Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Crop on the basis of Climatic Parameters using Machine Learning Approaches

India as a country is largely dependent on agriculture. New techniques are being implemented by government and farmers for increasing crop production and crop yield. AI and machine learning are most popular technologies that are being used by government and big private firms for predictions in various sectors like agriculture, healthcare, auto mobile etc. The yield is dependent on factors like weather, fertilizer, pesticide, soil and rain. This article discusses about implementation of machine learning models in agriculture where we are predicting crop that is suitable for the soil at a particular place keeping in mind nutrients and weather of that place. This work will help farmers who are new and will guide them for showing crops by the help of machine learning, which is most advanced technologies to predicates the crop for future forecasting.
Design of CMUT Cells for High-intensity Focused Ultrasound Applications

The advancements in fabrication technology have led to significant developments in MEMS -based capacitive micromachined ultrasonic transducer (CMUT) devices, which have several ultrasound applications. This work discusses the modeling and simulation of CMUTs to derive ultrasound frequencies suitable for HIFU applications using COMSOL® Multiphysics. It can be used for catheter-based monitoring of diseased tissues ablation in the human body. Polysilicon and SiO2 have been used for the top membrane. The bottom membrane is made of silicon for the circular CMUT cell. We performed eigen-frequency studies and pull-in voltage inspection of the CMUT device. The analytical calculations are performed using MATLAB®.
A Novel Technique of Enhancing CMRR of Fully- Differential Instrumentation Amplifier Using High Gain CMFB Loop

In this paper, the enhancement of the common- mode rejection ratio (CMRR) of a fully-differential capacitively-coupled chopper instrumentation amplifier (CCIA) used for biomedical signal acquisition system is proposed. To achieve the gain accuracy of the instrumentation amplifier (INA) high gain multi-stage fully-differential operational amplifier (Op-Amp) has been used as the building block. The enhancement of the CMRR is achieved by using common-mode feedback (CMFB) circuit with very high loop gain. The high CMFB loop gain has been achieved by suitable selection of the CMFB circuit and the connection of the CMFB circuit to the multi-stage Op-Amp. The entire circuit has been designed using 180 nm technology with 1.8 V supply. The designed multi-stage Op-Amp has a low-frequency differential mode (A ) and common mode ( ) gain of 123.5 dB and - 105.02 dB, respectively, using a CMFB loop having a gain of 136.3 dB. Using this Op-Amp the designed CCIA achieves a CMRR as high as 145.1 dB with Ad of 40 dB @3dB bandwidth of 10.38 KHz. Finally, the response of the CCIA has been compared with some of the state-of-art designs.
An anisotropic denoising algorithm for mesh models

Mesh denoising is an important tool for geometric processing. Existing methods still suffer from feature blurring or shape degeneration. To address this problem, we present a new anisotropic denoising algorithm which is based on global optimization. Compared with isotropy, anisotropy is more robust to the irregular sampling of 3D meshes. We define a weighting scheme, which contains three factors: area, position, and normal. Moreover, the global optimization problem is solved alternatively and iteratively to obtain denoising result. Various experiments and analysis show the advantages of the proposed algorithm. Our algorithm can remove noises effectively, and preserve mesh features such as sharp edges and corners robustly.
Research on the construction of new power system value evaluation index system

Since the ninth meeting of the Central Committee of Finance and economics proposed to build a new power system, provinces have been building a new power system in full swing. The new power system is a new thing, so far there is no recognized value evaluation standard. Based on the understanding of the connotation and characteristics of the new power system, this paper analyzes the value of the new power system, studies and puts forward its value system, and constructs the value evaluation index system of the new power system.
DEVELOPMENT OF COUPLED FE/BE MODELS TO INVESTIGATE THE STRUCTURAL AND ACOUSTIC RESPONSES OF A SUBMERGED VESSEL

An analytical model and a fully coupled finite element/boundary element model are developed for a simplified physical model of a submarine. The submerged body is modeled as a ring-stiffened cylindrical shell with finite rigid end closures, separated by bulkheads into a number of compartments and under axial excitation from the propeller-shafting system. Lumped masses are located at each end to maintain a condition of neutral buoyancy. Excitation of the hull axial modes from the propeller-shafting system causes both axial motion of the end closures and radial motion of the shell, resulting in a high level of radiated noise. In the low frequency range, only the axial modes in breathing motion are examined, which gives rise to an axisymmetric case, since these modes are efficient radiators. An expression for the structurally radiated sound pressure contributed by axial movement of the end plates and radial motion of the shell was obtained using the Helmholtz integral equation. In the computational model, the effects of the various influencing factors (ring stiffeners, bulkheads, realistic end closures, and fluid loading) on the free vibrational characteristics of the thin walled cylinder are examined. For both the analytical and computational models, the frequency responses, axial and radial responses of the cylinder, and the radiated sound pressure are compared.
Survey on Smart Home Automation Systems: Tools, Technology, Architecture and Communication Interfaces

The ascent of the Internet of Things (IoT) is noteworthy in beyond a couple of many years. Its growth affirms its use in various application areas i.e.: agriculture, healthcare, home automation, transportation, etc. Home Automation means extending the current activities performed inside the home with the help of developing cost effective powerful computational devices with user friendly environment by using different technologies like Wi-Fi, Bluetooth and ZigBee. These communication technologies provide controlled environment by different devices like tablet, smart phone and laptop. The Home Automation embeds the smartness in the home with the objective of operating it without any physical interference of human being. Thus, the objective of this paper is to analyse the growing trends of IoT devices to uncover the popular and growing application area i.e.: smart home environment and to have the detailed study of hardware/software tools, architectures and communication interfaces of selected IoT application area like development board, sensors, actuators, communication type, cost, development environment etc. The comparative literature study of selected application area includes the challenges and future research aspects. It will help the researchers to consider various parameters in most favoured IoT application area as per user needs.
Implementation of a Digital Twin Framework in the Modular Housing Industry

The construction industry is one of the minor digitized industries, and it has been characterized by a below- average increase in productivity in recent decades. The modular housing sector copies the production system of the manufacturing industry to meet these deficits, which makes it a perfect example for evaluating Industry 4.0 technologies. This paper explores the methodology and possibilities of implementing a digital twin of a modular housing production plant to optimize its production. The proposed approach based on a Digital Twin (DT) framework consists of three consecutive parts: (1) analyzing the production system by using the Lean tool of Value-Stream Mapping extended (x-VSM) towards Discrete-Event Simulation (DES) models, (2) collecting real- time data to track and trace the system's bottleneck by using Radio-Frequency Identification Devices (RFID) as a Tracking- and-Tracing system (TaT), and (3) optimizing it by using DES. Three scenarios to eliminate waste, increase throughput, and identify the best possible TaT implementation have been simulated. The results are a 64 % inventory reduction, a 20 % faster production lead time, and the finding of the critical part to be tracked using RFID. The resulting model has shown to be a valuable instrument for understanding the different process interdependencies. Moreover, it has been used as a support tool for middle management to assess the impact of different optimization approaches quantitatively. Overall, this paper shows the implications of combining DES and TaT systems towards a DT of a modular housing production plant.
A Review of Recent Advances in Classification and Prediction of Brain Tumor using Deep Learning

The latest developments in deep learning to identify brain tumors are reviewed in this article. The aim of this bibliographic review is to provide a thorough understanding of the most current breakthroughs to the researchers that decide to work with deep learning and artificial neural networks for cancer diagnostics. Brain tumors are categorized into two terms malignant (cancerous) and benign (non-cancerous), just like other tumors based on the critical stage. While benign tumors expand slower and rarely spread, malignant tumors grow considerably quicker than benign tumors and frequently do so into the surrounding brain tissue. Only malignant tumors are regarded as cancerous, however benign brain tumors can still pose a threat because of how their growth may affect nearby brain tissue. It may cause death if not treated in the initial phases. Although there have been many substantial efforts and promising results in this field, precise segmentation and classification remain difficult tasks. Because of the differences in tumor location, shape, and size, detecting brain tumors is extremely difficult. The use of AI in medical research has improved the precision and accuracy of brain disease prediction and detection. In this article, we’ve analyzed methods such as CNN, FCN, U-Net, and others, based on the metrics of accuracy, sensitivity, specificity, precision, and F1-score, which aid in assessing the effectiveness of the algorithm used.
Ensemble Learning on a Weak Correlated Android Malware data using Stratified K-Fold

In Android apps communicates with other apps by using Intent or PendingIntent. An Intent enables Android applications to share information between apps (like data, action, etc.,), and the PendingIntent delegate’s authority to other apps to perform the required action in the future. Android supports apps to collaborate with any $3^{\mathrm{rd}}$ party apps using a flexible communication model called Implicit Intent-based Communication. Though this communication channel is effective in collaboration it is unprotected and unsafe by default. Any application (even malware) can register to this implicit channel, and thereby can sniff the intents exchanged through the channel, making it vulnerable to malware attacks. In case, if an app is exchanging its sensitive data like GPS location or exchanging PendingIntent using implicit intents, in turn, this leads to unauthorized access and privilege escalation attacks. In this paper, we leverage the machine-learning techniques for security predictions in order to identify such possible threats from the apps’ binary inspection, and thereby our research can assist cyber forensic tools to identify the vulnerabilities caused by dynamic characteristics present in an application before executing the application itself. This paper presents a statistical model to analyze the malware nature of a mobile application: (1) based on the PendingIntent Flag usages, and (2) based on the type of Broadcast across apps. Our app classification achieved an F-score of 78.7%.
Vision-Based Detection of Water Hyacinth

This research proposes a vision-based method for detecting the invasive aquatic weed water hyacinth, commonly known as Eichhornia crassipes. They flourish in moving water, including rivers, lakes, and streams. This plant can double in size and cover an entire body of water in a matter of weeks. By decreasing the amount of oxygen in the water, water hyacinth negatively impacts aquatic life. The surrounding water and soil are drained of nutrients as a result. Using computer vision and machine learning, this article presents a model for detecting water hyacinths. The research provides a method for extracting features from hyacinth images using the Gray Scale Co-Occurrence Matrix (GLCM), a statistical methodology of the second order. For feature vector compilation, the Haralicks characteristics contrast, energy, homogeneity, dissimilarity, and correlation are utilized. The LGBM (Light Gradient Boosting Machine) classifier accurately identifies hyacinths with 88% of accuracy.
Radiation Tolerant by Design 12-Transistor Static Random Access Memory

In this paper, we propose a radiation-tolerant by design 12 transistor (RTBD 12T) based Static Random Access Memory (SRAM) cell for deep space application. The designed SRAM cell can effectively tolerate radiation events. It exhibits high resilience towards radiation upsets and accordingly three out of four nodes of the cell are fully immune to radiation upsets, and it has a critical charge (QC) equal to 1.6 fC making it a more promising choice for deep space application. The design metrics of the proposed SRAM cell are compared with a few conventionally used SRAM cells like 6T, Quad node (QUATRO 10T) SRAM, Quadruple cross coupled storage cells (QUCCE 12T) and Writability Enhanced (WEQUATRO). The higher read stability is validated by the read static noise margin (RSNM) analysis in which the proposed cell achieves higher noise margin as compared to other SRAM cells. The proposed 12TSRAM cell exhibits improved results in terms of hold power, which is $\left( {0.86 \times } \right),\left( {0.40 \times } \right)$ and $\left( {0.74 \times } \right)$ compared to that of QUATRO 10T, QUCCE 12T and WEQUATRO, respectively.
A Lightweight DoS and DDoS Attack Detection Mechanism-Based on Deep Learning

Denial of Service (DOS) attacks are one of the major attacks on any network and a potential threat to internet resources and services. This threat amplifies with Distributed Denial of Service (DDoS) attacks as these attacks do not give any alert or time for the victim to act. With the increase in devices connected to the internet, the intensity and frequency of attacks are also growing daily. Existing solutions like intrusion detection systems(IDS) cannot provide better results with the complexity of DDoS attacks because their filtering criteria have been static to distinguish attack traffic from regular traffic. So, with the robustness of attacks, the solutions need to be robust enough. The proposed method here is to use the state-of-art CNN model, i.e. EfficientNet and ResNet. EfficientNet model being a lightweight model, can be integrated with any device to avoid DDoS attacks. Since the prediction time is minimal, the proposed method can pinpoint the attack to act immediately.
Infrared thermography as a nondestructive tool for materials characterisation and assessment

Thermographic approaches, passive and active, are widely used due to the outstanding advantages that offer in a number of applications and particularly for the assessment of materials. Nonetheless, there are limitations; depending upon the approach used, as well as on the materials thermal, optical and physical properties, proper assessment (detection and/or quantification) is feasible. In thermal non-destructive evaluation (NDE), the active approach of infrared thermography where an excitation source, such as optical flash lamps, heat lamps, hot or cold air guns, etc., is employed with the intention of inducing thermal contrasts, has several applications. The temperature differences during the transient phase appear on the material surface and so detection of subsurface defects is possible (areas of different temperatures when compared to the sound part(s) due to the different thermal diffusivity). Since the heating or cooling features of the stimulus source are identifiable (in time and amplitude) by considering the time factor quantitative assessment is also feasible. However, when a material is heated, the thermal waves penetrate the material's surface. These waves are generally of various amplitudes and frequencies and are launched into the specimen, in a transient mode (i.e. transient thermography). In this work, different applications, employing transient thermographic testing, concerning the assessment of various composite materials and components are presented. Real time NDE is presented using various transient thermography approaches, i.e. pulsed thermography (PT), pulsed phase thermography (PPT) and/or thermal modelling (TM).
Parametric and Fault Analysis of Next Generation High Speed Interconnects

With incessant advancements in very large-scale integration technology (VLSI) technology, system reliability has become a predominant performance parameter for device as well as interconnect. High accuracy in interconnect system is required to satisfy the quest of high-speed switching applications with low power dissipation. The state-of-art of VLSI interconnect perpetuates presence of various faults such as stuck-at, propagation delay, crosstalk, bridging etc. which can resist the performance of entire on-chip system. For proper functioning of circuit, interconnect materials, modeling techniques and testing approaches are needed to be explored and analyzed. This novel paper presents an exhaustive analysis of advancements and contemporary work done in the field of interconnect design and fault modeling. It includes the comparison of performance parameters of different types of interconnects.
Gene Co expression analysis for identifying some regulatory genes in human lung cancer

Cancer occurs owing to significant variances in the gene expression level. Like all cancer lung cancer is the result of abnormalities in the regulatory systems of genes. It is evident that, gene expression systems can successfully frame phenotypes. Hence, it is important to analyze co expressed genes that could shed light on the fundamental principles governing the transcriptional regulatory processes influencing tumor formation and evolution. Oligonucleotide microarray and sequencing technology advancements have made it possible to quickly monitor the expression of genes in different tissues, providing a great mechanism in cancer research domain In the present article we have designed an algorithm which is focused on analyzing the gene correlation values of human lung expression and studying the gene correlation network. The algorithm effectively identifies some regulatory genes having significant association with lung cancer.
A Systematic Review of Concepts and Future Directions of Platform Ecosystem Development

Research in platform ecosystem development has gained considerable attention in the last decade, building on technology and digital platforms, open, trusted, innovative engagement. Digital platforms are now considered everywhere and have become widely adopted in the business world as an economic community supported by a foundation of interacting organizations and individuals. This paper aims to identify the key concepts in the platform ecosystem development and further establish the study's future directions. This includes defining the key players and components arising when facilitators attempt to build a platform ecosystem. Hence, a systematic review was conducted to confirm current practice, identify variations in reports and further inform areas for the future research study. The study results focused on the conceptual, empirical, and practical structure of the available evidence in the literature. We understand the critical requirements before an ecosystem can emerge or evolve and provide long-term success for businesses in today's circular economy through this research.
ComicLib: A New Large-Scale Comic Dataset for Sketch Understanding

The sketch is essential in everyday communication and has received much attention in the computer vision community. In general, researchers use learning-based approaches to study sketch-based algorithms. These methods rely on large-scale data to train complex models to achieve satisfactory performance. Most existing datasets are drawn by unskilled users in a closed environment. These datasets are of low complexity, making deep learning models unable to extract more information. This paper proposes a new large-scale comic sketch dataset called ComicLib for sketch understanding. We scan 181,354 comic sketch images from the comic library and annotate them through a crowdsourcing annotation platform developed by ourselves. Finally, we obtain a dataset of millions of comic objects in 17 categories. We conduct comparative experiments on sketch recognition, retrieval, detection, generation and colorization using a number of deep learning algorithms. These experiments provide the benchmark performance of the ComicLib dataset. We hope that ComicLib can contribute to the field of sketch-based research.
Driver's Companion-Drowsiness Detection and Emotion Based Music Recommendation System

Recent advancements and development in Deep Learning models and frameworks have opened doors to many possible applications of the same in order to tackle much more complex problems. In this paper, we tackle two major problems-drowsiness detection and emotion detection and create a combined system that can detect both emotional and physical state of user, and respond appropriately by alerting the user when they are showing signs of fatigue, and detecting their emotions in order to recommend appropriate entertainment in the form of music that can positively influence their experience. The driver drowsiness model includes the use of face landmark shape predictor and dlib library to detect the state of eyes in real time. If the eyelid is left closed for a few seconds, an alert is generated. For expression detection, a Convolutional Neural Network (CNN) trained on the FER2013 dataset is used for feature extraction and classification to one of 7 emotions. The music recommendation model is built based on Russell's model which classifies emotions based on valence and energy values. We successfully created a system that can accurately judge drowsiness in user as well as detect emotion with an accuracy of 83% and recommend songs based on user's emotional state.
Feature Similarity and its Correlation with Accuracy in Knowledge Distillation

Knowledge distillation (KD) has emerged as a popular model compression technique to transfer knowledge from a larger, more performante teacher network to a more compact student network to improve its accuracy. Depending on the type of knowledge being transferred, KD can be categorised as follows: response-based, feature-based or similarity-based distillation [5], [26]. Inspired by Bucilua et al. [3], KD was originally proposed by Hinton et al. [8] as a response-based distillation technique which transferred the so-called “dark knowledge” by “softening” the teacher's prediction vector before distilling it to the student in image classification.