Reviewerly/exHarmony huggingface dataset:
HT-RCM: Hashimoto's Thyroiditis Ultrasound Image Classification Model Based on Res-FCT and Res-CAM

Authors:
raw_author_name: Wenchao Jiang
raw_affiliation_string: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China
raw_author_name: Kang Chen
raw_affiliation_string: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China
raw_author_name: Zhipeng Liang
raw_affiliation_string: School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China
raw_author_name: Guanghui Yue
raw_affiliation_string: National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Health Science Center, School of Biomedical Engineering, Shenzhen University, Shenzhen, China
raw_author_name: Zhiming Zhao
raw_affiliation_string: Multiscale Networked Systems research group, University of Amsterdam, Amsterdam, The Netherlands
raw_author_name: Wei Song
raw_affiliation_string: Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, Guangzhou, Guangdong Province, China
raw_author_name: Ling Zhao
raw_affiliation_string: Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong Province, Guangzhou, Guangdong Province, China

Abstract: The early lesions of Hashimoto's thyroiditis are inconspicuous, and the ultrasonic features of these early lesions are indistinguishable from other thyroid diseases. This paper proposes a Hashimoto Thyroiditis ultrasound image classification model HT-RCM which consists of a Residual Full Convolution Transformer (Res-FCT) model and a Residual Channel Attention Module (Res-CAM). To collect the low-order information caused by hypoechoic signals accurately, the residual connection is injected between FCTs to form Res-FCT which helps HT-RCM superimpose the low-order input information and high-order output information together. Res-FCT can make HT-RCM focus more on hypoechoic information while avoiding gradient dispersion. The initial feature map is inserted into Res-FCT again through a down-sampling component, which further helps HT-RCM exact multi-level original semantic information in the ultrasound image. Res-CAM is constructed by implementing a residual connection between a channel attention module and a convolution layer. Res-CAM can effectively increase the weights of the lesion channels while suppressing the weights of the noise channels, which makes HT-RCM focus more on the lesion regions. The experimental results on our collected dataset show that HT-RCM outperforms the mainstream models and obtains state-of-the-art performance in HT ultrasound image classification.
Breast cancer detection using ensemble of convolutional neural networks

Authors:
raw_author_name: Swati Nadkarni
raw_affiliation_string: St. Francis Institute of Technology
raw_author_name: Kevin Noronha
raw_affiliation_string: St. Francis Institute of Technology

Abstract: Early detection leading to timely treatment in the initial stages of cancer may decrease the breast cancer death rate. We propose deep learning techniques along with image processing for the detection of tumors. The availability of online datasets and advances in graphical processing units (GPU) have promoted the application of deep learning models for the detection of breast cancer. In this paper, deep learning models using convolutional neural network (CNN) have been built to automatically classify mammograms into benign and malignant. Issues like overfitting and dataset imbalance are overcome. Experimentation has been done on two publicly available datasets, namely mammographic image analysis society (MIAS) database and digital database for screening mammography (DDSM). Robustness of the models is accomplished by merging the datasets. In our experimentation, MatConvNet has achieved an accuracy of 94.2% on the merged dataset, performing the best amongst all the CNN models used individually. Hungarian optimization algorithm is employed for selection of individual CNN models to form an ensemble. Ensemble of CNN models led to an improved performance, resulting in an accuracy of 95.7%.
A novel smart contract based blockchain with sidechain for electronic voting

Authors:
raw_author_name: Nirmala Hiremani
raw_affiliation_string: Visvesvaraya Technological University
raw_author_name: Mahantesh Birje
raw_affiliation_string: Visvesvaraya Technological University

Abstract: Several countries have been researching digital voting methods in order to overcome the challenges of paper balloting and physical voting. The recent coronavirus disease 2019 (COVID-19) epidemic has compelled the remote implementation of existing systems and procedures. Online voting will ultimately become the norm just like unified payments interface (UPI) payments and online banking. With digital voting or electronic voting (e-voting) a small bug can cause massive vote rigging. E-voting must be honest, exact, safe, and simple. E-voting is vulnerable to malware, which can disrupt servers. Blockchain’s end-to-end validation solves these problems. Three smart contracts-voter, candidate, and voting-are employed. The problem of fraudulent actions is addressed using vote coins. Vote coins indicate voter status. Sidechain technology complements blockchain. Sidechains improve blockchain functionality by performing operations outside of blockchains and delivering the results to the mainchain. Thus, storing the encrypted vote on the sidechain and using the decrypted result on the mainchain reduces cost. Building access control policies to grant only authorized users’ access to the votes for counting is made simpler by this authorization paradigm. Results of the approach depict the proposed e-voting system improves system security against replay attacks and reduces the processing cost as well as processing time.
Hybrid chaotic map with L-shaped fractal Tromino for image encryption and decryption

Authors:
raw_author_name: Konala Veera Venkata Satyanarayana Reddy
raw_affiliation_string: Andhra University

Abstract: Insecure communication in digital image security and image storing are considered as important challenges. Moreover, the existing approaches face problems related to improper security at the time of image encryption and decryption. In this research work, a wavelet environment is obtained by transforming the cover image utilizing integer wavelet transform (IWT) and hybrid discrete cosine transform (DCT) to completely prevent false errors. Then the proposed hybrid chaotic map with L-shaped fractal Tromino offers better security to maintain image secrecy by means of encryption and decryption. The proposed work uses fractal encryption with the combination of L-shaped Tromino theorem for enhancement of information hiding. The regions of L-shaped fractal Tromino are sensitive to variations, thus are embedded in the watermark based on a visual watermarking technique known as reversible watermarking. The experimental results showed that the proposed method obtained peak signal-to-noise ratio (PSNR) value of 56.82dB which is comparatively higher than the existing methods that are, Beddington, free, and Lawton (BFL) map with PSNR value of 8.10 dB, permutation substitution, and Boolean operation with PSNR value of 21.19 dB and deoxyribonucleic acid (DNA) level permutation-based logistic map with PSNR value of 21.27 dB.
A study of feature extraction for Arabic calligraphy characters recognition

Authors:
raw_author_name: Abdelhay Zoizou
raw_affiliation_string: University Sidi Mohamed Ben Abdellah
raw_author_name: Arsalane Zarghili
raw_affiliation_string: University Sidi Mohamed Ben Abdellah
raw_author_name: Ilham Chaker
raw_affiliation_string: University Sidi Mohamed Ben Abdellah

Abstract: Optical character recognition (OCR) is one of the widely used pattern recognition systems. However, the research on ancient Arabic writing recognition has suffered from a lack of interest for decades, despite the availability of thousands of historical documents. One of the reasons for this lack of interest is the absence of a standard dataset, which is fundamental for building and evaluating an OCR system. In 2022, we published a database of ancient Arabic words as the only public dataset of characters written in Al-Mojawhar Moroccan calligraphy. Therefore, such a database needs to be studied and evaluated. In this paper, we explored the proposed database and investigated the recognition of Al-Mojawhar Arabic characters. We studied feature extraction by using the most popular descriptors used in Arabic OCR. The studied descriptors were associated with different machine learning classifiers to build recognition models and verify their performance. In order to compare the learned and handcrafted features on the proposed dataset, we proposed a deep convolutional neural network for character recognition. Regarding the complexity of the character shapes, the results obtained were very promising, especially by using the convolutional neural network model, which gave the highest accuracy score.
Adversarial sketch-photo transformation for enhanced face recognition accuracy: a systematic analysis and evaluation

Authors:
raw_author_name: Sarappadi Narasimha Prasad
raw_affiliation_string: Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education

Abstract: This research provides a strategy for enhancing the precision of face sketch identification through adversarial sketch-photo transformation. The approach uses a generative adversarial network (GAN) to learn to convert sketches into photographs, which may subsequently be utilized to enhance the precision of face sketch identification. The suggested method is evaluated in comparison to state-of-the-art face sketch recognition and synthesis techniques, such as sketchy GAN, similarity-preserving GAN (SPGAN), and super-resolution GAN (SRGAN). Possible domains of use for the proposed adversarial sketch-photo transformation approach include law enforcement, where reliable face sketch recognition is essential for the identification of suspects. The suggested approach can be generalized to various contexts, such as the creation of creative photographs from drawings or the conversion of pictures between modalities. The suggested method outperforms state-of-the-art face sketch recognition and synthesis techniques, confirming the usefulness of adversarial learning in this context. Our method is highly efficient for photo-sketch synthesis, with a structural similarity index (SSIM) of 0.65 on The Chinese University of Hong Kong dataset and 0.70 on the custom-generated dataset.
A novel improved elephant herding optimization for path planning of a mobile robot

Authors:
raw_author_name: Ahmed Oultiligh
raw_affiliation_string: Cadi Ayyad University
raw_author_name: Hassan Ayad
raw_affiliation_string: Cadi Ayyad University
raw_author_name: Abdeljalil El Kari
raw_affiliation_string: Cadi Ayyad University
raw_author_name: Mostafa Mjahed
raw_affiliation_string: Royal School of Aeronautics
raw_author_name: Nada El Gmili

Abstract: Swarm intelligence algorithms have been in recent years one of the most used tools for planning the trajectory of a mobile robot. Researchers are applying those algorithms to find the optimal path, which reduces the time required to perform a task by the mobile robot. In this paper, we propose a new method based on the grey wolf optimizer algorithm (GWO) and the improved elephant herding optimization algorithm (IEHO) for planning the optimal trajectory of a mobile robot. The proposed solution consists of developing an IEHO algorithm by improving the basic EHO algorithm and then hybridizing it with the GWO algorithm to take advantage of the exploration and exploitation capabilities of both algorithms. The comparison of the IEHO-GWO hybrid proposed in this work with the GWO, EHO, and cuckoo-search (CS) algorithms via simulation shows its effectiveness in finding an optimal trajectory by avoiding obstacles around the mobile robot.
Modified fuzzy rough set technique with stacked autoencoder model for magnetic resonance imaging based breast cancer detection

Authors:
raw_author_name: Vishwanath Petli

Abstract: Breast cancer is the common cancer in women, where early detection reduces the mortality rate. The magnetic resonance imaging (MRI) images are efficient in analyzing breast cancer, but it is hard to identify the abnormalities. The manual breast cancer detection in MRI images is inefficient; therefore, a deep learning-based system is implemented in this manuscript. Initially, the visual quality improvement is done using region growing and adaptive histogram equalization (AHE), and then, the breast lesion is segmented by Otsu thresholding with morphological transform. Next, the features are extracted from the segmented lesion, and a modified fuzzy rough set technique is proposed to reduce the dimensions of the extracted features that decreases the system complexity and computational time. The active features are fed to the stacked autoencoder for classifying the benign and malignant classes. The results demonstrated that the proposed model attained 99% and 99.22% of classification accuracy on the benchmark datasets, which are higher related to the comparative classifiers: decision tree, naïve Bayes, random forest and k-nearest neighbor (KNN). The obtained results state that the proposed model superiorly screens and detects the breast lesions that assists clinicians in effective therapeutic intervention and timely treatment.
A dilution-based defense method against poisoning attacks on deep learning systems

Authors:
raw_author_name: Youngho Cho
raw_affiliation_string: Korea National Defense University

Abstract: Poisoning attack in deep learning (DL) refers to a type of adversarial attack that injects maliciously manipulated data samples into a training dataset for the purpose of forcing a DL model trained based on the poisoned training dataset to misclassify inputs and thus significantly degrading its performance and reliability. Meanwhile, a traditional defense approach against poisoning attacks tries to detect poisoned data samples from the training dataset and then remove them. However, since new sophisticated attacks avoiding existing detection methods continue to emerge, a detection method alone cannot effectively counter poisoning attacks. For this reason, in this paper, we propose a novel dilution-based defense method that mitigates the effect of poisoned data by adding clean data to the training dataset. According to our experiments, our dilution-based defense technique can significantly decrease the success rate of poisoning attacks and improve classification accuracy by effectively reducing the contamination ratio of the manipulated data. Especially, our proposed method outperformed an existing defense method (Cutmix data augmentation) by 20.9%p at most in terms of classification accuracy.
Comparison of convolutional neural network models for user’s facial recognition

Authors:
raw_author_name: Javier Orlando Pinzón-Arenas
raw_affiliation_string: Universidad Militar Nueva Granada
raw_author_name: Robinson Jimenez-Moreno
raw_affiliation_string: Universidad Militar Nueva Granada
raw_author_name: Javier Eduardo Martinez Baquero
raw_affiliation_string: Universidad de los Llanos

Abstract: This paper compares well-known convolutional neural networks (CNN) models for facial recognition. For this, it uses its database created from two registered users and an additional category of unknown persons. Eight different base models of convolutional architectures were compared by transfer of learning, and two additional proposed models called shallow CNN and shallow directed acyclic graph with CNN (DAG-CNN), which are architectures with little depth (six convolution layers). Within the tests with the database, the best results were obtained by the GoogLeNet and ResNet-101 models, managing to classify 100% of the images, even without confusing people outside the two users. However, in an additional real-time test, in which one of the users had his style changed, the models that showed the greatest robustness in this situation were the Inception and the ResNet-101, being able to maintain constant recognition. This demonstrated that the networks of greater depth manage to learn more detailed features of the users' faces, unlike those of shallower ones; their learning of features is more generalized. Declare the full term of an abbreviation/acronym when it is mentioned for the first time.
Hierarchal attribute based cryptographic model to handle security services in cloud environment: a new model

Authors:
raw_author_name: Banavathu Rajarao
raw_affiliation_string: Jawaharlal Nehru Technological University Anantapur

Abstract: The sharing of information in the cloud is a unique element of the environment, but there is a risk that the information may land with the wrong people. To counterattack this problem, security-associated methodologies were used to secure the information that was readily available to clients. Despite the lack of benefits, this provides productive/adaptability and dependability in access control strategies between clients in the sharing of information. The novel hierarchal attribute-based cryptographic security model (NHACSM) is being proposed to provide adaptability, versatility, and access control in sharing information in the appropriate climate. This model allows clients to share information in a hierarchal way, allowing for a productive assessment of access control strategy and improved security. The NHACSM method is used to reduce the total time values for different user instances compared to conventional approaches, for example, attribute-set-based encryption (ASBE), key-policy attribute-based encryption (KP-ABE), and ciphertext-policy attribute-based encryption (CP-ABE). With respect to 10 instances existing methods achieve 2.7, 2.5, and 2.3 respectively, and also compared to 20, 30, 40, and 50 instances, our proposed method is low. The encryption and decryption time evaluation values and performance evaluation of different approaches, ASBE, CP-ABE, were taken into account when increasing the user instance.
Generate fuzzy string-matching to build self attention on Indonesian medical-chatbot

Authors:
raw_author_name: Wiwin Suwarningsih
raw_affiliation_string: National Research and Innovation Agency
raw_author_name: Nuryani Nuryani
raw_affiliation_string: National Research and Innovation Agency

Abstract: Chatbot is a form of interactive conversation that requires quick and precise answers. The process of identifying answers to users’ questions involves string matching and handling incorrect spelling. Therefore, a system that can independently predict and correct letters is highly necessary. The approach used to address this issue is to enhance the fuzzy string-matching method by incorporating several features for self-attention. The combination of fuzzy string-matching methods employed includes Jaro Winkler distance + Levenshtein Damerau distance and Damerau Levenshtein + Rabin Carp. The reason for using this combination is their ability not only to match strings but also to correct word typing errors. This research contributes by developing a self-attention mechanism through a modified fuzzy string-matching model with enhanced word feature structures. The goal is to utilize this self-attention mechanism in constructing the Indonesian medical bidirectional encoder representations from transformers (IM-BERT). This will serve as a foundation for additional features to provide accurate answers in the Indonesian medical question and answer system, achieving an exact match of 85.7% and an F1-score of 87.6%.
Forecasting Response to Treatment with Global Deep Learning and Patient-Specific Pharmacokinetic Priors.

Authors:
raw_author_name: Willa Potosnak
raw_author_name: Cristian Challu
raw_author_name: Kin G Olivares
raw_author_name: Artur Dubrawski

Abstract: Forecasting healthcare time series is crucial for early detection of adverse outcomes and for patient monitoring. Forecasting, however, can be difficult in practice due to noisy and intermittent data. The challenges are often exacerbated by change points induced via extrinsic factors, such as the administration of medication. To address these challenges, we propose a novel hybrid global-local architecture and a pharmacokinetic encoder that informs deep learning models of patient-specific treatment effects. We showcase the efficacy of our approach in achieving significant accuracy gains for a blood glucose forecasting task using both realistically simulated and real-world data. Our global-local architecture improves over patient-specific models by 9.2-14.6%. Additionally, our pharmacokinetic encoder improves over alternative encoding techniques by 4.4% on simulated data and 2.1% on real-world data. The proposed approach can have multiple beneficial applications in clinical practice, such as issuing early warnings about unexpected treatment responses, or helping to characterize patient-specific treatment effects in terms of drug absorption and elimination characteristics.
MAEF-Net: MLP Attention for Feature Enhancement in U-Net based Medical Image Segmentation Networks

Authors:
raw_author_name: Jianfei Dong
raw_affiliation_string: School of Future Science and Engineering, Soochow University, Suzhou, China

Abstract: Medical image segmentation plays an important role in diagnosis. Since the introduction of U-Net, numerous advancements have been implemented to enhance its performance and expand its applicability. The advent of Transformers in computer vision has led to the integration of self-attention mechanisms into U-Net, resulting in significant breakthroughs. However, the inherent complexity of Transformers renders these networks computationally demanding and parameter-heavy. Recent studies have demonstrated that multilayer perceptrons (MLPs), with their simpler architecture, can achieve comparable performance to Transformers in natural language processing and computer vision tasks. Building upon these findings, we have enhanced the previously proposed "Enhanced-Feature-Four-Fold-Net" (EF 3-Net) by introducing an MLP-attention block to learn long-range dependencies and expand the receptive field. This enhanced network is termed "MLP-Attention Enhanced-Feature-four-fold-Net", abbreviated as "MAEF-Net". To further enhance accuracy while reducing computational complexity, the proposed network incorporates additional efficient design elements. MAEF-Net was evaluated against several general and specialized medical image segmentation networks using four challenging medical image datasets. The results demonstrate that the proposed network exhibits high computational efficiency and comparable or superior performance to EF 3-Net and several state-of-the-art methods, particularly in segmenting blurry objects.