Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Framework for Forensic Bone Age Estimation Using CNN Architecture and Multimodal Fusion

This study introduces an approach to forensic bone age estimation by employing a multimodal fusion strategy within a Convolutional Neural Network (CNN) architecture. The challenge of low-quality images in the dataset is addressed through the incorporation of the Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing technique, effectively enhancing image quality and bolstering model accuracy. Furthermore, efficient net transfer learning techniques are implemented for additional refinement. The validation of the proposed method, assessed using Mean Squared Error (MSE) and the coefficient of determination $R^{2}$ score, demonstrates compelling results with an MSE value of 18.21 and an $R^{2}$ score of 0.59. These outcomes surpass those of existing methods, highlighting the superior accuracy and robustness of our framework. This marks a significant advancement in forensic bone age estimation.
A Dual-Band Dual-Polarized 2×2 Antenna Array with Beamforming for 5G AiP and mmWave Applications

In this study, we present a dual-band (28/39GHz) 2 by 2 antenna array design on a 10(4+2+4) multi-layer organic substrate with broad bandwidth and higher isolation onto a compact AiP module. In addition, the H-type slot antenna structure can improve interference and isolation between 28 and 39GHz bands. The measured result shows the isolation can larger than 15dB between low and high band. For antenna measurement, the spherical of probing chamber is utilized to validate 28/39GHz antenna pattern and performance with a package level passive testing. Our AiP measured result shows the return loss is better than 10 dB in 23.5-30.5 GHz range, with ~7 GHz bandwidth and provides a high-gain per element (above, 6 dBi) radiation pattern for 28GHz applications. For 39GHz band, the antenna has 7 GHz bandwidth and provides a 5 dBi gain between 38–45 GHz. Comparison of S-parameter between simulated and measured results, there is a good correlation to obtain the quality of manufacturing. Furthermore, a 28GHz beamforming array is designed and demonstrated. The beamfoming array consists of a four-channel transceiver with 2 × 2 antenna array. The beamformer IC is implemented by TSMC 90-nm CMOS technology and the flip-chip bonded on BT substrate. For beamforming test, an integrated socket and load board are designed for support the mmWave module and then connection between DUT and test system. The load board substrate includes the IF, LO, and DC distribution network for respectively. There are 4-channel transmit beamforming array front-end module for testing. Finally, the 3D beam steering of 2 by 2 antenna array is measured with multi-states at 28 GHz, with a maximum realized gain of 11.8 dBi achieved in the main beam (θ=0°). It shows that beamforming can be generated at a specific beam direction by controlling the phase of the signal in each antenna element.
Design of Hybrid Energy Aware Cluster Based Multipath Routing Protocol in IoT Assisted Wireless Sensor Networks

In the past decade, the Internet of Things (IoT) has gained a significant research attention due to its ability to revolutionize the cyber and physical world. IoT makes physical items as intelligent objects, which will be capable of collecting, processing and managing the digital data. The communication of big data will be enabled by sensor nodes (SNs) in the IoT-based networks. The lifespan of the wireless sensor nodes is reducing due to the extreme consumption of transmission power. This research study designs and develops a novel Hybrid Energy Aware Cluster based Multipath Routing (HEACMPR) protocol in the IoT assisted WSN. The proposed HEACMPR technique primarily clusters the wireless nodes in the IoT assisted WSN and then selects the optimal route in the network. To obtain this, the proposed HEACMPR technique utilizes the sparrow search algorithm based clustering (SSO-C) technique to select the set of Cluster Heads (CHs) and produce clusters. For route selection, Aquila optimization algorithm based routing (AOA-R) approach is applied to effectually discover the routes to the destination. The simulation results of the HEACMPR technique is examined under different set of performance measures. The extensive outcomes showed the efficiency of the HEACMPR technique over other measures.
Design and Implementation of Neuro-Fuzzy Control Approach for Robot's Trajectory Tracking

Achieving precise trajectory tracking while minimizing errors, reducing settling time, and curbing overshoot presents a significant challenge in the domain of robot-manipulator control. In response, this paper proposes a novel controller design for robot manipulators, incorporating two distinct approaches: a conventional fuzzy controller and an advanced Neuro-Fuzzy controller. These controllers are tailored to elevate trajectory tracking performance by simultaneously optimizing time characteristics in robot manipulator responses. To assess their efficacy, a range of test scenarios is devised, encompassing different trajectory types, including linear and nonlinear paths. Upon comparative analysis, the results highlight an intriguing contrast: while the fuzzy controller exhibits higher maximum overshoot and longer settling times in the robot's response, the Neuro-Fuzzy controller consistently demonstrates superior performance. The comprehensive error analysis such as Integral Square Error (ISE) and Integral Absolute Error (IAE) further underscores the capability of the Neuro-Fuzzy-based robot manipulator in delivering trajectory tracking precision. This study contributes to the advancement of trajectory control techniques for robot manipulators, paving the way for enhanced automation and performance across various applications.
FruityHub: A diverse collection of fruits created for edibility estimation

The export of fresh fruits not only exerts a substantial influence on both national and global economies but also serves as a crucial source of livelihood for a vast number of farmers. Automated estimation of these fruits’ edibility (i.e. freshness level) is very essential to maintain the quality of exported fruits and to reduce the financial loss. Owing to the over-whelming performances achieved by deep neural networks in performing image classification tasks in recent years, many researchers have designed automated methods by exploiting the deep neural architectures to perform non-invasive quality identification of several plant parts (leaves, fruits, vegetables, etc.) in more time-efficient and cost-effective manner. The most notable hindrance which is faced by researchers while designing the automated methods is the lack of proper databases which are required to train them. To mitigate this research gap, in this work we have created a novel database namely, FruityHub comprising of a total of 1786 image frames belonging to eight different types of fruits namely, Dragon Fruits, Net Melons, Strawberries, Star Fruit, Zucchini, Mango, Pineapple and Plum. The image frames belonging to each fruit category can be classified as Fresh, Damaged and Severely Damaged which facilitates automated estimation of edible quality of these fruits depending upon their skin color, texture, shape, etc. We have also validated the effectiveness of this database using fourteen deep neural networks namely, AlexNet, Vgg16, ResNet152, MobileNetv2, ShuffleNetv2, SqueezeNet, InceptionV3, InceptionResNetv2, DenseNet121, NasNetLarge, NasNetMobile, GhostNetv2, EfficientNetB7 and Xception. The validation is performed using quantitative metrics like Test Accuracy, Recall, Precision and F1 score.
Efficient Sample Collection to Construct Observation Models for Contact-Based Object Pose Estimation

It is important for a robot to accurately estimate the pose of an object in order to manipulate it. Estimation by tactile information, which is not affected by occlusion, provides more valid estimates than visual information in some cases. To realize such tactile sensing-based object pose estimation, it is necessary to construct an observation model in advance by collecting samples of contact action and observed information. This process of sample collection is generally time-consuming and its cost can be a disadvantage of the tactile sensing-based object pose estimation. To mitigate the cost, in this paper, we propose an efficient sample collection method to generate observation models for object pose estimation. Contact actions useful for pose estimation are quantitatively evaluated, and efficient sample collection is achieved by a search to find the point that minimizes the quantitative value. The proposed sample collection strategy was evaluated in simulations by assuming a specific shape of object with a soft tactile sensor. It was shown that the proposed strategy realizes more efficient sampling in comparison with random/uniform sampling methods.
Detection of Breast Cancer using a Flexible Antenna at ISM Band

Breast cancer is a major health issue affecting women globally, and for detection traditional medical imaging methods like ultrasound and X-ray mammography have drawbacks due to low tissue contrast and potential risks. A low-cost wearable wireless antenna designed at 5.8 GHz is designed which can be integrated in wearable garments and in flexible biomedical devices. The antenna is constructed of a rectangular patch printed on a flexible Kapton polyimide substrate (relative permittivity, $\varepsilon r=3.4$) The prototype has been tested in concave bent geometries for radii of 25,45, and 73m.After bending there is a slight shift in the S-Parameter from 5.8GHz to 5.734 GHz, 5.7535GHz and 5.752GHz. When a breast phantom with tumor is positioned at a certain distance from antenna, a slight shift of S-parameter is observed. Compared to non-malignant breast cells, malignant cells have distinct electrical properties. SAR value of the proposed antenna is 0.496 W/kg for 10 g and 1.27 W/kg for 1 g of body tissue which is safe for body tissue. The designed antenna’s compact, portable, and flexible design, along with its performance in bending forms, demonstrate its applicability forfuture electronic devices.
Advancements in Face Masking Anonymization: Novel Approaches, Robust Privacy Metrics, and Real-Time Solutions

In todays world, where concerns about privacy are on the rise the use of techniques to mask faces and protect individuals identities in multimedia content has become extremely important. This study thoroughly examines methods of anonymization with a specific focus on how Generative Adversarial Networks (GANs) can be used to enhance privacy preservation. By conducting an analysis that includes techniques like Gaussian Blur, Pixelation, Deep Learning and hybrid approaches our research highlights the impressive effectiveness of GAN based anonymization. This innovative approach not effectively hides identities but also ensures that the anonymized content remains understandable and usable. Alongside evaluating existing techniques our paper also sets the stage for exploration, in this field with the goal of advancing face masking anonymization techniques.
Convolutional Neural Network Compression Method Based on Multi-Factor Channel Pruning

Deep neural networks have been widely applied across various domains, but their numerous parameters and high computational demands limit their practical usage scenarios. To address this issue, this paper introduces a convolutional neural network compression method based on multi-factor channel pruning. By integrating scaling and shifting factors from batch normalization layers, a multi-factor channel salience metric is proposed to measure channel importance. By removing redundant channels within the convolutional neural network, a compressed model is obtained. On the CIFAR-10 dataset, we pruned 93.06% of the parameters and 91.92% of the calculations from the VGG13BN network, with only a 2.81% decrease in accuracy. On the CIFAR-100 dataset, we pruned 72.84% of the parameters and 72.03% of the calculations from the VGG13BN network, with an accuracy improvement of 4.11%.
Modified Image Encryption Algorithm Based on Chaotic Cryptography

In today's interconnected world, where sensitive visual information is transmitted and stored across various channels and devices. To address the ever-growing security challenges in image encryption, this study presents a novel chaotic image encryption algorithm and conducts a comprehensive set of tests to assess its security and efficacy. The proposed encryption procedure leverages the inherent unpredictability of chaotic systems (CSs) to enhance the confidentiality and integrity of image data. Chaotic maps are employed to generate cryptographic keys, and pixel-level transformations are applied to obscure the content of the original image. This cryptographic approach aims to thwart a range of attacks, from statistical analysis to brute-force decryption attempts. In the course of the research, the algorithm's performance is meticulously evaluated. The security assessment included rigorous testing against common cryptographic attacks, including chosen-plaintext attacks, statistical analysis, and exhaustive key search. The robustness of the algorithm was scrutinized through experiments involving image compression, noise injection, and minor image alterations. The findings demonstrate that the proposed algorithm is safeguarding image data from unauthorized access. It exhibits resistance against various cryptographic attacks and maintains its security even when images undergo transformations or adversarial interference. Moreover, the algorithm's cryptographic key generation process ensures the production of secure keys, bolstering the overall security posture. Furthermore, the algorithm's computational efficiency strikes a balance between security and practicality, rendering it suitable for real-world applications where rapid encryption and decryption are essential. The comprehensive set of tests conducted underscores the algorithm's robustness and resilience against diverse security threats. While this study represents a significant step toward securing digital image data, ongoing research is encouraged to adapt the algorithm to specific application domains and further explore its compatibility with emerging technologies.
Examination of Interface Trap Charges on Electrically Doped Tunnel FET in the Presence of High-K Dielectric

In this article, we delve into a comparative analysis involving the dual material bipolar-gate electrically doped-tunnel FET (DMBG-EDTFET) and two proposed devices: the hetero-materials DMBG-ED-TFET (HM-DMBG-ED-TFET) and the high-K dielectric DMBG-ED-TFET (HD-DMBG-ED-TFET). The HM-DMBG-ED-TFET incorporates Si-Ge material at the source side and silicon on the drain and channel, while the HD-DMBG-ED-TFET employs a high-K dielectric ($\left.HfO_{2}\right)$ for its oxide layer, resulting in an observed enhancement in the ON current. However, the HM-DMBG-ED-TFET exhibits limitations in analog and RF performance, a critical concern for circuitry and radio frequency-related applications. Additionally, we explore the impact of ITCs (interface traps and charges) on these devices, aiming to address reliability issues. This investigation involves analyzing the effects of ITCs on various aspects, including electrical characteristics, linearity and distortion parameters have been carried out for low distortion and high-frequency performance investigation of the device.
Biological Template Protection Based on Multikey Cryptographic Decomposition Features

As more and more privacy protection mechanisms are introduced, facial recognition is gradually being optimized in terms of homo-morphic computing power. However, certain aspects have been overlooked, such as the reversibility of facial features due to key leakage and the degradation of accuracy when keys are exchanged to update the template library. To address this problem, we propose a feature grouping method that aims to improve the efficiency of ciphertext operation. This approach involves a multi-key encryption strategy for facial features that ensures their irreversibility. Experimental validation supports both the irreversibility and the ability to update within this framework. When rigorously tested, this scheme shows increased security compared to single-key frameworks because part of the key is withheld, making it difficult for attackers to obtain comprehensive facial feature data. In addition, even when using keys of the same length, attackers are faced with increased time expenditure. With regard to the upgradability of systems, experimental findings have shown for the first time that the loss of accuracy associated with homomorphic encryption algorithms is within tolerable limits. It is noteworthy that our proposed method has an even smaller loss in accuracy. When assessing the performance and precision of the algorithm, we achieved an impressive accuracy rate of 97.425% with processing times of 0.302 seconds per face. The benefits of our feature grouping method become more clear as the face template library grows. It outperforms non-grouping methods by more than 4 seconds on nearly 1,000 face libraries.
Machine Learning Classifiers for Social Media Bots Detection on Twitter using Explainable AI

Social networks have shaped modern society by revolutionizing how people connect and communicate with others around the world. Social media bots have become more prevalent as a result of social networks' phenomenal development and influence, nevertheless. These automated systems that are guided by algorithms or malicious actors can mimic human behavior and provide the impression that actual people are there. Growing concerns about the integrity of online debate, the dissemination of false information, and potential public opinion manipulation have been sparked by the use of social media bots. It is now necessary to create efficient procedures for identifying and thwarting social media threats in order to tackle this expanding danger. Such efforts use a variety of methods, including as examining profile metadata, observing behavioral patterns, and utilizing the strength of machine learning algorithms. In this paper, we propose an approach which uses multiple features of a user entity on Twitter to train a model for social media bot detection. We employed Twibot-22 dataset to conduct the experiment and compared the performance of our approach with established benchmark models in order to assess its efficacy. Remarkably, our Random Forest model outperformed the benchmarks, achieving an accuracy rate of 0.96. We utilized explainable AI technique to enhance the interpretability of random forest model. This study provides insights that are of immense value in refining the model and delving deeper into the distinguishing features of social bots and human users within the Twitter environment.
Retention Analysis and Player Segmentation based on Gameplay Data

The global gaming market has been expanding at a fast pace. However, the online and mobile gaming industry is struggling with the lower retention rate compared to the other domains. Although improving the retention rate will lead to more profit and reducing costs, the extremely high churn rate makes it difficult to apply the mass marketing strategy. In order to solve this problem, player segmentation is one of the approaches to design appropriate marketing campaigns to improve the retention rate. Therefore, the goal of this paper is to improve the retention rate by cluster and retention analysis. To achieve this, new players needs to be segmented. Player segmentation was conducted by using the Agglomerative hierarchical and K-means algorithms and based on the aggregated player variables which were retrieved from play log. The K-means model with Manhattan method is selected as the best clustering model. New players are categorized into three segments by the model; Churners, Constant players, and Initially excited players. Next, retention analysis for the player segments was conducted. Retention rates were calculated on Day1, Day7 and Day28 for the three segments, and characteristics of each segment were extracted. Based on the characteristics, segmental recommendations to improve the retention rate were suggested for the three segments: constant players, churners and initially excited players. The segmental recommendations allow to adequately approach more specific and narrowed players.
Multi-Versatile IoT Tracker for Enhanced Monitoring and Tracking Applications

Regardless of their past or present circumstances, people from all walks of life, including women, kids, and the elderly, have learned to prioritize their security. In response to this issue, several Internet of Things (IoT) software solutions have been developed to assist individuals in alerting designated contacts when facing challenging situations. The popularity of location monitoring services has surged as awareness about missing persons has increased. This article aims to demonstrate how to use the A9G board to build a programmable Internet of Things tracker with features like audio surveillance, an SOS (Save Our Souls) button, and location tracking. The tracker may be used to track a variety of things, including automobiles, animals, and personal possessions. An alarm mechanism within the gadget detects unusual activities and alerts users. The ESP32C3 microcontroller was chosen because of its powerful processing skills, low power usage, and integrated Wi-Fi and Bluetooth functions. The gadget has remote control capabilities thanks to the A9G module's ability to send and receive data and instructions through cellular networks. The proposed IoT tracker can also be paired with an Android application that tracks the location using GPS and Google Maps and notifies the user and includes calling functionality. This study proposes a simple and affordable remote monitoring device that can monitor a wide range of variables for home and professional use. High-capacity lithium polymer batteries power the device.
Evaluating the Effective Machine Learning Techniques for Early Prediction of Heart Disease

Machine learning has found applications in numerous fields across the globe its use in healthcare has proven to be highly valuable, particularly in diagnosing conditions like heart disease and locomotor disorders. This research study focuses on leveraging the popular algorithms in machine learning for the prediction of potential heart diseases in patients. The study includes a comparative analysis of various classifiers, namely decision trees, Naive Bayes, logistic regression, Support Vector Machines (SVM), and Random Forest (RF). The goal is to categorize the maximum effective classifier for accurate and consistent predictions. Additionally, the research proposes an ensemble classifier that combines both strong and weak classifiers. This hybrid approach aims to take advantage of a large number of training and validation samples, enhancing the classifier's performance. The results of the comparative analysis and the proposed ensemble classifier hold promise for advancing the field of medical diagnosis and treatment customization. Ultimately, the incorporation of techniques in the healthcare sector, machine learning has the potential to significantly advance patient outcomes and overall healthcare quality.
One-Probe Nanoprobing of Power Devices and Electronic Packages

Results involving the use of a single nanoprobing contact are presented, on both a SiC planar MOSFET device, and a small system-in package. In both cases, mechanical polished samples were prepared, and then probed with a single contact. For the SiC MOSFET, the depletion zones were imaged while the samples were mounted on a 45 0 stub. Discussion of the different signals generated from Passive Voltage Contrast (PVC) and Electron Beam Induced Current (EBIC), as well as possible artifacts of sample grounding, are provided. For the package, Electron Beam Absorbed Current (EBAC) provided indication of connected features within the sample. The experiment showed the capability of measuring features across three orders of magnitude in size.