bluuebunny/crossref_metadata_2025 huggingface dataset:
Enacting Human–Robot Encounters with Theater Professionals on a Mixed Reality Stage
Authors: Rozendaal Marco C. , Vroon Jered , Bleeker Maaike
Published: July 2024

In this article, we report on methodological insights gained from a workshop in which we collaborated with theater professionals to enact situated encounters between humans and robots on a mixed reality stage combining VR with real-life interaction. We deployed the skills of theater professionals to investigate the behaviors of humans encountering robots to speculate about the kind of interactions that may result from encountering robots in supermarket settings. The mixed reality stage made it possible to adapt the robot’s morphology quickly, as well as its movement and perceptual capacities, to investigate how this together co-determines possibilities for interaction. This setup allowed us to follow the interactions simultaneously from different perspectives, including the robot’s, which provided the basis for a collective phenomenological analysis of the interactions. Our work contributes to approaches to HRI that do not work toward identifying communicative behaviors that can be universally applied but instead work toward insights that can be used to develop HRI that is emergent, and situation- and robot-specific. Furthermore, it supports a more-than-human-design approach that takes the fundamental differences between humans and robots as a starting point for the creative development of new kinds of communication and interaction.
Reflections on a Career in Pediatric Neuropathology, with a Note of Gratitude
Authors: Kinney Hannah C.
Published: July 2024

I am honored to be asked by the journal to write this personal essay about my career in pediatric neuropathology—a life of immense satisfaction, meaning, and fulfillment. My motivation to enter this discipline is highlighted, as is my decision to perform brain research in the sudden infant death syndrome, the leading cause of postneonatal infant mortality in the United States today. I also touch upon collaborations, mentoring, and experiences along the way—especially with the light microscope. I close with thoughts about the future of the discipline from my perspective as a lifelong devotee.
Development of neuronal timescales in human cortical organoids and rat hippocampus dissociated cultures
Authors: Martin-Burgos Blanca , McPherson Trevor Supan , Hammonds Ryan , Gao Richard , Muotri Alysson R. , Voytek Bradley
Published: July 2024

Neuronal timescales describe the persistence of activity within a network of neurons. Timescales were found to fluctuate with development in two model systems. In cortical organoids timescales increased, peaked, and then decreased throughout development; in rat hippocampal dissociated cultures timescales decreased over development. These distinct developmental models overlap to highlight a critical window in which timescales lengthen and contract, potentially indexing changes in the information capacity of neuronal systems.
Fatigue-related changes in intermuscular electromyographic coherence across rotator cuff and deltoid muscles in individuals with and without subacromial pain
Authors: Yu Xin Sienna , Zhu Huiying , Griffin Lisa
Published: July 2024

Through the computation of shared neural drive across glenohumeral muscles, this study reveals that individuals with subacromial pain were unable to increase shared neural drive within the rotator cuff and across the supraspinatus and deltoid muscles during neuromuscular fatigue induced by sustained isometric contraction. These deficits in common drive across the shoulder muscles likely contribute to the joint instability and pain experienced by these individuals.
Antidiabetic Activity, Phytochemical Analysis, and Acute Oral Toxicity Test of Combined Ethanolic Extract of Syzygium polyanthum and Muntingia calabura Leaves
Authors: Widodo Agustinus , Sulastri Evi , Ihwan Ihwan , Cahyadi Mohamad Hadi , Maulana Saipul , Zubair Muhammad Sulaiman
Published: July 2024

Syzygium polyanthum is known for its capacity to regulate blood glucose levels in individuals with diabetes, while Muntingia calabura leaves have a traditional history as an alternative therapy due to their antidiabetic compounds. The combination of these two plants is expected to yield more optimized antidiabetic agents. This study aims to assess the antidiabetic activity of the combined ethanolic extract of S. polyanthum and M. calabura leaves by measuring the in vitro inhibition of the α‐glucosidase enzyme and the blood glucose level in streptozotocin‐induced rats and to determine the phytochemical contents of total phenolics, total flavonoids, and quercetine as marker compounds. Acute oral toxicity test was also evaluated. Both plants were extracted by maceration using 96% ethanol. Various combinations of S. polyanthum and M. calabura leaves extracts (1 : 1, 2 : 1, 3 : 1, 1 : 3, and 1 : 2) were prepared. The in vitro test, along with the total phenolic and total flavonoid content, were measured by using UV‐Vis spectrophotometry, while quercetine levels were quantified through high‐performance liquid chromatography (HPLC). The in vivo and acute toxicity tests were performed on rats as an animal model. The findings demonstrated that the 1 : 1 combination of S. polyanthum and M. calabura leaves ethanolic extract displayed the highest enzyme inhibitory activity with IC50 value of 36.43 µg/mL. Moreover, the combination index (CI) was found <1 that indicates the synergism effect. This combination also decreases the blood glucose level in rats after 28 days of treatments without significant difference with positive control glibenclamide (p > 0.005), and it had medium lethal doses (LD50) higher than 2000 mg/kg BW. Phytochemical analysis showed that the levels of total phenolics, total flavonoids, and quercetine were 30.81% w/w, 1.37% w/w, and 3.25 mg/g, respectively. These findings suggest the potential of combined ethanolic extracts of S. polyanthum and M. calabura leaves (1 : 1) as raw materials for herbal antidiabetic medication.
Regulatory Considerations for Genome-Edited T-cell Therapies
Authors: Jadlowsky Julie K. , Chang Ju-fang , Spencer David H. , Warrington John M. , Levine Bruce L. , June Carl H. , Fraietta Joseph A. , Singh Nathan
Published: July 2024

Methods to engineer the genomes of human cells for therapeutic intervention continue to advance at a remarkable pace. Chimeric antigen receptor–engineered T lymphocytes have pioneered the way for these therapies, initially beginning with insertions of chimeric antigen receptor transgenes into T-cell genomes using classical gene therapy vectors. The broad use of clustered regularly interspaced short palindromic repeats (CRISPR)-based technologies to edit endogenous genes has now opened the door to a new era of precision medicine. To add complexity, many engineered cellular therapies under development integrate gene therapy with genome editing to introduce novel biological functions and enhance therapeutic efficacy. Here, we review the current state of scientific, translational, and regulatory oversight of gene-edited cell products.
Figure 5 from Exploration of Immune-Modulatory Effects of Amivantamab in Combination with Pembrolizumab in Lung and Head and Neck Squamous Cell Carcinoma
Authors: Lim Sun M. , Kang Seong-san , Kim Dong K. , Lee Soo-Hwan , Synn Chun-Bong , Baek Sujeong , Yang Seung M. , Han Yu J. , Kim Mi H. , Han Heekyung , Na Kwangmin , Kim Young T. , Yun Mi R. , Kim Jae H. , Byeon Youngseon , Kim Young S. , Lee Jii B. , Hong Min H. , Curtin Joshua C. , Patel Bharvin , Bergiers Isabelle , Pyo Kyoung-Ho , Cho Byoung C.
Published: July 2024

LDHA and SLC16A3 are important regulators of glycolytic pathways and were upregulated in EGFRHIGH/METHIGH tumor subcluster of HNSCC PDX (YHIM-3003). A, Correlation between expression of EGFR and biomarkers based on TCGA database. LDHA and SLC16A3 expression was positively correlated with expression of EGFR in both HNSCC and LUSC. B, Expression of LDHA and SLC16A3 was significantly increased in EGFRHIGH/METHIGH tumor subcluster (EMHIGH) of HNSCC PDX (left). Additionally, expression of LDHA and SLC16A3 increased in pembrolizumab treated group. C, Regulators of glycolysis (HK2, GPI, ALDOA, PGK1, PGAM1, ENO1, ENO2) comparatively increased in the EMHIGH tumor subcluster (left) and pembrolizumab treated group (right). D, Regulators of hypoxia (HIF1A, HDAC1, KDM1A, KDM2A) and downstream signaling markers (CA9, VEGFA, TWIST1) increased in the EMHIGH tumor subcluster (left) and pembrolizumab treated group (right). E, Total protein and surface expression of EGFR and MET demonstrated strong correlation in HNSCC and LUSC cell lines (left). H1703, LUSC human cancer cell, was treated with IFNγ for 24 hours to mimic the physiological response of pembrolizumab in the TME (right). Induction of IFNγ, though not significant, upregulated the expression of pEGFR, while pMET level significantly increased (P < 0.01). F, Protein expression of EGFR/p-EGFR, MET/p-MET, MCT4 (SLC16A3), and LDHA after 72 hours of amivantamab at 10 mg/mL in H1703. IFNγ was treated at 100 ng/mL for 24 hours (left). Amivantamab reduced the expression of these genes under IFNγ-induced conditions (right).
Figure 4 from Exploration of Immune-Modulatory Effects of Amivantamab in Combination with Pembrolizumab in Lung and Head and Neck Squamous Cell Carcinoma
Authors: Lim Sun M. , Kang Seong-san , Kim Dong K. , Lee Soo-Hwan , Synn Chun-Bong , Baek Sujeong , Yang Seung M. , Han Yu J. , Kim Mi H. , Han Heekyung , Na Kwangmin , Kim Young T. , Yun Mi R. , Kim Jae H. , Byeon Youngseon , Kim Young S. , Lee Jii B. , Hong Min H. , Curtin Joshua C. , Patel Bharvin , Bergiers Isabelle , Pyo Kyoung-Ho , Cho Byoung C.
Published: July 2024

Single-cell RNA sequencing analysis of EGFRHIGH/METHIGH and EGFRLOW/METLOW subclusters in the tumor of humanized HNSCC (YHIM-3003) PDX mice showing relative increase in EGFR and MET expressing subcluster after treatment of pembrolizumab (n = 5 in each treatment group). Top 50 genes were analyzed by log2 fold change (FC) against P-values and immune related genes were divided into three categories as following: immunomodulation, metastasis potential and cancer progression (drug resistance/cancer stemness). A, Heatmap of tumor indicating a region of tumor subcluster with elevated expression of both EGFR and MET. B, Density plot illustrating EGFRHIGH/METHIGH and EGFRLOW/METLOW subclusters in treatment groups. Expression of EGFR and MET was relatively higher in the pembrolizumab treated group compared to the other treatment groups (C). D, Tumor subcluster with elevated dual expression of EGFR and MET (EMHIGH) was define and analyzed for DEGs. E, EGFR and MET in EMHIGH and EMLOW, showing increased expression of both markers in EMHIGH tumor cluster. F, DEG analysis of the top genes in the EGFRHIGH/METHIGH tumor subcluster showing genes related to immunomodulation, tumor metastasis, drug resistance and cancer stemness in volcano plot. G, Multiplex IHC of the tumor tissue showing reduced expression of EGFR in tumor treated with combination treatment. EGFR+ site, colored green, represents tumor regions that were stained positive for EGFR and does not directly translate to the level of expression. Tumor with damaged or indistinct regions of tumor nest was treated as an outlier and was removed from each group. H, Fluorescence intensity of EGFR in randomized region of interest sites converted into average H-scores. H-scores ranged from 0 (H-0, blue) to 3 (H-3, red), with H-3 representing the highest intensity as shown in the top. Average H-score of EGFR expression in pembrolizumab treated mice was significantly higher than the other groups (P < 0.01).
Learning methodologies towards leveraging security resiliency in internet-of-things environment
Authors: Somanath Sowmya , Ajay Usha Banavikal
Published: July 2024

The evolution of artificial intelligence (AI) has faciliated a significant contribution of machine learning and deep learning in order to improvise the security features of large internet-of-things (IoT) environment. Since last decade there has been different variants of learning-based methodologies towards leveraging security improvements among communication in IoT devices; however, it is yet to know the strength and weakness of them. Hence, this paper presents a review of security methodologies adopted in machine learning and deep learning-based techniques in IoT to understand the degree of resiliency and effectiveness of these techniques. The paper further contributes towards highlighting the current methodologies with respect to benefits and limiting factors along with exclusive highlights of research trends while the research gap explored assists in offering these insights. The distinct findings of the study assist in paving the work direction in future by harnessing better form of learning scheme.
Novel computational intelligence-based model for effective traffic management in intelligent transportation system
Authors: Appaji Impana , Pandian Raviraj
Published: July 2024

The evolution of intelligent transportation system (ITS) is essentially meant for upgrading the driving experience with more safety and accessibility of various analytical information from its extensive network. However, a significant gap is observed that doesn't cater up the complete demands of ITS. It has been also noted that computational intelligence (CI) based approach is slowly gaining pace in solving the transport related problems in ITS as compared to its other counterpart existing methodologies like artificial intelligence. The proposed manuscript introduces a novel computational framework towards assisting in relaying routing and navigational services using CI-based approach. A design of novel navigational controller unit is presented for global ITS scenario towards yielding an optimal decision of routing. The CI-based approach is implemented by integrating fuzzy process with evolutionary searching, learning and probability theory in most simplified form. The study also introduces a novel concept of relaying decision as feedback from navigational controller unit to specific vehicular node discretely unlike existing traffic controller system with an agenda to offer faster and effective clearance of queued vehicular nodes from target area. The study outcome shows higher consistency in its relay with better performance from existing study model in ITS.
Scalability and performance of decision tree for cardiovascular disease prediction
Authors: Admassu Assegie Tsehay , Kumar Napa Komal , Thulasi Thiyagu , Kalyan Kumar Angati , Thiruvarasu Vasantha Priya Maran Jeyanthiran , Dhamodaran Vigneswari
Published: July 2024

As one of the most common types of disease, cardiovascular disease is a serious health concern worldwide. Early detection is crucial for successful treatment and improved survival rates. The decision tree is a robust classifier for predicting the risk of cardiovascular disease and getting insights that would assist in making clinical decisions. However, selecting a better model for cardiovascular disease could be challenging due to scalability issues. Hence, this study examines the scalability and performance of decision trees for cardiovascular disease prediction. The study evaluated the performance of a decision tree for predicting cardiovascular disease. The performance evaluation was carried out by employing a confusion matrix, cross-validation score, model complexity, and training score for varying sizes of training samples. The experiment depicted that, the decision tree model was 88.8% accurate in predicting the presence or absence of cardiovascular disease. Therefore, the implementation of the decision tree is beneficial for the prediction and early detection of heart disease events in patients.
Recommendation method for selecting the rice seeds based on group decision support system
Authors: Hamdani Hamdani , Wati Masna , Suprihanto Didit , Salsabila Nur Maya , Septiarini Anindita , Nurmadewi Dita , Mawardi Viny Christanti
Published: July 2024

In 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.
Artificial intelligence-enabled profiling of overlapping retinal disease distribution for ocular diagnosis
Authors: Sundararajan Sridhevi , Ramachandran Harikrishnan , Gupta Harshitha
Published: July 2024

Eyesight, an invaluable gift profoundly impacts our daily lives. In a rapidly evolving healthcare landscape, the preservation and enhancement of ocular health stand as critical objectives. This research endeavors to analyze the two retinal fundus multi-disease image datasets (RFMiD) one containing 3200 images and the other containing 860 fundus images. The primary objective of this study is to scrutinize these datasets, discern variations in the frequency of labeled diseases within and across them, and explore common combinations of labels. These findings hold important implications for the field of retinal image analysis, as they provide valuable insights into the distribution and co-occurrence of defects.
Fuzzified input data tuning for agriculture commodities price prediction
Authors: Hegde Girish , Hulipalled Vishwanath R. , Simha Jay B.
Published: July 2024

The quality of the input data will typically affect the prediction accuracy. Preprocessing of data is commonly referred to as input data tuning. Tuning the input data is critical for projecting commodity prices. Anomalies or outliers are unavoidable in historical price data. To increase prediction and forecasting accuracy, it is necessary to find and correct outliers before training the prediction model. To correct the anomaly and increase prediction accuracy, the Fuzzified Input Data Tuning and Prediction algorithm proposed in this study. The identified outliers are corrected using the relevant fuzzy set value in this method. With outlier corrected data, we used Long Short-Term Memory and Seasonal Autoregressive Integrated Moving Average to anticipate tomato prices in Karnataka state. The result of the proposed algorithm is compared with the Sliding Window anomalies correction model, and without disposing of the outliers. The suggested algorithm, with 37.89%, performed better than Sliding Window with 40.08% and 43.11% Mean Absolute Percentage Error, respectively, and without outlier correction. The sensitivity analysis shows that the performance of the model is unaffected by the forecasting horizon. Finally, comparitive analysis peformed with previous research work, and the proposed model performed better.
Framework towards critical event classification of bipolar disorder in internet of things ecosystem
Authors: Kunjali Ajeeth Yashaswini , Kasaragod Madhura
Published: July 2024

Bipolar disorder is quite a challenging mental illness which encounters substantial degree of challenges in confirmed diagnosis irrespective of modernized increasing pace of development in medical science. With the evolving standards of automation in healthcare section integrated with advanced technology, it is imperative to anticipate a reliable on-line diagnosis of mental illness for a given scenario of internet of things (IoT). Review of existing methodologies showcases a wide gap between enormous research work towards identification of bipolar disorder and only few studies towards on-line diagnosis considering patients residing in smart city. Therefore, the proposed scheme introduces a novel computational framework of an underlying architecture of an IoT that not only facilities an effective and simplified transmission of multimodal data autonomously from the patient forwarded to clinical analytical unit but also perform a multitier classification using deep neural network. The study outcome exhibits proposed scheme to offer better data transmission with higher accuracy performance in contrast to existing prevalent schemes.
TMS320F28379D microcontroller for speed control of permanent magnet direct current motor
Authors: Chalardsakul Tanawat , Piliyasilpa Chotnarin , Sukontanakarn Viroch
Published: July 2024

This paper aims to study the behavior of the proportional integral derivative (PID) and the fuzzy-based tuning PI-D controller for speed control of a permanent magnet direct current (PMDC) motor. The proposed method used a fuzzy-based tuning PI-D controller with a MATLAB/Simulink program to design and real-time implement a TMS320F28379D microcontroller for speed control of a PMDC motor. The performance of the study designed fuzzy-based tuning PI-D and PID controllers is compared and investigated. The fuzzy logic controller applies the controlling voltage based on motor speed errors. Finally, the result shows the fuzzy-based tuning PI-D controller approach has a minimum overshoot, and minimum transient and steady state parameters compared to the PID controller to control the speed of the motor. The PID controllers have poorer performance due to the non-linear features of the PMDC motor.
A hybrid hue saturation lightness, gray level co-occurrence matrix, and k-nearest neighbour for palm-sugar classification
Authors: Jumarlis Mila , Mulyadi Ida , Mirfan Mirfan , Imawati Irmawati , Mardiah Mardiah , Faisal Muhammmad , Anisa Hairin
Published: July 2024

In recent years, there has been an increasing demand for high-quality raw materials driven by consumers and the food industry. This study aims to build a model to predict the type of palm sugar using a hybrid method of hue-saturation-lightness (HSL), gray level co-occurrence matrix (GLCM), and K-nearest neighbor (KNN). The price of palm sugar is determined based on the type and ingredients used. However, due to the lack of public knowledge in distinguishing the types of palm sugar, there is the potential for price manipulation that can harm the community. The accuracy rate of 97.6% of the palm sugar type prediction results shows that the model that was built has worked very well. The results have practical implications, such as developing automated systems to classify palm species in specific industries to benefit economics and operational efficiency. Future research directions may explore the integration of advanced machine-learning techniques and real-time image processing for further improving classification performance and scalability in industrial applications.
The prediction of thermal sensation in building using support vector machine and extreme gradient boosting
Authors: Effendy Nazrul , Abiyu Fadhilah Muhammad Zhafran , Kraton Danang Wahyu , Abrar Haidar Alghazian
Published: July 2024

The building has great potential for energy savings as one of the locations that humans often occupy. In addition to energy efficiency, humans must consider environmental sustainability and the comfort of the building's occupants. Conditioning of indoor air quality, including those related to thermal comfort, continues to be pursued to be more economical, one of which is to utilize the prediction of occupants' thermal sensations. The prediction results can be utilized to adjust room air conditions more economically. This paper proposes using extreme gradient boosting (XGBoost) and support vector machine (SVM) to predict the thermal sensation in the building. The built environment parameters are preprocessed, and the thermal sensation is predicted by intelligent systems. The ten variables that most influence the level of accuracy of this thermal sensation prediction system are thermal preference vote, indoor operative temperature, Griffith's neutral temperature, indoor globe temperature, mean radiant temperature, Indoor air temperature, predicted mean vote, and outdoor mean temperature. SVM with four features, XGBoost and XGBoost with hyperparameter tuning, achieve an accuracy of 99.45%, 97.81%, and 98.08%, respectively. Regarding computational complexity, training an SVM system with the same number of features requires a shorter time than XGBoost training. The same thing also happened with the test of the SVM system, which required a shorter time compared to the time for the examination of the XGBoost system.
Multi quadrotors coverage optimization using reinforcement learning with negotiation
Authors: Bonaventura Wijaya Glenn , Agustinus Tamba Tua
Published: July 2024

This paper proposes an optimization scheme to maximize the area coverage of multiple quadrotor unmanned aerial vehicles that are deployed to monitor an operational area/space. Each quadrotor initially performs a single agent reinforcement learning to determine target points with optimal coverage area. Whenever each quadrotor encounters the others within a predetermined negotiation region that is defined by an inter-agent distance threshold, it will activate a multiagent reinforcement learning with action negotiation algorithm and coordinate its movement policies to maximize the total coverage area and avoids inter-agent coverage overlaps. Results of simulation evaluations are shown to illustrate the performance of the proposed learning-based coverage optimization method.
A mobile-optimized convolutional neural network approach for real-time batik pattern recognition
Authors: Rosalina Rosalina , Sahuri Genta , Desriva Hana
Published: July 2024

This research focuses on preserving and sharing knowledge about Indonesian batik, a blend of art and technology symbolizing the nation's creativity. To address declining awareness of batik types, a mobile application is introduced for real-time recognition and classification of batik motifs. The goal is to maintain appreciation and understanding of this cultural heritage. Using the EfficientNet convolutional neural network (CNN) architecture, the study enhances model accuracy with effective scaling. A dataset of 1350 images representing 15 batik types supports robust model training and evaluation. Results demonstrate successful implementation, yielding an Android app capable of deep learning-based real-time recognition with an 83% accuracy rate. This innovation aims to empower users to identify and appreciate distinct batik types, ensuring cultural preservation for current and future generations.
Age prediction from COVID-19 blood test for ensuring robust artificial intelligence
Authors: Nurul Qomariyah Nunung , Kazako Dimitar
Published: July 2024

With the advancement of artificial intelligence (AI) nowadays, the world is experiencing conveniences in automating some complex and tedious tasks, such as analysing large data and predicting the future by mimicking human expertise. AI has also shown promise for mitigating future crisis, such as pandemic. Since the beginning of the COVID-19, several AI models have been published by the researchers to help the healthcare to fight in this situation. However, before deploying the model, one needs to ensure that the model is robust and safe to learn from the real environment, especially in medical domain, where the uncertainty and incomplete information are not unusual. In the effort of providing robust AI, we proposed to use patient age as one of the feasible feature for ensuring vigorous AI models from electronic health record. We conducted several experiment with 28 blood test items and radiologist report from 1,000 COVID-19 patients. Our result shows that with the predicted age as an additional feature in mortality classification task, the model is significantly improved when compared to adding the actual age. We also reported our findings regarding the predicted age in the dataset.
New insight in cervical cancer diagnosis using convolution neural network architecture
Authors: Khozaimi Ach , Firdaus Mahmudy Wayan
Published: July 2024

The Pap smear is a screening method for early cervical cancer diagnosis. The selection of the right optimizer in the convolutional neural network (CNN) model is key to the success of the CNN in image classification, including the classification of cervical cancer Pap smear images. In this study, stochastic gradient descent (SGD), root mean square propagation (RMSprop), Adam, AdaGrad, AdaDelta, Adamax, and Nadam optimizers were used to classify cervical cancer Pap smear images from the SipakMed dataset. Resnet-18, Resnet-34, and VGG-16 are the CNN architectures used in this study, and each architecture uses a transfer-learning model. Based on the test results, we conclude that the transfer learning model performs better on all CNNs and optimization techniques and that in the transfer learning model, the optimization has little influence on the training of the model. Adamax, with accuracy values of 72.8% and 66.8%, had the best accuracy for the VGG-16 and Resnet-18 architectures, respectively. Resnet-34 had 54.0%. This is 0.034% lower than Nadam. Overall, Adamax is a suitable optimizer for CNN in cervical cancer classification on Resnet-18, Resnet-34, and VGG-16 architectures. This study provides new insights into the configuration of CNN models for Pap smear image analysis.
Hybrid embedded and filter feature selection methods in big-dimension mammary cancer and prostatic cancer data
Authors: Md Noh Siti Sarah , Ibrahim Nurain , M. Mansor Mahayaudin , Md Ghani Nor Azura , Yusoff Marina
Published: July 2024

The feature selection method enhances machine learning performance by enhancing learning precision. Determining the optimal feature selection method for a given machine learning task involving big-dimension data is crucial. Therefore, the purpose of this study is to make a comparison of feature selection methods highlighting several filters (information gain, chi-square, ReliefF) and embedded (Lasso, Ridge) hybrid with logistic regression (LR). A sample size of n=100, 75 is chosen randomly, and the reduction features d=50, 22, and 10 are applied. The procedure for feature reduction makes use of the entire sample sizes. Each sample size's results are compared, including tests with no feature selection process. The results indicate that LR+ReliefF is the best method for mammary cancer data, whereas LR+IG is the best for prostatic cancer data, making the filter more suitable than embedded for big-dimension data. This study revealed that the sample's features and size influence the most effective method for selecting features from big-dimension data. Therefore, it provides insight into the most effective methods for particular features and sample sizes in high-dimensional data.
Machine learning for potential anti-cancer discovery from black sea cucumbers
Authors: Fahrury Romdendine Muhammad , Fatriani Rizka , Ananta Kusuma Wisnu , Annisa Annisa , Nurilmala Mala
Published: July 2024

Despite being an abundant marine organism in Indonesia, black sea cucumbers (Holothuria atra) is still underutilised due to its slightly bitter taste. This study aims to identify potential anti-cancer compounds from black sea cucumbers using machine learning (ML) to perform drug discovery. ML models were used to predict interactions between compounds from the organism with cancer-related proteins. Following prediction, all compounds were computationally validated through molecular docking. The validated compounds were then screened using absorption, distribution, metabolism, excretion, and toxicity (ADMET) Lab 2.0 to assess their druglike properties. The results showed that ML predicted seven out of 86 compounds were interacted with cancer-related proteins. Computational validation from the results showed that four out of seven compounds demonstrated stable interaction with proteins where only one compound meet the criteria of drug-like compound. The framework of ML and computational validation highlighted in this study shows a great promise in the future of drug discovery specifically for marine organisms. Since computational method only works in prediction realms, wet lab validation and clinical trials are imperative before the drug candidate can be produced as actual anti-cancer drug.
Lip reading using deep learning in Turkish language
Authors: Pourmousa Hadi , Özen Üstün
Published: July 2024

Computer vision is one of the most important areas of artificial intelligence and lip reading is one of the most important areas of computer vision. Lip-reading, which is more important in noisy environments or where there is no sound flow, is one of the working areas that can help the hearing-impaired people. There is no dataset in Turkish for lip reading, which there are different datasets at alphabet, word, and sentence level in different languages. The dataset of this study was created by the author and video data were collected from 72 people for 71 words. Audio streams were removed from the collected videos and a dataset was created using only images. Due to the small size of the dataset, the data was replicated with the Camtasia application. After the model of the research was designed and trained, the model was tested on adjectives, nouns, and verbs dataset and success rates of 71.8%, 71.88%, and 79.69% were obtained, respectively.
Elevating sentiment analysis with VGG-16's facial expression insights
Authors: Mehta Pradnya , Chhabada Dev , Wankhade Renuka , Patel Dhimahi , Gote Anirudh , Yenkikar Anuradha , Agrawal Poorva , Kaur Gagandeep
Published: July 2024

In today's data-driven world, the ability to analyze emotional responses is essential. The pressing necessity that drives this study is to revolutionize the field of sentiment analysis by extracting the hidden information from people's facial expressions. It examines people's preferences, worries, and pleasure, revealing their views on many topics. Beyond text-based sentiment analysis, this research adds facial expression-based sentiment analysis into existing systems for tailored recommendations and mental health monitoring. The system emphasizes visual stimuli's emotional influence to improve decision-making, content adaptability, and user experiences. The implementation involves transfer learning with the pre-trained VGG-16 model, which enhances ability to discern intricate emotional cues from facial expressions. Convolutional Neural Network (CNN) and contextual analysis allow the model to understand users' emotions and provide insights into their thoughts, feelings, and behaviours. To improve emotion recognition reliability and reactivity, this study examines Random Forest, Support Vector Machine (SVM), and CNN methodologies. The VGG-16 CNN model outperforms over SVM and Random Forest classifiers with accuracy of 95%. This study highlights facial expression-based sentiment analysis.
BERT-based models for classifying multi-dialect Arabic texts
Authors: Fouadi Hassan , El Moubtahij Hicham , Lamtougui Hicham , Yahyaouy Ali
Published: July 2024

The area of natural language processing (NLP) is presently a rapidly developing field characterized by innovation and research. Despite this progress, several dialects of Arabic (DA) are classified as low-resource languages, making it challenging for NLP systems to process DA data. One approach to address this issue is to train NLP models on social media data sets containing DA texts. Therefore, these open-access social media datasets, as outlined in our paper, can serve as a valuable resource for developers and researchers involved in the processing of DA.To create our multilingual corpus, we gathered data from various datasets containing different versions of DA. These datasets will be used to classify texts in terms of sentiment classification, topic classification, and dialect identification. Our study contributes to the automated analysis of the classification of Arabic dialects. We aim to investigate and assess various machine learning and deep learning techniques, with a specific focus on utilizing the BERT model. The results of our experiments on our datasets show that DarijaBERT and DziriBERT trained on a similar DA outperform traditional machine learning methods and previous more general pre-trained models that were trained on multiple dialects or languages.
Reinforcement of low-resource language translation with neural machine translation and backtranslation synergies
Authors: Prasada Padma , Panduranga Rao Malode Vishwanatha
Published: July 2024

This research investigates challenges and advancements in neural machine translation (NMT), specifically targeting English-to-Kannada translation. Emphasizing the scarcity of data and linguistic complexity in low-resource languages (LRL), particularly Kannada, the study underscores the need for specialized techniques. Starting with exploration of Kannada's historical and cultural significance, the paper highlights critical importance of linguistic comprehension. The primary objective is to develop robust NMT models for precise and contextually relevant translations in low-resource scenarios. The novelty of this research lies in its innovative approach to Kannada NMT challenges, incorporating comprehensive examination of historical and cultural context to establish strong linguistic foundation. Motivated by the urgency to address translation needs in LRL, the paper proposes novel strategies, advocating notably for backtranslation to generate synthetic parallel corpora. Rigorous testing, including bilingual evaluation understudy (BLEU) score assessments, evaluates effectiveness of these proposed approaches. Beyond assessing backtranslation, the study explores challenges faced by Kannada NMT in handling dialectical and spelling variations. The research reports substantial 83-percentage-point average increase in BLEU scores, contingent on aligning unique Kannada terms with the same domain as existing occurrences. This study contributes significantly to Kannada natural language processing by offering novel insights into NMT intricacies and providing practical solutions for enhancing translation accuracy in low-resource settings.
Recommendation mobile antivirus for Android smartphones based on malware detection
Authors: Saputra Hendra , Zahra Amalia , Faldi Faldi , Fadzlul Rahman Ferry , Harits Sayekti , Joko Pranoto Wawan , Rahman Fathur
Published: July 2024

The proliferation of smartphone malware attacks due to a lack of vigilance in app selection raises serious concerns. Built-in smartphone security features often must be improved to protect devices from these threats. Although numerous articles recommend top-tier antivirus solutions, there need to be more reliable data sources that raise suspicions about undisclosed promotional motives. This research endeavors to establish a ranking of antivirus efficacy to provide optimal recommendations for Android smartphone users. The research methodology entails a meticulous comparison of malware detection and labeling outcomes between various antivirus programs within Virustotal and the labeling system employed by the Euphony application. The comparative results are categorized into three groups: antivirus solutions proficient in identifying specific malware types, those detecting malware presence without categorization, and antivirus software failing to detect malware effectively. The experimental findings present the five leading antivirus solutions, ranked from the highest to lowest scores, as Ikarus, Fortinet, ESET-NOD32, Avast-Mobile, and SymantecMobileInsight. Based on the comprehensive assessment conducted in this study, these solutions are recommended as the top antivirus choices. These recommendations are poised to significantly aid users in selecting the most suitable antivirus protection for their Android smartphones.
Improved unmanned aerial vehicle control for efficient obstacle detection and data protection
Authors: Moldamurat Khuralay , Atanov Sabyrzhan , Akhmetov Kairat , Bakyt Makhabbat , Belgibekov Niyaz , Zhumabayeva Assel , Shabayev Yuriy
Published: July 2024

The article centers on the research objectives and tasks associated with developing a swarm control system for unmanned aerial vehicles (UAVs) utilizing artificial intelligence (AI). A comprehensive literature review was undertaken to assess the effectiveness of the "swarm" method in UAV management and identify key challenges in this domain. Swarm algorithms were implemented in the MATLAB/Simulink environment for modeling and simulation purposes. The study successfully instantiated and simulated a UAV swarm control system adhering to fundamental principles and laws. Each UAV operates autonomously, following target-swarm principles inspired by the collective behavior of bees and ants. The collective movement and behavior of the swarm are controlled by an AI-based program. The system demonstrated effective obstacle detection and avoidance through computer simulations. Results obtained highlight key features contributing to success, including decentralized autonomy, collective intelligence, UAV coordination, scalability, and flexibility. The deployment of a local radio communication system in UAV swarm control and remote object monitoring is also discussed. The research findings hold practical significance as they enable the effective execution of complex tasks and have potential applications in various fields.
Hybrid software defined network-based deep learning framework for enhancing internet of medical things cybersecurity
Authors: Rbah Yahya , Mahfoudi Mohammed , Balboul Younes , Chetioui Kaouthar , Fattah Mohammed , Mazer Said , Elbekkali Moulhime , Bernoussi Benaissa
Published: July 2024

The risk of cyber-attacks has increased significantly with the rapid development of the Internet of Medical Things (IoMT). The proliferation of IoMT devices in healthcare facilities has made conventional intrusion detection approaches challenging to employ. Our study proposes a novel hybrid framework leveraging Software Defined Network (SDN) controllers and deep learning techniques, specifically Convolutional Neural Networks (CNN) and Bidirectional Long-Term Memory (Bi-LSTM), to address these challenges. Our framework introduces a unique combination of SDN and deep learning, allowing for dynamic and efficient management of IoMT security. The integration of CNN and Bi-LSTM enables the system to handle diverse data types encountered in IoMT, offering a comprehensive approach to threat detection. Unlike traditional methods, our hybrid solution adapts seamlessly to the evolving threat landscape of healthcare IoT systems. The urgency of our research is affirmed by the critical need to fortify IoMT security amid escalating cyber threats. The conventional methods struggle to cope with the complex nature of IoMT networks, making our exploration of a hybrid SDN-based deep learning framework imperative. With a background in cybersecurity and a dedicated focus on healthcare IoT, we recognize the urgency to develop a solution that not only enhances detection accuracy but also ensures real-time responsiveness in healthcare settings. The proposed method has been validated using the “IoT-Healthcare security” dataset, revealing its efficacy in detecting numerous frequent threats and outperforming current state-of-the-art techniques in terms of high detection accuracy of 99.97% and less than 1.8 (s) in terms of speed efficiency.
Artificial intelligence-based learning model to improve the talents of higher education students towards the digitalization era
Authors: Wahjusaputri Sintha , Bunyamin Bunyamin , Indah Nastiti Tashia , Sopandi Evi , Subagyo Tatang , Veritawati Ionia
Published: July 2024

Artificial intelligence (AI) technology is a hallmark of the 4.0 revolution. The two main issues in Indonesia are infrastructure that needs to be equipped with technology and intelligence-based curriculum integrated with business and industry programs, and lecturers as educators who do not want to use and develop AI technology in applying guided learning models. This research aims to create a learning model based on AI that will help college students develop their talents while maintaining the Pancasila principles in the age of digitization. This study contains four stages: data collection, data analysis, research analysis outcomes, and validation of research analysis results. This research developed an AI-based learning model for use in higher education consisting of four dimensions: input, process, output, and outcome. The input dimension includes components such as students, lecturers, organizations, and infrastructure ready to adopt AI-based learning models. The process dimension consists of the elements that influence the operation of the AI-based learning model system and the functionality provided by the learning model. The output dimension includes characteristics that may be directly measured and process feedback. Finally, the outcome comprises the predicted outputs from the AI-based learning model.
Comparing emotion classification: machine learning algorithms and hybrid model with support vector machines
Authors: Hamid Zghair Ghufran , Shaheed Al-Azzawi Dheyaa
Published: July 2024

Recently, the use of artificial intelligence techniques has become widespread, having been adopted in brain-computer interfaces (BCIs) with electroencephalograms (EEGs). BCIs allow direct communication between a person's brain and a computer, and have various uses ranging from assistive technology to neuroscientific study. This paper provides an introductory overview of BCIs and EEG. We adopted the use of machine learning (ML) algorithms, including K-nearest neighbors (KNN), logistic regression, decision trees, random forests, and support vector machine (SVM). Additionally, we proposed a hybrid model of deep learning (DL) and ML by combining convolutional neural networks (CNNs) and SVMs. Our achieved 98% accuracy. The goal is to classify EEG signals into three emotional states: happy, normal, and sad. The study aims to achieve a comprehensive understanding of the effectiveness of these algorithms in accurately classifying emotional states based on EEG data. By comparing the performance of traditional ML methods and the proposed hybrid model, we seek to identify the most robust and accurate approach to sentiment classification.
Apple fruits categorizing based on deep convolutional neural network techniques
Authors: Hussain Nashaat , Zaki Gihan , Hassan Mohamed
Published: July 2024

For a variety of reasons, including the high degree of similarity between varieties of the same type of fruit, the requirement to train the technique on a large amount of data, and the type and number of features suitable for application, the use of computer vision techniques in the classification of fruits still faces many challenges. Additionally, the technique's effectiveness and speed both need to be improved. Deep conventional neural network (DCNN) approaches were required for all of these reasons. A proposed CNN model is described in this work. The suggested methodology is intended to quickly and accurately categorize thirteen groups of apple fruits. The proposed technique was based on training and testing the model on a maximum number of images of apple fruits, by increasing the number of database images tenfold, after augmentation was performed on the images. The technology also relied on good tuning of the hyperparameters. To further ensure the efficiency of training, validation was performed on 20% of the database. All results that demonstrate the high efficiency of the proposed model were reviewed. The results of the proposal were compared with the results of four related techniques. The results showed the great advantage of the proposed technology at all levels.