Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Dynamic comparator using CMOS and FINFET technologies

A two-stage and three-stage dynamic comparator architecture is proposed using FINFET technology for high speed, low power dissipation and comparison is done between three-stage and two-stage dynamic comparators with and without tail transistors. The designed comparator compares the two analog input voltage signals and generates the greater signal as the output. FINFET technology is used to overcome the short-channel effects in CMOS. In this paper the FINFET is designed with different supply voltages and analyzed power dissipation and delay. The comparison with tail and without tail of two-stage and three-stage comparators specifies that the delay and power dissipation has been reduced greatly with tail transistor. Two-stage, Three-stage Dynamic Comparator are designed and simulated in HSPICE using 16nm and 20nm CMOS and FINFET technologies.
A Comprehensive Review on the Use of Artificial Intelligence in Mental Health Care

The integration of Artificial Intelligence (AI) technology into mental health services has emerged as a promising way to improve the quality and accessibility of mental health services. In the field of analysis, Artificial Intelligence techniques like Natural Language Processing (NLP) and Machine Learning algorithms have shown great potential for the analysis of big data such as electronic medical records (EMRs) and social media posts. This technology can help identify patterns and symptoms of various mental disorders, facilitating early detection and intervention. AI also plays an important role in mental health diagnosis. Machine learning algorithms trained with extensive data enable accurate and efficient classification and diagnosis of different diseases. Additionally, AI-powered virtual assistants and chatbots are being developed to provide initial assessment, psychoeducation, and even treatment, thereby expanding access to mental health services. Healing is another area where intelligence shows promise. Intelligent algorithms can analyze patient information, past medical results, and evidence-based guidelines to recommend personalized treatments. In addition to this, techs such as VR-AR (virtual and augmented reality) and digital therapy combined with artificial intelligence are collaborating to provide treatment and improve the patient experience. AI programs continue to advance the care and management of mental health. Wearable devices equipped with sensors can collect physical and behavioural data in real-time, and this data can be analyzed by intelligent algorithms to detect early signs of relapse or flare-up. This proactive monitoring allows for timely intervention and personalized care, ultimately improving patient outcomes.
Time Series Forecasting of Agricultural Products Sale Using Deep Learning

Due to the massive production of data, the time series dataset is useful for time-based prediction as it holds time related information. Different forecasting techniques help to predict in the field of the stock market, sales, healthcare, banking, weather etc. The coming out of new companies has increased every year, and they have taken part in the competition with other existing organizations with their products and services. As the competition is growing day by day, sales analysis is a must for every organization to make a strong position in the competitive market. In that case, product and area-wise sales analysis can help a company to attain its aim by accomplishing its target. The success of a business mainly relies on the sales of its product. Predicting the sales may help the company to discern the amount of growth rate. Moreover, this research target is to estimate the production amount for their next manufacture. More precise predictions can give fame and make companies successful. Though it is a challenging task, with the help of machine learning algorithms this issue can be sorted out effectively. In this research, we have analyzed the product selling rate and used Multi Step LSTM for forecasting the sales of the agricultural product for the next month sales.
Development of a Depression Detection System using Speech and Text Data

Depression is a prevalent mental health condition that impacts a considerable proportion of the world's population. However, early detection and intervention are crucial for effective treatment. Current depression detection models often rely on a single modality, either speech or text data, which may lead to inaccurate results due to the limited information being considered. In this paper, we propose a novel approach for the accurate detection of depression in individuals using both speech and text data. This approach addresses the limitations of existing solutions by combining objective measures of speech and text data, providing a non-intrusive and efficient means for depression detection. In this project, two models were trained: a speech emotion recognition model using Long Short-Term Memory (LSTM) and a text model based on the Random Forest Method. The TESS dataset was used to train the speech emotion recognition model, whereas the text model was trained on the Twitter dataset, which is accessible on Kaggle. The results indicate that our proposed approach is highly effective in detecting depression, with an accuracy of 98% achieved by the speech emotion recognition model and 95% by the depression detection model on the testing data. The outputs of both models are combined using a decision tree method, resulting in an accuracy of 100%. The proposed method employs a Decision Tree algorithm that takes the output of both the Speech Emotion Recognition (SER) model and the text-based depression detection model as inputs and applies a set of rules to classify users into one of three categories: depressed, mildly depressed, or not depressed. Then a webpage was developed where users can input their speech and text data and receive a prediction of their depression status based on the integrated output of both models. Overall, the results demonstrate that the proposed system provides an assuring solution for the early diagnosis of depressive symptoms.
Deep Learning-Based Brain Tumor Detection and Classification of MRI Images

Brain tumors are a common cause of death, ranking 10th among various causes. They can affect both adults and children, characterized by abnormal cell growth within the brain. These tumors can be classified based on location, behavior, and morphology. Accurate classification is crucial for effective patient management and treatment, as misclassification can have severe consequences. Magnetic resonance imaging (MRI) is commonly used for tumor detection, and automated methods using convolutional neural networks (CNN) have emerged in recent years. In our research, we propose a CNN-based approach for classifying brain tumors using MRI scans. Our focus is on the multi-class categorization of glioma, pituitary, meningioma tumors, and the absence of tumors. We train our CNN model from scratch and evaluate it with K-fold cross-validation for improved accuracy. Evaluation is performed on a publicly available dataset from Kaggle, comprising MRI scans with the four classes. Our proposed method achieves a 94.78% accuracy in multi-class classification, demonstrating the effectiveness of CNN for brain tumor classification. This approach aids doctors and radiologists in making accurate diagnoses, ultimately improving patient outcomes.
YOLO v5, v7 and v8: A Performance Comparison for Tobacco Detection in Field

Accurate tobacco plant detection is crucial for effective agricultural management strategies. Due to the challenges posed by high plant variability, adverse lighting conditions, and occlusions, existing machine learning algorithms and convolutional neural networks (CNNs) have struggled to achieve satisfactory detection accuracy under real-world scenarios. This paper employs YOLOv8, a state-of-the-art object detection algorithm, to reliably detect tobacco plants. A comprehensive evaluation and benchmark of 12 state-of-the-art YOLO object detection algorithms including the YOLOv8, is established for tobacco detection. YOLOv8 demonstrates remarkable test accuracy of 96.4compared to YOLOv5 and YOLOv7. Performance comparisons with YOLOv5 and YOLOv7, two ancestor algorithms, using test images further solidify YOLOv8’s superior detection capabilities for tobacco plants. YOLOv8’s real-time object detection ability renders it an ideal solution for mobile and embedded devices, opening new possibilities for on-the-go agricultural management.
A Review and Evaluation of Novel Object Detection Algorithms for Autonomous Driving

Semantic segmentation is an upcoming and important part of research in the field of Computer Vision and Deep learning. It involves the task of identifying different objects in an image and assigning classes to them. This particular exercise is very useful for autonomous driving applications in which identification of the objects in the vehicle’s environment is of paramount importance. To that extent, we choose KITTI autonomous driving data-set as our research interest and focus on reviewing the four semantic segmentation models from the KITTI Dataset leaderboard, based on their IoU (Intersection-over-Union) score and suggest improvements to one of the models to improve its score. We also present an analysis to explain each model’s superiority over another in terms of performance and suggest changes in the network architectures, loss functions, and optimizers to further improve each model’s IoU score.
Partial Dominance: A New Framework for Top-k Dominating Queries on Highly Incomplete Data

The Top-k dominance locates the top k objects that happen to be most dominant across a dataset. It makes it possible for data analysts to unearth a previously unknown pattern known as dominant objects, which may be put to use in a variety of applications including decision support systems, suggested system, etc. Hence, TKD becomes an important decision making tool for many organizations. To dominate a record over another record, it is necessary for the the record to be dominated by all the attributes in the dataset. But it can happen that one entity A dominates most of the attributes of another entity B and B dominates A by a very small number of attributes. In this circumstance it can not be said that A dominates B or B dominates A using traditional dominating score computation scheme. When none dominates, traditional top-k dominating query cannot find top k objects for such datasets. This study proposes a framework to deal with such datasets that computes top k dominating query more accurately and more realistic manner form any kind of dataset. In order to make the solution realistic, this study incorporates the concept of partial dominance that can calculate the dominating objects from such dataset. The non-candidate objects are eliminated using the concept of data bucketing. The experimental result shows that the proposed model outperforms existing important approaches for top-k dominant query.
Design of Hexagonal Wideband Microstrip Patch Antenna for MIMO Wireless Applications

In this work, a four-port MIMO structure has been proposed to achieve improved isolation and operate in the ultra/super-wideband (UWB) frequency range. The projected designs comprise hexagonal rings embedded with UWB hexagonal units with a partial ground plane defect. The alteration of the hexagonal patch and ground planes modifies the current flow and yields in realizing a wider fractional bandwidth. The triple-band notches are created on conventional hexagonal geometry using rings. The proposed unit cell is centrosymmetric geometry and it resonates from 2.62 − 20.64 GHz with fractional bandwidth of 155%. The proposed four-port MIMO configuration has physical dimensions of 0. 86λ × 0. 86λ × 0. 017λ and it resonates from 2.70 − 20.70 GHz with fractional bandwidth of 153.85%. The distinctive feature of a unit cell and MIMO geometry is incorporating a simple effective ground defect structure to generate the isolation of 18 dB over the resonating spectrum. Further, the MIMO diversity characteristics have been performed and achieved good performance which indicates the proposed geometry is appropriate for wideband communication systems.
An Flexible Wearable Compact Metamaterial-loaded monopole Antenna for Wireless Body Area Network

In this feild, the challenge is to construct a flexible antenna with good performance in the wearable environment. In this research work, a novel metamaterial loaded antenna is proposed to work in the wireless body area network. By employing a split ring resonator in the ground plane, we are able to obtain reduced size. The size of antenna is 25 mm x 19 mm x 0.3 mm with polyamide substrate making the antenna flexible. The obtained directivity in dB is 3.0529, with a good impedance matching. The proposed antenna is tested on cotton cloth textile material. In addition to this, the obtained specific absorption rate is 0.9 W/kg that is within the permissibe limits. No major difference is seen between the simulated and measured return loss. The return loss is tested with N9926A FieldFox Handheld Microwave Analyzer, 14 GHz. The simulated and measured return loss have been found in good agreement.
Improving Skin Disease Diagnosis with Deep Learning: A Comprehensive Evaluation

One of the deadliest maladies is skin disease. Despite it could be challenging to diagnose dermatological problems precisely. In the present study, deep learning will be used to pinpoint the skin problem described earlier. Such a tool would be more efficient than manual procedures, which are time-consuming and call for expert help. This paper's main goal is to provide an in-depth analysis of conceptual review of current advancements in deep learning-based skin disease identification. Although deep learning is gaining popularity, there are still many problems to be solved and much more study to be done. The present manual procedures for diagnosing skin diseases are known to be time-consuming because they rely on professional judgement.
Driver Alertness and Awareness Monitoring System

A lapse in driver alertness or awareness of surroundings is one of the leading causes of road accidents. Driving while distracted or drowsy triples the risk of loss of control and vehicular accident. With developments in computer vision and deep learning, it is now possible to monitor drivers using a smartphone camera and avoid this risk. We describe a method that uses smartphone cameras to monitor driver alertness and awareness. The system compares the driver’s face to the photo on his or her driver’s licence. It detects the drowsiness of the driver using a real-time video stream. It also classifies driver actions into 9 commonly occurring distraction categories or as safe driving. The front facing video stream of the driver’s phone is also used for monitoring road safety behaviour by estimating the distance to surrounding vehicles on the road and other risks such as pedestrians and signal violations. We have also developed a model that can classify the roads into one of the 4 categories - Good, Poor, Satisfactory and Very Poor using CNN. Lastly, the system alerts drivers to the possibility of increased accident risk based on various factors such as location, time of day, etc. Monitoring drivers and alerting them to possible loss of awareness significantly reduces road accident risk.
A Survey: Authentication Protocols & Security Analysis for Wireless Sensor Networks

Wireless sensor networks (WSNs) are collection of small devices equipped with sensors, microprocessors, and wireless communication capabilities. These devices are employed for the purpose of sensing, collecting, analysing, and wireless transmitting data via radio waves. WSNs are applied in various fields, including environmental surveillance, defence, healthcare, and industrial process control. However, WSNs are vulnerable to security risks, particularly in remote and hard-to-reach locations. To ensure security, multiple authentication techniques have been developed, and the present study focuses on security challenges and threats to WSNs, reviewing different authentication protocols currently used in WSNs. The survey highlights the growing interest in security and suggests several authentication methods that prioritize security, scalability, and reduced communication and computation overheads. The survey concludes that authentication protocols play a critical role in reducing the likelihood of attacks and ensuring secure data transmission in WSNs.
Ultra-Low Power Approximate Arithmetic Circuits

Multiplication and addition are the two paramount arithmetic operations in application specific ICs and DSP processors. The power consumption, area and performance are the major constraints in scaled CMOS integrated circuits. The computation using accurate computing techniques includes overhead in terms of area and time. The accurate computation is not the constraint for data mining, big data, AI, Internet of Things, deep learning applications. In such cases, it would be more viable and cost-effective if computing is done with the results are properly accurate is called approximate computing. So, a 4-bit approximate adder and multiplier circuits are implemented in this work using energy recovery logic at 45nm technology node using cadence virtuoso. Eldo simulator is used to perform functional verification. The combined use of energy recovery logic with approximate computing achieves about 50-75% power reduction over corresponding CMOS implementations. Further, analyzed the behavior of these circuits with supply voltage scaling and technology by varying the amplitude of power clock from 0.4V-1.8V and width of the transistor from 120nm-160nm. Along with this analysis another prominent design metric in SoC applications is Area Power Product (APP), which is also computed in this work
Cygiene: Cyber Hygiene Score App

Mobile devices have become an integral part of our lives, containing sensitive data and accessing critical services. However, mobile cybersecurity remains an afterthought for many users, leaving them vulnerable to attacks that compromise their privacy and security. Cygiene is a mobile app that evaluates the security of mobile devices. Cygiene generates an easy-to-understand security score based on factors like operating system updates, screen lock strength, installed risky apps, and more. It also provides actionable reports with remediation recommendations that guide users to improve their security practices. By raising awareness about vulnerabilities and helping users address security gaps, Cygiene aims to make mobile environments safer for everyone.
An Autoencoder-based approach for Finding Better Judgment from Crowd Opinions

In recent years, outsourcing a difficult problem to a crowd is becoming very popular to solve the task in a very efficient manner. During the last decade, several approaches have been considered to utilize the power of the crowd for solving various societal problems. However, a lot of challenges are present in deriving multiple opinions from the crowd. For example, while collecting multiple responses from the crowd, there exist some spammers that can try for various malfunctioning. Hence, aggregating this multiple feedback to find a possible decision is a difficult task and the proper method is designed for this. Although several approaches are available, in the current literature, however, most of the work employs an ExpectationMaximization (EM) algorithm for solving this kind of problem. Although in the majority of cases, a few types of goodness metric criteria such as crowd worker’s accuracy, biases, and question difficulty are used. On the contrary, here, we introduce a new way to represent the response matrix using an autoencoder with the aim to find the relevant features of crowd workers. Here, first, we extract some features from the response matrix comprising opinions of a crowd for different questions. After extracting the new features we pass the response matrix through an autoencoder to create a latent space representation of the response matrix. In order to process the response matrix, further, we take the latent space representation. Finally, we performed clustering on the latent space representation and find the aggregated decisions from different crowd workers’ opinions. We performed the experiment on three different real crowdsourced datasets and the experimental results demonstrate the effectiveness of the current approach.
A Simplified Method of Detection and Predicting the Severity of Knee Osteoarthritis

Knee osteoarthritis (OA) is a degenerative joint disease caused by the gradual wear and tear of the knee's articulatory cartilage. It is most typically found in adults. It is a progressive illness that can cause impairment if it is not treated or diagnosed in its early stages. This paper presents a simplified approach for detecting and predicting knee osteoarthritis based on the Kellgren-Lawrence (KL) grading system using Residual Networks. The baseline of the X-Ray images was obtained from Mendeley Data. The following four approaches are proposed for grading the severity of Knee Osteoarthritis: the first distinguishes severity according to the original KL Grading System, the second distinguishes normal (KL 0-I) from osteoarthritic knees (KL II-IV), the third distinguishes severity as normal (KL 0-I), non-severe (KL II), or severe (KL III-IV), and the fourth distinguishes severity level from KL Grade 0 to KL Grade III. The empirical results showed that the performance improved with fewer multiclass labels being used, with binary class labels and three class labels surpassing all the other models proposed in this paper. The model for binary class labels achieved a 90% accuracy rate and weighted Precision, Recall, and F1 scores of 90%, 90%, and 90%, respectively whereas the model for three classes labels achieved a 90% accuracy rate and weighted Precision, Recall, and F1 scores of 90%, 90%, and 89%, respectively. The proposed models also showed how they might be used to classify patients early in the course of the disease, slowing its progression and raising their quality of life.
A Relative Weight-Based Fuzzy k-Nearest Neighbour Classifier for Chemical Analysis of Wine and Glass Identification

The nearest neighbour (NN) classifier is a widely used classifier that classifies a test pattern based on the class of its closest neighbour. The NN classifier has several variants, namely k-NN, modified k-NN (Mk-NN) and fuzzy k-NN (Fk-NN). These variants classify a test pattern using some or all of the training patterns. The k-NN classifier is different from the Mk-NN classifier in terms of weight, whereas the Mk-NN classifier is different from the Fk-NN classifier in terms of membership value. This paper introduces a new classifier called relative weight-based Fk-NN (RWFk-NN) by taking advantage of Mk-NN and Fk-NN classifiers. It also presents a comparative analysis of these classifiers using three techniques, namely k-fold cross-validation, k-test analysis and m-test analysis, on the wine and glass identification datasets from the University of California Irvine (UCI) machine learning repository. The performance of all the classifiers is measured in terms of accuracy. The findings reveal that the RWFk-NN classifier outperforms all the other classifiers in both datasets.