Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of γ-ray radiation on two-dimensional molybdenum disulfide (MoS2) nanomechanical resonators

We report on experimental investigation and analysis of γ-ray radiation effects on two-dimensional molybdenum disulfide (MoS2) drumhead nanomechanical resonators vibrating at megahertz frequencies. Given calibrated dosages of γ-ray radiation of ∼5000 photons with energy at 662 keV, upon exposure over 24 or 12 h, all the MoS2 resonators exhibit ∼0.5–2.1% resonance frequency upshifts due to the ionizing γ-ray induced charges and their interactions. The devices show γ-ray photon responsivity of ∼30–82 Hz/photon, with an intrinsic γ-ray sensitivity (limit of detection) estimated to approach ∼0.02–0.05 photon. After exposure expires, resonance frequencies return to an ordinary tendency where the frequency variations are dominated by long-term drift. These γ-ray radiation induced frequency shifts are distinctive from those due to pressure variation or surface adsorption mechanisms. The measurements and analyses show that MoS2 resonators are robust yet sensitive to very low dosage γ-ray, demonstrating a potentia.
User Profiling and Interest-based Machine Learning to Solve the Cold-Start Problem for a Recommendation Engine

The cold-start problem is a major issue in recommender systems that becomes a challenge for e-commerce companies when processing large amounts of data. Several strategies have been proposed to overcome the cold-start problem using different approaches, but the user interest approach is a unique and standalone approach. In this study, we present an interest-based approach that establishes a relationship between user profile data and corresponding user interests. Our work will recommend the items to the cold-start users based on their pro-files and interest matching, and also promote the cold-start users. Our approach is unique in that we consider user interests rather than similarities between items and recommend items which are based on user interests. The experiments and related tests show that proposed method is efficient and successful as far as of classification time, training time, and precision. Although other classifiers were efficient in terms of accuracy, the Bayesian classifier and Chain Classifier had the shortest classification time as well as training time.
Effectiveness of Certificate Transparency (CT) Check and Other Datapoints in Countering Phishing Attacks

Phishing, a form of social engineering attack, has been present for more than two decades. Despite efforts by academic researchers and technology companies to combat this threat, statistics indicate that Phishing attacks are increasing. Cyber criminals target unsuspecting victims using Phishing attacks, particularly as critical businesses like banks expand their online offerings. This paper assesses academic and technological strategies to mitigate Phishing attacks, such as Certificate Transparency (CT). However, practical analysis of CT reveals that bad actors can evade CT checks by using wild card SSL certificates, which are readily available. The paper presents findings on the limitations of CT and how attackers can bypass it. For real-time analysis, CT monitoring implemented by Facebook is leveraged. The results pave the way for future research into anti-phishing measures.
A Study on Different Hybrid Deep Learning Approaches to Forecast air Pollution Concentration of Particulate Matter

Implementation of sophisticated and hybrid technology for air pollution concentration forecasting of particulate matter is considered to be a critical issue nowadays due to rapid urbanisation and industrialisation leading to higher air pollution levels. The PM impacts human health critically, increasing morbidity rates globally. The paper aims at studying different hybrid, deep-learning approaches to forecasting the air pollution concentration of PM. adopting a secondary, qualitative and deductive approach, the paper conducts comprehensive data collection and analysis. The results indicate that using multiple network layers within a deep learning hybrid model holds the most accuracy in practical usages such as CNN-LSTM model and GRU/Bi-GRU modelling. It is concluded that the hybrid deep learning approach enables the identification of hidden patterns in the PM concentration that can be used for adopting mitigation approaches. However, issues related to financial, logistical and infrastructure related issues are identified in the implementation process of such hybrid models widely, especially in low- and medium-income countries.
An Investigation of Task Offloading Latency in Edge-Cloud Environment Using Machine Learning Techniques

The volume of information produced by Internet of Things (IoT) gadgets has significantly expanded along with how many of these gadgets are connected to the Internet. Now a day’s Edge-Cloud computing has a major roll to store the data. Edge computing is close proximity to the network's edge enables quick data processing and supporting user request. Therefore, we present an Edge-Cloud system design that enables scheduling IoT application offloading operations reducing a massive volume of data being transmitted in the network.
Impact of Technology-based Education on Student Learning Outcomes and Engagement

This study seeks to determine the impact of technology-based education on student learning outcomes and engagement. This study also identified recent trends and challenges related to the implementation of technology in education, such as the need for technical support and the potential for distractions during class. The results showed that technology-based education had a positive impact on student learning outcomes, including academic achievement, knowledge retention, and critical thinking skills. Additionally, the use of technology in education led to increased student engagement and motivation, as well as improved teacher-student interactions. These findings have implications for educators, policymakers, and technology developers who seek to enhance the quality and effectiveness of technology-based education.
IoT-Based Android Malware Detection Using Graph Neural Network With Adversarial Defense

Since the Internet of Things (IoT) is widely adopted using Android applications, detecting malicious Android apps is essential. In recent years, Android graph-based deep learning research has proposed many approaches to extract relationships from the application as a graph to generate graph embeddings. First, we demonstrate the effectiveness of graph-based classification using graph neural networks (GNNs)-based classifier to generate API graph embedding. The graph embedding is used with “Permission” and “Intent” to train multiple machine learning and deep learning algorithms to detect Android malware. The classification achieved an accuracy of 98.33% in CICMaldroid and 98.68% in the Drebin data set. However, the graph-based deep learning is vulnerable as an attacker can add fake relationships to avoid detection by the classifier. Second, we propose a generative adversarial network (GAN)-based algorithm named VGAE-MalGAN to attack the graph-based GNN Android malware classifier. The VGAE-MalGAN generator generates adversarial malware API graphs, and the VGAE-MalGAN substitute detector (SD) tries to fit the detector. Experimental analysis shows that VGAE-MalGAN can effectively reduce the detection rate of GNN malware classifiers. Although the model fails to detect adversarial malware, experimental analysis shows that retraining the model with generated adversarial samples helps to combat adversarial attacks.
Double-SP Method to Enhance Privacy and Security of Smart City Applications

Smart cities promise a lot of well-being to their users in all areas of life through millions of applications and services. Smart services rely heavily on collecting data and the preferences of users. But on the dark side, the users' information is posed to threat and penetration during transmission or stored far in the clouds. Depending on encryption only is not sufficient if the attacker has strong resources or if the attacker is the service provider (SP) itself. In addition, changing data before sending is not a practical solution in many systems because of the adverse impact on the quality of a main service. This research presented a new idea to address the issue of protection. The proposed method enhanced the privacy and security of users' data without affecting the accuracy of service. The core of the suggested solution relies on a knowledge base of services that are managed by experts. Also, the solution depends on fog nodes to measure the level of security and privacy of users' queries without delay. Moreover, the fog nodes manage contact with SPs. Finally, the proposed method divided the SP into two, one for user queries and the other for user data. The simulation and analytical discussion on a practical case in smart cities demonstrated the superiority of the proposed approach over previous methods in the level of protection by maintaining the quality of services, and the resistance to attacks.
Sales Forecasting: A Comparison of Traditional and Modern Times-Series Forecasting Models on Sales Data with Seasonality

The adoption of advanced technologies is crucial for the efficient operations of modern businesses. Sales prediction, demand forecasting, production planning, and inventory forecasting are essential elements of business planning. This study compares classic and modern sales forecasting models to create a reliable and generic prediction tool for businesses and aid CEO decision-making. On sales transactional data, Exploratory Data Analysis was conducted to understand the features in the dataset. After data wrangling, the processed data was used to train the various forecasting models. To help in business strategy decision-making, different forecasting models were applied to the given dataset and the behavior and accuracy of each model were studied. At first, the traditional models based on moving average, Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average (SARIMA) were utilized, followed by the use of modern sales forecasting models including Long Short-Term Memory (LSTM) and its variants, and Fb-Prophet. The Root Mean Square Error was utilized as the performance measure to compare the various models. The results of the study showed that SARIMA models are better for organizations with fast-moving sales and seasonal patterns. While SARIMA outperforms the basic LSTM model, it is possible to enhance LSTM models to surpass current sales forecasting methods in the future.
Neural Machine Translation: A Survey of Methods used for Low Resource Languages

Machine translation (MT) is a maj or natural language processing subfield that is designed to automatically translate human spoken languages. Neural machine translation (NMT) has gained significant achievement in recent times and is becoming the emerging dominant approach in actual MT systems. The availability of significant quantities of parallel data is critical for the performance of Neural Machine Translation (NMT) models. A key challenge in machine translation for many language pairs is a dearth of parallel corpora. An important research concern in the field of machine translation is enhancing NMT accuracy for languages with limited resources. Extensive research is being done to enhance Neural Machine Translation accuracy by leveraging a limited parallel dataset. In this paper, we try to provide a broad and well-structured review of the methods used to enhance the translation accuracy of low-resource languages by exploiting monolingual data, which is typically easy to get and is available in a considerably higher amount, and further classification in two broad categories according to the adjustments they required to the NMT model's architecture is done. We believe that our survey will be useful to new students starting in this field.
Singularities of Solutions to Quadratic Vector Equations on the Complex Upper Half‐Plane

Let S be a positivity‐preserving symmetric linear operator acting on bounded functions. The nonlinear equation −1m=z+Sm with a parameter z in the complex upper half‐plane ℍ has a unique solution m with values in ℍ. We show that the z‐dependence of this solution can be represented as the Stieltjes transforms of a family of probability measures v on ℝ. Under suitable conditions on S, we show that v has a real analytic density apart from finitely many algebraic singularities of degree at most 3.
Plant Leaf Diseases Severity Estimation using Fine-Tuned CNN Models

The quantity and quality of agricultural harvests are both severely impacted by crop diseases. Accurately measuring the severity of a disease is vital because it allows farmers to use the appropriate amount and kind of pesticides on the crops that are threatened. Incorrect estimates of disease severity in plants can lead to wasteful or ineffective use of pesticides, making it a difficult assignment even for researchers and plant pathologists. Recently, there has been a rapid rise in the application of machine vision and deep learning methods to smart farming. To guarantee food security, we need to raise output and improve crop quality, but doing so requires a more precise and innovative method of assessing the severity of the crop disease. This research proposes a transfer-learning based strategy for estimating the severity of diseases on tomato leaves with the use of learned VGG-16 / VGG-19 CNN networks and tested on a hybrid dataset consisting of both images captured in the field with a Canon EOS 1500D camera on a white background and images captured under controlled laboratory conditions from the plant village dataset. In addition, the authors made hyper- adjustments to the hyperparameters of pre-trained CNN models to boost their efficacy. To evaluate the efficacy of finely tuned CNN models, the study uses accuracy and loss measurements over multiple iterations on training and validation datasets. When compared to another CNN model evaluated on the same dataset, VGG-16 was shown to obtain superior classification accuracy (92.46%).
Automated Test Case Generation Using T5 and GPT-3

Test case generation for a given topic can be a challenging and time-consuming task, particularly when the conversation about the topic includes boundary conditions and requirements. This process requires a complete understanding of the system and its scenarios to cover all possible examples, both good and bad. This paper proposes an automated solution for generating test cases using natural language processing techniques. The proposed methodology uses T5 and GPT-3 models to extract the context and topic of the conversation and generate test cases. The model identifies relevant keywords and generates test cases based on them, providing context-based meanings of specific words and defining new terms used in the conversation. The proposed approach eliminates the need for manual test case generation and allows for the testing of more complex systems with a larger number of scenarios. This will reduce the dependency on the expertise of the test designer, resulting in complete coverage of all scenarios. Additionally, this automation will save time and resources, making the process of test case generation more manageable. By utilizing NLP techniques, it will help to ensure that all scenarios are taken into account and that the test cases generated are comprehensive and accurate.
Bronchopneumonia Diagnosis Using Convolutional Neural Network

Bronchopneumonia is a potentially fatal bacterial infection caused by Streptococcus pneumococcus that affects one or both lungs in humans. The majority of the children affected were under the age of two. Children who have bronchopneumonia should be treated as soon as possible to help them recover. In order to reliably identify pneumonic lungs Convolutional neural network methods are utilized in this research using chest X-rays. These models can be utilized by medical professionals to treat bronchopneumonia in the real world. The Lung X-Ray Pictures (Lung infection) dataset from Kaggle was utilized in the experiment. To diagnose bronchopneumonia, radiotherapists with particular expertise must evaluate lung X-rays. In order to treat Bronchopneumonia rapidly, especially in distant places, it would be useful to develop an autonomous system. CNN has gained a lot of attention for its research in diagnosing diseases since deep-learning algorithms are adept at analyzing medical imagery. Image classification tasks immensely profit from functionalities obtained from 18 large-scale datasets. According to statistical findings, it can be very helpful to analyze an X-ray of the chest in order to identify bronchopneumonia using pre-trained CNN models and supervised classifier algorithms.
The Role of IOT in Health/Patient

Today it is an era of fast extending technology if we want to overcome from various challenges we need to upgrade ourselves. Different challenges are emerging day by day in healthcare sector also. Due to lack of doctors, health workers and medical researchers health care sector is lagging behind, to overcome from these health issues we need to upgrade ourselves with new technology like IOT based medical devices. IOT based medical devices will bring a revolutionary change in healthcare sector which will manage the health issues so efficiently which will save time and money both. IOT based devices will be connected with biosensors which will collect required data of human body and will send it to cloud computing for further diagnosis. These sensors will be embedded with various physiological and normal reading of human body, which will be interconnected with internet technology. Using IoT based medical devices which will save waiting time and cost reduction. Main motive of this report paper is to facilitate and introduce the benefit of IOT based medical devices in the healthcare sectors to diagnose and save life of patients.
Design and analysis of a Foldable Miniaturized Single Band Origami Antenna for Space Applications

Next-generation systems in airborne, spaceborne, and terrestrial applications are expected to greatly rely on deployable reconfigurable high-performance antenna systems. This research paper introduces a novel design concept for an origami antenna that is both foldable and physically reconfigurable. The antenna is optimized for use in the 2.45 GHz industrial, scientific, and medical (ISM) band, making it suitable for a range of industrial, scientific, and medical applications. The antenna is modeled and analyzed using the finite element method-based Ansys Electronics Desktop. To be deployed for foldable and conformal applications, the antenna is designed on the magic cube made of flexible material (Kapton polyimide) with εr = 3.5, loss tangent = 0.002, and patch thickness of 0.12 mm. The radiator is designed on the folded magic cube resulted in a miniaturized volume of 30 × 30 × 30 mm3. The origami-based antenna proposed in this study boasts several key features, including compact size, flexibility, foldability, wide bandwidth, bi-directional radiation pattern, and a high gain value of 2.7 dBi. These unique features position the proposed antenna as a strong candidate for use in various applications such as space exploration, medical settings, military operations, and rescue missions.
Predictive Analytics on Gender Classification using Machine Learning

The present study suggests a statistical Machine learning method to predict gender based on facial features using different facial features of a person. The dataset contains eight variables, of which seven are independent, and one dependent variable and the values are in categorical format, i.e., 0's and 1's. It contains two values, i.e., either male or female. Facial features such as nose length, nose width, forehead height, forehead width, are the most important for the determination of gender. For human recognition, we analyse many features in AI, such as iris, thumbprint, faces, voice, etc., for unique identification [1]. However, over all these features, facial representation is an important metric for Gender classification. Every individual has unique face metrics, but the similarity between males and females is determined through the measures of features. Analytical factors like race, gender, and age can be extracted from facial features. Thus, a successful gender classification approach can be a base and boost the performance of many machine learning applications like human recognition, biometric verification, and intelligent human-computer interfaces. Recognising gender through facial features can become unvarying due to the high variance in the illustration and poses of the faces [2]. In this analysis process, we have implemented various machine learning algorithms concerning classification, such as Logistic classification, Neural Networks, SVM, KNN, Decision tree, Random Forest, and Boosting algorithms. We have compared the accuracy results of all the algorithms applied and finally found the in-Boosting Gradient boosting algorithm fits the dataset best with high accuracy. We have used the python programming tool for Machine learning. Based on the best-fit algorithm, we have further processed data for predictions.
Robustness Analysis of Zero-Knowledge Proofs using QRP for IoT Devices

We attempt to study the time complexity and asymptotic security of the method in order to robustly analyze the security mechanism. Security analysis studies allow us to determine whether an algorithm is efficient or not. Security analysis studies also help us check whether algorithms tolerate different attacks on them or not. This research paper presents the robustness analysis of the zero knowledge proofs (ZKP) technique using the quadratic residuosity problem (QRP) as a superior alternative for Internet of Things (IoT) devices. If we want to achieve the main security issues (attacks on confidentiality, authentication, integrity, and availability) for IoT devices, then we need to know the security strength of the security mechanism we implement for it. The zero-knowledge proofs technique has been used with the QRP problem, and it is analyzed and compared with Elliptic Curve Cryptography (ECC) in order to see how they are correlated with each other. This study helps us know whether ZKP using QRP can address the security issues of IoT devices in a better way or not. The final covariance and correlation results show that both ZKP using QRP for the square root modulo n problem (SQROOT) and ECC are highly correlated with one another. Asymptotic time complexity and the graph of ZKP using QRP for the SQROOT algorithm compared to ECC clearly show that QRP for the SQROOT algorithm has not as good a time complexity as ECC but can address the security issues required by IoT devices.
Digital Health System for Individualised Rehabilitation After Ankle Inversion Trauma

Ankle injuries after inversion trauma are common and they can have serious consequences. Studies report a 60% reduced risk of re-injury and enhanced outcomes after training. Elements of such training can be performed in the home environment. Existing e-health solutions offer independent or supported rehabilitation after ankle inversion trauma. However, the monitoring and individualisation of the exercise programs are limited and cognitive factors are not addressed in those e-health solutions. The digital system presented in this study aims to provide individualised physical and cognitive rehabilitation, which helps patients after ankle inversion trauma return to work or sport in an effective and efficient manner and reduce the risk of chronic disability. The system can be used independently by users by means of a mobile app or accompanied by a health professional, who can monitor her patient on a web page dashboard. Information regarding the injury, rehabilitation process and injury coping strategies is provided. Additionally, exercises affecting ankle range of motion and muscle strengthening are included. Validated questionnaires and simple assessments are incorporated to determine the user's physical and cognitive baseline level, design an individualized exercise program and monitor the user's progress.