avenues for the efficient manipulation of nanomagnetic structures through strain.

关键词： Exchange interaction Magnetic interactions Magnetization dynamics Magnetoacoustic effect Micromagnetism Spin dynamics

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Evaluation of Deep Convolutional Neural Network-Based Tattoo Detection 7

Evaluation of Deep Convolutional Neural Network-Based Tattoo...

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7th International Congress on Ambient Intelligence, Software engineering, and e-Health and Mobile Health, AmITIC 2024

作者： Jiménez-Delgado, E. Quesada-López, C. Méndez-Porras, A. Lara-Petitdemange, A. Alfaro-Velasco, J. Graduate Program in Computer Science and Informatics University of Costa Rica Costa Rica Technological Institute of Costa Rica Computer Engineering Department Costa Rica School of Computer Science and Informatics University of Costa Rica Costa Rica

ISBN: (纸本)9798350364538

In this study, two deep learning models for automatic tattoo detection were analyzed;a modified Convolutional Neural Network (CNN) and pre-trained ResNet-50 model. In order to achieve this, ResNet-50 uses transfer learning with fine-tuning. The purpose of this study was to evaluate the accuracy, precision, recall, F1-score, and computational efficiency of the system being considered. To augment the dataset included 1000 photos that were equally divided between those showing tattoos and those that did not show tattoos. A k-fold cross-validation approach was employed in training and testing the models. Although custom CNNs are effective, utilizing pre-trained ones like ResNet-50 can offer even better outcomes. Specifically, ResNet-50 attained a higher accuracy (0.86 compared to 0.79), precision (0.85 versus 0.78), recall (0.91 against 0.86), and F1-score (0.91 vis-a-vis 0.86) as compared to custom CNNs. In selecting these models for examination, two main motivations were considered. The first motivation is to see whether transfer learning with a pre-trained ResNet-50 model does well when compared with a customized CNN designed specifically for tattoo detection. Secondly,the intent of this study is to know what advantages can be derived from each approach and their demerits too. Furthermore, it seeks to determine if transfer learning can provide an alternative in contrast to the common CNN techniques with regards to precision and computational efficiency. In this research, two models will be evaluated in order to answer the question of what is better for tattoo detection: transfer learning or designing custom architectures. © 2024 IEEE.

关键词： Convolution

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Fuzzy Logic for Assessing the Health of a Corn Crop within Family Farming

Fuzzy Logic for Assessing the Health of a Corn Crop within F...

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2024 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2024

作者： Mendonca, Vanessa Santos, Mikael Munoz, Daniel M. Humberto Llanos, Carlos Federal Institute of Brasilia Department of Computer Science Brazil Mechatronics Graduate Program Department of Mechanical Engineering Brazil Federal Institute of Brasilia Department of Agronomy Brazil University of Brasilia Faculty of Gama Electronics Engineering Undergraduate Program Brasilia Brazil

ISBN: (纸本)9798350374575

Technological advancements are increasingly evident across various sectors, including automobiles, industry, and healthcare. In precision agriculture, significant progress has been made, with AgroTICs and Smart Agriculture gaining substantial traction in the market. However, a gap remains between cutting-edge technology and family farming, presenting a challenge from both social and applied research perspectives. However, there is still a gap between cutting-edge technology and family farming, which creates a challenge from a social and applied research point of view. In this context, this paper proposes a monitoring model based on Fuzzy Logic and sensor automation applied to estimate the health of a corn crop. The proposed Fuzzy inference system involves calculating an indicator of nutrients as well as the average color and area of corn plants. The nutrient indicator is automatically computed by an ESP32 microcontroller using sensor readings, while the average color and area inputs are manually entered via a mobile application. Additionally, the Fuzzy inference is integrated into the ESP32. The model underwent experimental validation on the health of the plantation, and the results were evaluated in four areas: one was designated for testing, and three were for validation. The model achieved an accuracy of 97.5% in Scenario 3, categorized as 'Very Favorable', and an accuracy of 65% in Scenarios 2 and 4, categorized as 'Unfavorable'. The implications of this research contribute to the advancement of AgroTICs among small producers, with the potential to enhance and automate the monitoring of their harvest production. © 2024 IEEE.

关键词： Agribusiness

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An Efficient Text-Independent Speaker Identification Using Feature Fusion and Transformer Model

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computers, Materials & Continua 2023年第5期75卷 4085-4100页

作者： Arfat Ahmad Khan Rashid Jahangir Roobaea Alroobaea Saleh Yahya Alyahyan Ahmed H.Almulhi Majed Alsafyani Chitapong Wechtaisong College of Computing Khon Kaen UniversityKhon Kaen40000Thailand Department of Computer Science COMSATS University IslamabadVehari CampusVehari61100Pakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science Community College in DwadmiSharqa UniversityDawadmi17472Saudi Arabia School of Telecommunication Engineering Suranaree University of TechnologyNakhon Ratchasima30000Thailand

Automatic Speaker Identification(ASI)involves the process of distinguishing an audio stream associated with numerous speakers’*** common aspects,such as the framework difference,overlapping of different sound events,and the presence of various sound sources during recording,make the ASI task much more complicated and *** research proposes a deep learning model to improve the accuracy of the ASI system and reduce the model training time under limited computation *** this research,the performance of the transformer model is *** audio features,chromagram,Mel-spectrogram,tonnetz,Mel-Frequency Cepstral Coefficients(MFCCs),delta MFCCs,delta-delta MFCCs and spectral contrast,are extracted from the ELSDSR,CSTRVCTK,and Ar-DAD,*** evaluation of various experiments demonstrates that the best performance was achieved by the proposed transformer model using seven audio features on all *** ELSDSR,CSTRVCTK,and Ar-DAD,the highest attained accuracies are 0.99,0.97,and 0.99,*** experimental results reveal that the proposed technique can achieve the best performance for ASI problems.

关键词： Speaker identification signal processing Arabic deep learning transformer

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Development of a Deep Learning-Based System for Road Traffic Anomaly Detection

Development of a Deep Learning-Based System for Road Traffic...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Ogundokun, Roseline Oluwaseun Ashiedu, Nicole Olaniyan, Julius Olaniyan, Deborah Awoniyi, Charles Victoria, Adedayo-Ajayi Akande, Hakeem Babalola Adebiyi, Abayomi A. Landmark Univerisity Department of Computer Science Omu Aran Nigeria Kaunas University of Technology Department of Multimedia Engineering Lithuania Bupa Insurance Services London United Kingdom University of Ilorin Department of Telecommunication Science Nigeria Durban University of Technology Department of Electrical Power Engineering South Africa

ISBN: (纸本)9798350358155

Continuous supervision is required to monitor real-world traffic surveillance recordings and take necessary steps in the event of tragic incidents. Nevertheless, constantly monitoring them under human supervision is laborious and prone to errors. Hence, a deep learning technique has been suggested to automatically identify and pinpoint road accidents by framing the issue as anomaly detection. This study focuses on developing an advanced system that uses deep learning approaches to detect irregularities in road traffic. Our research aims to meet this necessity by utilizing the potential of deep learning techniques that are specifically adapted to the complexities of road traffic monitoring. To overcome the limitations of lack of knowledge of the causes of road traffic, the system employs Super-Resolution Generative Adversarial (SRGAN) and Convolutional Neural Network (CNN) models to classify the images obtained from the dataset. The study performed excellently well using CNN and SRGAN with an accuracy of 100%, precision of 100%, recall of 100%, f1-score of 100% and ROC value of 100%. © 2024 IEEE.

关键词： Convolutional neural networks

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Density Based Spatial Clustering of Applications with Noise and Sentence Bert Embedding for Indonesian Utterance Clustering

Density Based Spatial Clustering of Applications with Noise ...

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

作者： Hasani, Muhammad Fikri Heryadi, Yaya Arifin, Yulyani Lukas Suparta, Wayan Bina Nusantara University BINUS Graduate Program Computer Science School of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350320954

Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance. © 2023 IEEE.

关键词： Embeddings

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A Performance Optimization of Quantum Computing Simulation using FPGA 19

A Performance Optimization of Quantum Computing Simulation u...

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19th International Conference on Electrical engineering/Electronics, computer, telecommunications and Information Technology, ECTI-CON 2022

作者： Jungjarassub, Yaninee Piromsopa, Krerk Chulalongkorn University Computer Science Program Department of Computer Engineering Faculty of Engineering Bangkok Thailand

ISBN: (纸本)9781665485845

We use FPGA to optimize the simulation of quantum computing in two aspects. (a) The if-else state is used in place of tensor product calculation. This allows the tensor product of each quantum operator to be generated in a single clock cycle. (b) The pre-calculated lookup ROM is used for estimating the sine and cosine values. This facilitates the computation of quantum gates that are related to angle. To validate our work, we implement our design in VerilogHDL. The performance is evaluated using an FPGA simulator. The result shows a dramatic improvement in the simulation process comparing to those of simulation on classical computers. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Cardiovascular Disease Analysis Using Correlational Analysis and Association Rules Mining for In-depth Analysis to Identify Predominant Variables

Cardiovascular Disease Analysis Using Correlational Analysis...

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

作者： Siswanto, Boby Soeparno, Haryono Sianipar, Nesti F. Budiharto, Widodo Bina Nusantara University BINUS Graduate Program- Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Engineering Faculty Food Technology Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350320954

Cardiovascular disease is one of the dangerous non-communicable disorders or diseases that has become one of the causes of death worldwide. Various studies have been conducted to prevent cardiovascular disease in the world. This study analyzed cardiovascular disease medical record data from the Kaggle public dataset by implementing correlational analysis combined with association rule mining to identify variables that are the predominant cause of cardiovascular disease. Correlational analysis can analyze the interrelationships between variables in a dataset, but not in depth. Association rule mining can identify the interrelationships of variables in the form of frequent item sets, which can be calculated for their support and confidence values. The result of this study is a combination of correlation analysis with association rule mining that can identify predominant variables to cause cardiovascular disease. Found that the variable gender=woman, height=short ( © 2023 IEEE.

关键词： Association rules

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