bluuebunny/crossref_metadata_2025 huggingface dataset:
Transparent precision: Explainable AI empowered breast cancer recommendations for personalized treatment
Authors: Lokare Reena R , Wadmare Jyoti , Patil Sunita , Wadmare Ganesh , Patil Darshan
Published: July 2024

Breast cancer stands as a prevalent global concern, prompting extensive research into its origins and personalized treatment through Artificial Intelligence (AI)-driven precision medicine. However, AI's black box nature hinders result acceptance. This study delves into Explainable AI (XAI) integration for breast cancer precision medicine recommendations. Transparent AI models, fuelled by patient data, enable personalized treatment recommendations. Techniques like feature analysis and decision trees enhance transparency, fostering trust between medical practitioners and patients. This harmonizes AI's potential with the imperative for clear medical decisions, propelling breast cancer care within the precision medicine era. This research work is dedicated to leveraging clinical and genomic data from samples of metastatic breast cancer. The primary aim is to develop a machine learning (ML) model capable of predicting optimal treatment approaches, including but not limited to hormonal therapy, chemotherapy, and anti-HER2 therapy. The objective is to enhance treatment selection by harnessing advanced computational techniques and comprehensive data analysis. A decision tree model developed here for the prediction of suitable personalized treatment for breast cancer patients achieves 99.87% overall prediction accuracy. Thus, the use of XAI in healthcare will build trust in doctors as well as patients.
Securing high-value electronic equipment: an internet of things driven approach for camera security
Authors: Nordin Siti Aminah , Yusoff Zakiah Mohd , Hanif Faisal Muhammad , Kamarudin Khairul
Published: July 2024

This article addresses pressing challenge of securing high-value electronic equipment, notably cameras, which face dual threats of damage in high humidity conditions and theft due to their significant market value. To confront these issues, the study introduces innovative internet of things (IoT)-driven approach aimed at strengthening conventional storage box security. Central to this approach is integration of IoT technologies, such as Arduino and ESP32, to develop advanced safety storage box. This enhanced system features essential hardware components, including buzzer, password-protected keypad, radio frequency identification reader, and DHT11 sensor for humidity monitoring. Additionally, mobile alarm system is incorporated to promptly alert owners of any detected movement vibrations, thereby augmenting security measures. By leveraging these components, proposed methodology seeks to mitigate risks associated with camera theft and fungal contamination, thereby advancing electronic device security. The expected outcome is marked enhancement in protection of high-value electronic equipment, particularly cameras, through continuous real-time monitoring and proactive security measures. This research underscore’s critical role of IoT technologies in fortifying security measures for valuable electronic assets, contributing significantly to ongoing discourse on innovative strategies in field. Through its comprehensive approach, this study aims to offer practical solutions to mitigate security risks and safeguard electronic equipment against potential threats, thereby addressing critical need in realm of electronic device security.
Fuzzy logic based sliding surface adjustment of second-order sliding mode controllers
Authors: V P Basheer , Kareem Abdul , Aithal Ganesh
Published: July 2024

This research work designs a variant of second-order sliding mode control scheme, making use of varying sliding surface inferred using a fuzzy inference system. The varying sliding surface is an effective strategy to improve controller performance. A surface with a relative degree of two is first built by accounting for the uncertainties and perturbances of the system. Thereafter, in order to enhance the dynamics of the system being controlled, a varying sliding surface based on a straightforward double input-single output fuzzy logic inference architecture is proposed. The controller ensures system's reaching conditions, and also the stability and robustness. The designed control scheme is studied in comparison with a sliding mode controller of second order having a constant surface of sliding using SIMULINK based simulation for a nonlinear system. The comparison shows that the proposed strategy exhibits an improved dynamic performance than the conventional sliding mode control of second order having a constant surface of sliding.
On-device training of artificial intelligence models on microcontrollers
Authors: Thai Bao-Toan , Tran Vy-Khang , Pham Hai , Nguyen Chi-Ngon , Nguyen Van-Khanh
Published: July 2024

Numerous studies are currently training artificial intelligence (AI) models on tiny devices constrained by computing power and memory limitations by implementing model optimization algorithms. The question arises whether implementing traditional AI models directly on small devices like micro-controller units (MCUs) is feasible. In this study, a library has been developed to train and predict the artificial neural network (ANN) model on common MCUs. The evaluation results on the regression problem indicate that, despite the extensive training time, when combined with multitasking programming on multi-core MCUs, the training does not adversely affect the system's execution. This research contributes an additional solution that enables the direct construction of ANN models on MCU systems with limited resources.
Autonomous radar interference detection and mitigation using neural network and signal decomposition
Authors: Kurniawan Dayat , Rohman Budiman Putra Asmaur , Indrawijaya Ratna , Wael Chaeriah Bin Ali , Suyoto Suyoto , Adhi Purwoko , Firmansyah Iman
Published: July 2024

Autonomous radar interference is a challenging problem in autonomous vehicle systems. Interference signals can decrease the signal-to-interference-noise ratio (SINR), and this condition decreases the performance detection of autonomous radar. This paper exploits a neural network and signal decomposition to detect and mitigate radar interference in autonomous vehicle applications. A neural network (NN) with four inputs, one hidden layer, and one output is trained with various signal-to-noise (SNR), interference radar bandwidth, and sweep time of autonomous radar. Four inputs of NN represent SNR, mean, total harmonic distortion (THD), and root means square (RMS) of the received radar signal. Variational mode decomposition (VMD) and zeroing based on a constant false alarm rate (CFAR-Z) are used to mitigate radar interference. VMD algorithm is applied to decompose interference signals into multi-frequency sub-band. As a result, the proposed neural network can detect radar interference, and NN-VMD-CFAR-Z can increase SINR up to 2dB higher than the NN-CFAR-Z algorithm.
Enhanced multi-ethnic speech recognition using pitch shifting generative adversarial networks
Authors: Nugroho Kristiawan , Hadiono Kristophorus , Sutanto Felix , Marutho Dhendra , Farooq Omar
Published: July 2024

Research in the field of speech recognition is a challenging research area. Various approaches have been applied to build robust models. A problem faced in speech recognition research is overfitting, especially if there is insufficient data to train the model. A large enough amount of data can train the model well, resulting in high accuracy. Data augmentation is an approach often used to increase the quantity of dataset. This research uses a data augmentation approach, namely pitch shifting, to increase the quantity of speech dataset, which is then processed into spectrogram data and then classified using a generative adversarial network (GAN). Using the pitch shifting-generative adversarial network (PS-GAN) model, this research produces high accuracy performance in multi-ethnic speech recognition, namely 98.43%, better than several similar studies.
A comparison of meta-heuristic and hyper-heuristic algorithms in solving an urban transit routing problems
Authors: Muklason Ahmad , Ahlan Robbani Shof Rijal , Riksakomara Edwin , Premananda I Gusti Agung
Published: July 2024

Public transport is a serious problem that is difﬁcult to solve in many countries. Public transport routing optimization problem also known as urban transit routing problem (UTRP) is time-consuming process, therefore effective approches are urgently needed. UTRP aims to minimize cost passenger and operator from a combination of route set. UTRP can be optimize with heuristics, meta-heuristics, and hyper-heuristics methods. In several previous studies, UTRP can be optimized with any meta-heuristics and hyper-heuristics methods. In this study we compare the performance of meta-heuristic methods, i.e. ill-climbing, simulated annealing, and hyper-heuristics method based on modiﬁed particle swarm optimization algorithm. The experimental results showed that the proposed methods could solve UTRP effectively. Regarding their performance, the results show that despite the generality of hyper-heuristics, their performance are competitive. More speciﬁcally, hyper-heuristics method is the best method compared to the other two methods in each dataset. In addition, compared to prior studies results, he proposed hyper-heuristics could outperform them in term of cost passenger of small dataset Mandl. The main contribution of this paper is that to best of our knowledge, it is the ﬁrst study comparing the performance of meta-heuristics and hyper-heuristics approaches over UTRP.
Feature level fusion of multi-source data for network intrusion detection
Authors: Somashekar Harshitha , Halebidu Basavaraju Pramod
Published: July 2024

The generation of data, collecting, and refining in computer networks have increased exponentially in recent years. Network attacks have also grown in prevalence with this proliferation of data and are now an inherent issue in complicated networks. Current network intrusion detection systems (NIDS) have significant issues with regard to anomaly detection. Several machine learning classification approaches are used to create NIDSs, but they are not sufficiently sophisticated to reliably detect complicated or synthetic attacks, especially if working with a lot of multi-scale data. Data fusion has been used in network intrusion detection to address these issues. For network intrusion detection, we suggested a multi-source data fusion technique in this research, which combines specific features from two datasets to produce a single dataset. Also, a machine learning classifier with fewer parameters is utilized for the fused dataset. The random forest shows the best classification accuracy compared to others in this work. For the normal classification, model accuracy is 92.8%, and the proposed fusion model showed 97.3% accuracies. Furthermore, the findings show that, when compared to other cutting-edge techniques, the suggested model is substantially more effective in detecting intrusions.
Encoder-decoder approach for describing health of cauliflower plant in multiple languages
Authors: Mondhe Parag Jayant , Satone Manisha P. , Wasatkar Namrata N.
Published: July 2024

Physically examining each plant to determine its state of health and determining the disease if plant is affected due to it, is challenging. The encoder - decoder approach is proposed for describing health of cauliflower plant in English, Hindi and Marathi languages from aerial images. Experiments are performed with different CNN models and LSTM combinations. The Multilanguage Cauliflower Captions Dataset (MCCD) is developed to evaluate the performance of the model. The dataset contains 1213 images where each image is described in 3 different languages. The dataset contains images of cauliflower plant affected due to bacterial spot rot, black rot and downy mildew diseases. It also contains images of healthy plant. The objective metrics such as BLEU scores and subjective criteria are used to decide the quality of the generated description.
Inverse kinematic solution and singularity avoidance using a deep deterministic policy gradient approach
Authors: Surriani Atikah , Wahyunggoro Oyas , Imam Cahyadi Adha
Published: July 2024

The robotic arm emerges as a subject of paramount significance within the industrial landscape, particularly in addressing the complexities of its kinematics. A significant research challenge lies in resolving the inverse kinematics of multiple degree of freedom (M-DOF) robotic arms. The inverse kinematics of M-DOF robotic arms pose a challenging problem to resolve, thus it involves consideration of singularities which affect the arm robot movement. This study aims a novel approach utilizing deep reinforcement learning (DRL) to tackle the inverse kinematic problem of the 6-DOF PUMA manipulator as a representative case within the M-DOF manipulator. The research employs Jacobian matrix for the kinematics system that can solve the singularity, and deep deterministic policy gradient (DDPG) as the kinematics solver. This chosen technique offers enhancing speed and ensuring stability. The results of inverse kinematic solution using DDPG were experimentally validated on a 6-DOF PUMA arm robot. The DDPG successfully solves inverse kinematic solution and avoids the singularity with 1,000 episodes and yielding a commendable total reward of 1,018.
Framework for content server placement using integrated learning in content delivery network
Authors: Dharmapal Priyanka , Channakrishnaraju Channakrishnaraju
Published: July 2024

Content placement is a significant concern in content delivery networks (CDN), irrespective of various evolving studies. Existing methodologies showcase various significant unaddressed issues concerning content placement approaches' complexities. Therefore, the proposed study presents a novel computational framework towards dynamic content placement strategy using a novel integrated machine learning approach. Simplified mathematical modelling is used to formulate and solve the content placement problem. At the same time, reinforcement learning and the sequential attentional neural network have been utilized to optimize the decision-making towards placement of content servers. Designed and assessed over a Python environment, the proposed scheme is witnessed to exhibit 35% reduced bandwidth utilization, 20% reduced delay, 23% reduced computational resource utilization, and 28% reduced algorithm processing time in contrast to existing predictive content placement schemes.
Ensemble of naive Bayes, decision tree, and random forest to predict air quality
Authors: Resti Yulia , Eliyati Ning , Rahmayani Mau’izatil , Alwine Zayanti Des , Sri Kresnawati Endang , Setyo Cahyono Endro , Yani Irsyadi
Published: July 2024

Air quality prediction is an important research issue because air quality can affect many areas of life. This study aims to predict air quality using the ensemble method and compare the results with the prediction results using a single method. The proposed ensemble method is built from three singlesupervised methods: naïve Bayes, decision trees, and random forests. The results show that the ensemble method performs better than the single methods. The ensemble method achieves the highest performance with scores of 99.89% accuracy, 79.6% precision, 79.81% recall, and 79.7% F1-score. The performance comparison between single and ensemble models is expected to provide information on the percentage increase in predictive model performance metrics from the single to ensemble methods.
Identifying liver cancer cells using cascaded convolutional neural network and gray level co-occurrence matrix techniques
Authors: Chiterki Anil Bellary , Kumar Gowdru Arun , Prithviraja Dayananda , Chanabasappa Kundur Niranjan , Ramadoss Balakrishnan
Published: July 2024

Liver cancer has a high mortality rate, especially in South Asia, East Asia, and Sub-Saharan Africa. Efforts to reduce these rates focus on detecting liver cancer at all stages. Early detection allows more treatment options, though symptoms may not always be apparent. The staging process evaluates tumor size, location, lymph node involvement, and spread to other organs. Our research used the CLD staging system, assessing tumor size (C), lymph nodes (L), and distant invasion (D). We applied a deep learning approach with a cascaded convolutional neural network (CNN) and gray level co-occurrence matrix (GLCM)-based texture features to distinguish benign from malignant tumors. The method validated with the cancer imaging archive (TCIA) dataset, demonstrating superior accuracy compared to existing techniques.
Contextual embedding generation of underwater images using deep learning techniques
Authors: Kerai Shivani , Khekare Ganesh
Published: July 2024

This article delves into the cutting-edge realm of artificial intelligence, specifically focusing on its application in marine research via underwater image analysis. It introduces an innovative, integrated approach that combines object detection with image captioning tailored for the aquatic domain. Central to this approach is the advanced technique of image feature extraction, complemented by the strategic implementation of attention mechanisms within neural networks. These mechanisms are key in enhancing the precision and contextual understanding of underwater imagery. The efficacy of this method is underscored by extensive experiments on diverse underwater datasets. Results show notable improvements in detecting and describing complex underwater scenes, thereby providing invaluable insights for marine biologists, environmentalists, and the broader scientific community. This exploration marks a significant advancement in marine research, offering a new lens through which the underwater world can be understood and preserved.
Comparing logistic regression and extreme gradient boosting on student arguments
Authors: Wahyuningsih Tri , Manongga Danny , Sembiring Irwan
Published: July 2024

Identifying the effectiveness level and quality of students' arguments poses a challenge for teachers. This is due to the lack of techniques that can accurately assist in identifying the effectiveness and quality of students' arguments. This research aims to develop a model that can identify effectiveness categories in students' arguments. The method employed involves the logistic regression+XGBoost algorithm combined with separate implementations of term frequency-inverse document frequency (TF-IDF) and CountVectorizer. Student argument data were collected and processed using natural language processing techniques. The research results indicate that TF-IDF outperforms in identifying effectiveness classes in student arguments with an accuracy of 66.20%. The multi-output classification yielded an accuracy of 89.32% in the initial testing, which further improved to 92.34% after implementing one-hot encoding. A novel finding in this research is the superiority of TF-IDF as a technique for identifying effectiveness classes in student arguments compared to CountVectorizer. The implications of this research include the development of a model that can assist teachers in identifying the effectiveness level of students' arguments, thereby improving the quality of learning and enhancing students' argumentative competence.
A systematic review of non-intrusive human activity recognition in smart homes using deep learning
Authors: El Ghazi Mariam , Aknin Noura
Published: July 2024

Smart homes are a viable solution for improving the independence and privacy of elderly and dependent people thanks to IoT sensors. Reliable human activity recognition (HAR) devices are required to enable precise monitoring inside smart homes. Despite various reviews on HAR, there is a lack of comprehensive studies that include a diverse range of approaches, including sensor-based, wearable, ambient, and device-free methods. Considering this research gap, this study aims to systematically review the HAR studies that apply deep learning as their main solution and utilize a non-intrusive approach for activity monitoring. Out of the 2,171 studies in the IEEE Explore database, we carefully selected and thoroughly analyzed 37 studies for our research, following the guidelines provided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. In this paper, we explore various modalities, deep learning approaches, and datasets employed in the context of non-intrusive HAR. This study presents essential data for researchers to employ deep learning techniques for HAR in smart home environments. Additionally, it identifies and highlights the main trends, challenges, and future directions.
TourMapQA: using deep learning to develop a vietnam map-based tourism question answering system
Authors: Pham Vuong Ba , Nguyen Phuc Chi-Hong , Phung Bao The , Phan Truong H. V.
Published: July 2024

A question answering system is an important task in information retrieval. In recent years, this system has been interested in research and achieved outstanding results. In general, the output of the question answering is text. However, few studies have used a map as an answer to the question answering in Vietnam tourism. This paper introduces a question answering system integrating long short-term memory (LSTM) on the Vietnam map. Specifically, our model received an input question about any road in Vietnam. Then, the model used LSTM to indicate the coordinate of that road and called the Dijkstra algorithm to find the shortest path from the current location to the input road. Next, from the coordinate of the input road, we leveraged the LSTM model to identify sightseeing places that were on the shortest path. Finally, our system showed all the sightseeing places on the Vietnam map. Technically, the experimental results showed that our model’s performance was improved than previous models such as recurrent neural network, recurrent neural network with embedding, bidirectional recurrent neural network, and encoder-decoder recurrent neural network. Practically in terms, we applied our method to build a real application and compared it with Google Maps, and Bing Map.
DualFaceNet: augmentation consistency for optimal facial landmark detection and face mask classification
Authors: Songsri-in Kritaphat , Rattaphun Munlika , Kaewchada Sopee , Ruang-on Somporn
Published: July 2024

In an era where face masks are commonplace, facial recognition faces new challenges and opportunities. This study introduces DualFaceNet (DFN), a cutting-edge neural network that efficiently combines facial landmark detection with mask classification. Benefiting from multi-task learning (MTL) and enhanced with a unique consistency loss, DFN outperforms traditional single-task models. Tests using the reputable 300W dataset and a face mask dataset showcase DFN’s strengths: a significant reduction in landmark error to 5.42 and an increase in mask classification accuracy to 92.59%. These results highlight the potential of integrating MTL and custom loss functions in facial recognition. As face masks continue to be globally essential, DFN’s integrated approach offers a fresh perspective in facial recognition studies. Furthermore, DFN paves the way for adaptive facial recognition systems, emphasizing the adaptability needed in our current era.
Quantitative strategies of different loss functions aggregation for knowledge distillation
Authors: Doan Huong-Giang , Nguyen Ngoc-Trung
Published: July 2024

Deep learning models have been successfully applied to many visual tasks. However, they tend to be increasingly cumbersome due to their high computational complexity and large storage requirements. How to compress convolutional neural network (CNN) models while still maintain their efficiency has received increasing attention from the community, and knowledge distillation (KD) is efficient way to do this. Existing KD methods have focused on the selection of good teachers from multiple teachers, or KD layers, which is cumbersome, expensive computationally, and requires large neural networks for individual models. Most of teacher and student modules are CNN-based networks. In addition, recent proposed KD methods have utilized cross entropy (CE) loss function at student network and KD network. This research focuses on the quantifiable evaluation of teacher-student model, in which knowledge is not only distilled from training models that have the same CNN architecture but also from different architectures. Furthermore, we propose combination of CE, balance cross entropy (BCE), and focal loss functions to not only soften the value of loss function in transferring knowledge from large teacher model to small student model but also increase classification performance. The proposed solution is evaluated on four benchmark static image datasets, and the experimental results show that our proposed solution outperforms the state-of-the-art (SOTA) methods from 2.67% to 9.84% at top 1 accuracy.
Detection of vague object signatures on deep learning surveillance devices
Authors: Swardika I Ketut , Widyastuti Santiary Putri Alit
Published: July 2024

The deep learning of object detection has become a breakthrough in recent years. Many papers demonstrated that this method records significant reliability results. However, the question arises whether objects that were successfully detected are initially conditioned clear in daylight. The object being detected is in the form of a photographic product that has numerous problems. It can be distant or have low-contrast so that their signatures are challenging to recognize, especially detection of persons in surveillance systems for dark-environments. This paper contributes to proving the deep learning method capable of detecting night-person (NP) with high precision and recall in the dark without image enhancement, by using ordinary cameras which operate on day-night or visible-near infrared spectrum runs on embedded systems. For that, an infrared-cut filter mechanical shutter is designed to block for the day or deliver infrared light for the night. The NP signatures are illuminated by an external infrared light source, providing three-channel high-resolution images. The distance of a NP from the camera becomes a decisive successful detection. The external infrared light source makes objects under or overexposed affecting the object being recognized. The validation with thoroughly new data of the NP constantly provides high precision and recall.
Transfer learning scenarios on deep learning for ultrasoundbased image segmentation
Authors: Bani Unggul Didik , Iriawan Nur , Kuswanto Heri
Published: July 2024

Deep learning coupled with transfer learning, which involves reusing a pretrained model's network structure and parameter values, offers a rapid and accurate solution for image segmentation. Differing approaches exist in updating transferred parameters during training. In some studies, parameters remain frozen or untrainable (referred to as TL-S1), while in others, they act as trainable initial values updated from the first iteration (TL-S2). We introduce a new state-of-the-art transfer learning scenario (TL-S3), where parameters initially remain unchanged and update only after a specified cutoff time. Our research focuses on comparing the performance of these scenarios, a dimension yet unexplored in the literature. We simulate on three architectures (Dense-UNet-121, Dense-UNet-169, and Dense-UNet-201) using an ultrasound-based dataset with the left ventricular wall as the region of interest. The results reveal that the TL-S3 consistently outperforms the previous state-of-the-art scenarios, i.e., TL-S1 and TL-S2, achieving correct classification ratios (CCR) above 0.99 during training with noticeable performance spikes post-cutoff. Notably, two out of three top-performing models in the validation data also originate from TL-S3. Finally, the best model is the Dense-UNet-121 with TL-S3 and a 20% cutoff. It achieves the highest CCR for training 0.9950, validation 0.9699, and testing data 0.9695, confirming its excellence.
Optimized feature selection approaches for accident classification to enhance road safety
Authors: Sobhana Mummaneni , Venkatesh Mendu Gnana Siva Sai , Vemulapalli Nihitha , Kumar Chintakayala Kushal
Published: July 2024

In the modern era, the issue of road accidents has become an increasingly critical global concern, requiring urgent attention and innovative solutions. This investigation has compiled an extensive dataset of 10,356 accident occurrences that occurred between the years 2018 and 2022 in Ernakulam district. By utilizing advanced feature selection methodologies, such as genetic algorithm and coyote optimization, this research has identified pivotal accident determinants. The study harnesses the potential of deep learning techniques, encompassing recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), and multilayer perceptron (MLP) for classifying accidents according to severity (categorized as fatal, grievous, and severe). Eight predictive models are trained using the dataset, and the top two are ensembled. Integrating deep learning and optimization strategies, this research aims to create a robust accident classification system. The system will help in developing proactive policies that can reduce the frequency and severity of accidents in Ernakulam district. 
 
You only look once v8 for fish species identification
Authors: Rizqi Basuki Nurfadjri Akbar , Hustinawaty Hustinawaty
Published: July 2024

This research aims to test the performance of you only look once (YOLOv8) in identifying fish species in Indonesian waters. Fish image data is obtained from various sources to conduct testing. The results show that YOLOv8 is able to identify fish species with a mAP accuracy rate of 97%. These results reveal the great potential of deep learning technology in supporting the preservation of marine biodiversity as well as the development of various applications, such as fisheries monitoring, conservation, and marine-based tourism development in Indonesia. With its efficient object detection and classification capabilities, YOLOv8 can simplify and accelerate the process of identifying fish species, even on a large scale. Thus, this technology is a highly effective solution to overcome the challenges of manual fish species identification, which requires a lot of time and effort. The results of this study provide valuable insights into the potential utilization of Indonesia's natural resources in the context of scientific development, the tourism industry, and the fisheries sector, which is vital to the country's economy.
Herbal plant leaves classification for traditional medicine using convolutional neural network
Authors: Fauzi Alfharizky , Soerowirdjo Busono , Haryatmi Emy
Published: July 2024

The classification of herbal plant leaves can be implemented in agriculture and traditional medicine. Primarily, sorting leaves was done before it was processed into medicinal ingredients. Currently, the sorting was still done manually by writing it on notes. Sometimes there were errors in the selection of leaves for medicinal ingredients. Herbal plants had various forms and are very greatly. Artificial intelligence technology was needed to have fast-paced time efficiency in sorting leaves. In the field of artificial intelligence, there was a specific or detailed learning process known as deep learning. The objective of this research was to classify herbal plant leaves images by applying and combining the convolutional neural network (CNN) deep learning method with data augmentation methods without the pre-trained architecture such as MobileNet and LeNet. This technique consisted of 4 main stages such as collecting data, preprocessing or normalizing data, building a model, and evaluating. The dataset used in this research were 4 types of herbal plants that do not flower and do not bear fruit including gulma siam, piduh, sirih, and tobacco. Each class had 250 images with total dataset used in this research was 1,000 images of herbal plant leaves and divided into 2 data, namely 80% data training 20% data testing, and validation. The data was trained with the epoch of 100 for the best training. It had an accuracy score of 98.74%. Without the data augmentation process it had an accuracy score of 91.43%.
SANAS-Net: spatial attention neural architecture search for breast cancer detection
Authors: D'souza Melwin , Prabhu Gurpur Ananth , Kumara Varuna
Published: July 2024

The utilization of mammography images plays a vital role in the prompt detection and treatment of breast cancer. Breast imaging techniques aid medical professionals in assessing the dimensions, morphology, and spatial orientation of breast lesions, facilitating the differentiation between benign and malignant conditions. Breast tissue can vary widely in terms of density, composition, and structure, leading to complexities in distinguishing between benign and malignant conditions. The primary contribution of this paper is the proposal of a spatial attention-based neural architecture search network (SANAS-Net) technique that incorporates a spatial attention mechanism, enabling the model to learn and prioritize key regions within mammograms (MMs). Multi-head attention is employed within the transformer blocks to effectively capture a wide range of spatial relations and feature interactions. Global contextual information was integrated into the transformer blocks by means of introducing positional embeddings. Several practical studies have been undertaken to verify the effectiveness of our methodology in identifying fully attentive networks that exhibit good performance in distinguishing between malignant and benign breast cancer cases. The experimental study reached a test accuracy of 89.95%, which is way higher than previously proposed algorithms for mammography imagebased breast cancer detection.
Acapella-based music generation with sequential models utilizing discrete cosine transform
Authors: Saputra Julian , Prasetiadi Agi , Kresna Iqsyahiro
Published: July 2024

Making musical instruments that accompany vocals in a song depends on the mood quality and the music composer’s creativity. The model created by other researchers has restrictions that include being limited to musical instrument digital interface files and relying on recurrent neural networks (RNN) or Transformers for the recursive generation of musical notes. This research offers the world’s first model capable of automatically generating musical instruments accompanying human vocal sounds. The model we created is divided into three types of sound input: short input, combed input, and frequency sound based on the discrete cosine transform (DCT). By combining the sequential models such as Autoencoder and gated recurrent unit (GRU) models, we will evaluate the performance of the resulting model in terms of loss and creativity. The best model has a performance evaluation that resulted in an average loss of 0.02993620155. The hearing test results from the sound output produced in the frequency range 0-1,600 Hertz can be heard clearly, and the tones are quite harmonious. The model has the potential to be further developed in future research in the field of sound processing.
An improved convolutional recurrent neural network for stock price forecasting
Authors: Pham Hoang Vuong , Lam Hung Phu , Duy Le Nhat , Pham The Bao , Trinh Tan Dat
Published: July 2024

Stock price forecasting is a challenging area of research, particularly due to the complexity and unpredictability of financial markets. The accuracy of prediction models is influenced by various factors, including nonlinearity, seasonality, and economic shocks. Deep learning has demonstrated better forecasts of stock prices than traditional approaches. This study, therefore, proposed a new approach to improve forecasting system based on an end-to-end convolutional recurrent neural network (CRNN) with attention mechanism. Our approach first investigates local stock price features using 1D convolutional neural network, and then employs a bidirectional long short-term memory (Bi-LSTM) network for forecasting. This model stands out by effectively utilizing contextual data and representing the temporal character of data. The Bi-LSTM is helpful for understanding the history and future contextual information since it uncovers both past and future contexts of stock data. Furthermore, integrating attention mechanism within the CRNN represents a significant improvement. This allows our model to handle long input sequences more effectively and capture the inherent stochasticity in stock prices, which is often missed by traditional models. The effectiveness of our approach is investigated using data on 10 stock indexes from Yahoo Finance. The results show that our method outperforms ARIMA, LSTM, and conventional methods.

 
Multi-scale input reconstruction network and one-stage instance segmentation for enhancing heart defect prediction rate
Authors: Sutarno Sutarno , Nurmaini Siti , Sapitri Ade Iriani , Rachmatullah Muhammad Naufal , Tutuko Bambang , Darmawahyuni Annisa , Firdaus Firdaus , Islami Anggun , Samsuryadi Samsuryadi
Published: July 2024

Artifacts and unpredictable fetal movements can hinder clear fetal heart imaging during ultrasound scans, complicating anatomical identification. This study presents a new medical imaging approach that combines one-stage instance segmentation with ultrasound (US) video enhancement for precise fetal heart defect detection. This innovation allows real-time identification and timely medical intervention. The study acquired 100 fetal heart US videos from an Indonesian Hospital featuring cardiac septal defects, generating 1,000 frames for training, validation, and testing. Utilizing a combination of the multi-scale input reconstruction network (MIRNet) for image enhancement and YOLOv8l-seg for real-time instance segmentation, the method achieved outstanding validation results, boasting a 99.50% mAP for bounding box prediction and 98.40% for mask prediction. It delivered a remarkable real-time processing speed of 68.4 frames per second. In application to new patients, the method yielded a 65.93% mAP for bounding box prediction and 57.66% for mask prediction. This proposed approach offers a promising solution to early fetal heart defect detection using ultrasound, holding substantial potential for enhancing healthcare outcomes.
Enhancing the English natural language processing dictionary using natural language processing++
Authors: Chikkarangaiah Jayanth , Uday Adarsh , De Hilster David , Gangadhar Shobha , Shetty Jyoti
Published: July 2024

Every natural language-based project requires the use of an English dictionary. But the current English dictionaries are not updated as the English language is constantly evolving. The English dictionary used for natural language processing (NLP) projects needs to be enhanced by adding more words and phrases. This helps in improving the accuracy of NLP applications such as machine translation, performance of text analysis, recognition, and part of speech (POS) tagging. Several approaches are proposed in this direction, this paper develops and demonstrates enhancement of the English dictionary using a more versatile and robust programming language known as NLP++, a plugin to distributed big data analytics platforms such as HPCC systems. The unique features of NLP++ language is the enabler for realization of the proposed approach. This paper also discusses key NLP techniques, dictionary refinements analysis using NLP and NLP++. The results show that the proposed approach using NLP++ has significantly improved the accuracy and comprehensiveness of the English dictionary.
Automatic detection of safety requests in web and mobile applications using natural language processing techniques
Authors: Salmi Salim , Oughdir Lahcen
Published: July 2024

Web and mobile applications have become an essential part of our daily lives. However, as the usage of these applications increases, so does the potential for safety concerns. It is crucial for application developers to ensure that their applications are safe and secure for users. One way to achieve this is through the identification and processing of safety requests made by users. This research paper proposes a method for identifying safety requests made by users in web and mobile applications using natural language processing (NLP) and deep learning techniques. The approach involves training a machine learning and deep learning model on a dataset of user requests to identify and classify safety requests. The models are then integrated into the application’s code to automatically detect and respond to safety requests. A case study on a ride-sharing application showed that the proposed approach achieved high accuracy in identifying safety requests, with an F1 score of 0.85. The proposed method can be applied to vari- ous web and mobile applications to improve safety and security, and reduce the workload of manual safety request processing.
New disturbance observer-based speed estimator for induction motor
Authors: Indriawati Katherin , Pandu Wijaya Febry , Mufit Choirul
Published: July 2024

This paper discusses a novel disturbance observer designed as an estimator to determine the rotor speed of an induction motor. This observer is a solution to obtain a simple structure with a small number of compact observer gains. Furthermore, the adaptation law is no longer required to estimate induction motor speed values. This is a machine model-based computation method that uses a stationary reference frame. The nonlinearity problem is solved using an additional state vector in the observer model, which is known as an extended state observer. This approach easily and systematically determines the observer gain by applying the linear quadratic regulator (LQR) method, thereby avoiding time-consuming trial errors. The proposed observer, which was presented in both continuous and discrete forms, was tested using a sensorless V/f- controlled induction motor. The simulation results show that the proposed observer can accurately estimate all states, namely, the rotor flux and stator current; therefore, the proposed estimator provides the speed and electromagnetic torque for a wide operational range of speeds and load torques. It was also shown that the proposed observer was robust to noise and uncertainty in induction motor parameters.
Enhancing internet of things security and efficiency through advanced elliptic curve cryptography-based strategies in fog computing
Authors: Srinivasa Ravindra Krishnapura , Panduranga Rao Malode Vishwanatha
Published: July 2024

Fog computing (FC) has evolved as a significant paradigm within the internet of things (IoT) ecosystem, serving as a crucial link between edge devices and centralised cloud computing resources. This research paper investigates advanced methodologies for improving the security and efficiency of FC in the IoT domain. The primary emphasis is placed on the utilisation of elliptic curve cryptography (ECC) to accomplish these goals. This study examines the difficulties encountered in ensuring the security of IoT deployments based on FC. It also presents novel solutions based on ECC to mitigate these obstacles. Moreover, this study investigates techniques for enhancing the efficiency and allocation of resources in IoT applications within a FC environment. This study seeks to offer significant insights into the application of ECC-based techniques for enhancing the security and efficiency of FC in the context of the IoTs. These insights are derived through a combination of theoretical analysis and practical implementations. To evaluate the effectiveness of the proposed system, an analysis is conducted to examine the encryption time, decryption time, and correlation coefficients. These metrics are then compared to those of existing state-of-the-art approaches.
Optimization of opinion mining classification techniques using dragonfly algorithm
Authors: Rani Mikanshu , Singh Jaswinder
Published: July 2024

With the rapid evolution and growth of the internet, many individuals are using websites, blogs, and social media, and sharing their opinions about any product or service on online social platforms. Opinion mining (OM) is a field of analyzing opinions or reviews given by the public about services or products on online resources into positive, negative, or neutral classes. Governments, businesses, and researchers are using OM to analyze the reviews or opinions of the public. Thus, OM is helping individuals and businesses in better decision making. This paper mainly focuses on the feature extraction, performance analysis of OM classifiers and optimization using swarm intelligence (SI). Our proposed work: i) evaluates the performance of OM classification techniques after data collection, pre-processing, and feature extraction, ii) applies the dragonfly algorithm (DA) for optimization, and iii) evaluates the performance of OM classification techniques after applying DA and compares it with the observed performance of OM classifiers before optimization. The experimental results show that OM classification techniques perform better after optimization using DA in terms of precision, recall, f-score, and accuracy.
Efficient fusion of spatio-temporal saliency for frame wise saliency identification
Authors: Narasimha Sharada P , Lingareddy Sanjeev C
Published: July 2024

Video saliency detection is a rapidly growing subject that has seen very few contributions. The most common technique used nowadays is to perform frame-by-frame saliency detection. The modified Spatio-temporal fusion method presented in this paper offers a novel approach to saliency detection and mapping. It uses frame-wise overall motion color saliency as well as pixel-based consistent Spatio-temporal diffusion for its temporal uniformity. Additionally, a variety of techniques is advocated as a way to increase the saliency maps' overall accuracy and precision. The video is divided into groups of frames, and each frame temporarily goes through diffusion and integration in order to compute the color saliency mapping, as covered in the proposed method section. Then, with the aid of a permutation matrix, the inter-group frame is used to format the pixel-based saliency fusion, after which the features, or the fusion of pixel saliency and color information, direct the diffusion of the spatiotemporal saliency. The result is tested using five publicly accessible global saliency evaluation metrics, and it is determined that the proposed algorithm outperforms numerous saliency detection techniques with an improvement in accuracy margin. The robustness, dependability, adaptability, and precision are all demonstrated by the results.
Comparison and evaluation of YOLO models for vehicle detection on bicycle paths
Authors: Garcia-Pajuelo Joshue , Paiva-Peredo Ernesto Alonso
Published: July 2024

Non-permitted vehicles have taken over bicycle lanes in various Latin American cities as an alternative escape from traffic. Still, they do not foresee the risk to which they expose users of smaller vehicles, such as cyclists. Technological advancement has made researchers use deep learning (DL) to solve various problems in a city's traffic. However, no research has been found focusing on any issue of vehicles allowed or prohibited to travel on a bicycle lane. Therefore, in this article, the you only look once (YOLO) algorithm was used, taking the lightest models from the YOLOv4 to the most recent version, YOLOv8, to detect 05 classes of vehicles that transit or interfere in a bicycle lane, such as bicycles (Bi), motorcycles (Mo), electric motorcycles (ME), electric scooters (SE) and motorcycle cabs (Mt). When testing with the test images, the YOLOv8m model in 50 epochs, using a batch size of 32 and SGD optimizer, was the most optimal, obtaining F1 results with 88.00%, mAP@0:50 of 94.80% and mAP@0.50:0.95 of 76.60%, also had a training time of 1:28h using a Nvidia T4 GPU from Google Colab.