Miracle-dz/UniFAR-FTUs huggingface dataset:
Study on Artificial Intelligence in Healthcare

We have seen during this pandemic, India, there are many dangers to the healthcare system which calls for the need to encourage hospitals o switch an electronic healthcare information system from paper-based medical records to an electronic medical related data that offers e-healthcare information and integrates well with computer-based judgment making systems. With the growing interest in AI centered robust information technology (IT) infrastructure is critical in addressing such national issues as the need to improve the protection and quality of health care, as well as the rising health care costs in the health sector. Network technology and communication are presently noticeable improvements in information technology and to a great extent affect the medical services. The necessities of both patients as well as suppliers of healthcare/Medicare facilities shall be addressed. An AI enabled hospital or medical system is essential, challenging, and achievable. This paper focuses on the Medical, hospital related uses of IT, such as better clinical decision-making, imaging and history taking, reduced duplication of diagnostics, better reliability of medication, increased utilization of health facilities and preventive health initiatives.
Segmentation of Spinal Canal using Active Contour Model

Today, the lifestyle of people has paved the way for rise in spinal cord disorders. Severe cases have been reported which could be treated if diagnosed early. Scoliosis is a deformity to spine and ribs which is the primary cause of spinal curvature. The major challenge of scoliosis disease is the unnoticeable change in the orientation of spinal column at its early stage. Moreover, it is visually detectable only in the prodromal stage. The early diagnosis could help cure the disease through exercises and minor surgeries. Depending on manual diagnosis techniques is a tiring task as it can deliver inaccurate results. An automatic segmentation method which helps in the early diagnosis is proposed in this paper. Initially, CT image which is the input is fed into the system. CT images have high contrast between bone and adjacent tissues. Sagittal view datasets have been chosen in order to calculate the cob angle for the measurement of scoliosis intensity. Further, distortions are removed from the image and pre processing is performed followed by K-means clustering which detects the spinal canal. In order to segment the required features, the output of clustering is loaded to Active Contour Model. Finally, segmentation of spinal canal is completed. Experimental results prove the accuracy of 95%,86.86%,92.22% for Lumbar Vertebrae CT , Lumbar spine CT, Lumbar spine CT with multiple compression fractures respectively for the proposed system which is greater than traditional diagnosis methods. Subsequently, this would be a revolutionary study which assists the doctors for the early diagnosis of the disease.
Pakistani Standard Vehicle Plates Recognition using Deep Neural Networks

With many applications in security, surveillance, and intelligent transportation systems, Automatic License Plate Recognition (ALPR) is a fundamental and important task. In the recent era with the growth of Deep Learning (DL) techniques, ALPR also has shown dramatically accurate results as other computer vision problems. Most of the practical ALPR systems are country-specific, for example, a lot of work has been done for Chinese, Indian, Brazilian, American, or European vehicle plates that have specific vehicle plate format. This work focuses on vehicle plate recognition for multiple formats implemented in the same country or state. For this purpose, Pakistan has been chosen where license plates have unique formatting styles even within different provinces that enhance the complexity of character segmentation and recognition, and ultimately make it a challenging problem. Moreover, due to commercialization and privacy concerns, there was no publicly available dataset of Pakistani license plates, so we have built our own dataset under different environmental conditions like sunny, foggy, or night-time. Also, the dataset contains some blurred, angled, and distorted images. We achieved 90.13% segmentation accuracy for standard number plates. After this, for correct recognition of characters, we have developed an eight layers Deep Neural Network (DNN) model and achieved 97.19% classification accuracy. To the best of our knowledge, it is the first-ever system developed for Pakistani vehicle plates using DNN where distorted images have also been considered in the dataset and a reasonable accuracy is achieved.
Comparative study of different denoising techniques on Brain NCCT images

Medical images often get affected by noise and artifacts while acquisition techniques or patient’s movement. Some time radiologists unable to make useful and accurate conclusion from noisy images. The main aim of this study is to conduct a comparative measure of various denoising techniques such as Gaussian filter, Median filter, Bilateral filter, Non-Local Mean filter(NLM), Total Variation (TV) Anisotropic Diffusion (AD), BM3D method, on brain NCCT(Non-Contrast computed tomography) images. The prime focus of this research is to make comparative analysis of these techniques not merely to remove but also to preserve edges of brain NCCT images. The experimental results present that BM3D shows the best performance in terms of PSNR value, followed by total variation method and anisotropic diffusion method in terms of removal of noise. However marginal loss of edges and other fine details are there.
Coding Efficiency Enhancement Using Time Interleaved Level Splitting and Optimized Multi-Level Delta Sigma Modulation in Digital Transmitter

This paper proposes a novel scheme for energy-efficient transmitter design using optimized multi-level DSM (OML-DSM) along with time-interleaved level splitting technique. This scheme significantly improves the overall transmitter efficiency by providing high coding efficiency (CE) at the DSM stage and high drain efficiency (DE) at the amplification stage. The position of quantization level and quantization level selection window range is optimized in OML-DSM to achieve better CE compared to conventional multi-level DSM. These optimized multi-level signals are further split into bi-level signal streams using time-interleaved level splitting technique to improve the DE at the amplification stage. A 10 MHz LTE signal with 12 dB PAPR is used for simulation and measurement purposes. For hardware validation, 6-level OML-DSM is applied, which converts the 10 MHz LTE signal into six quantization levels and level splitter split these multi-levels into three bi-level signal streams with different magnitude and power levels. These bi-level signal streams are amplified using class-E SMPAs. The output of SMPAs is combined with Gysel unequal power combiner. Hardware results show that the CE and channel efficiency is improved by 18.7%, and 11.4%, respectively, compared to conventional 6-level DSM. The measured EVM is 2.2%, which shows good in-band performance.
COVID-SEGNET: Diagnosis of Covid-19 Cases on Radiological Images using Mask R-CNN

The novel coronavirus (COVID-2019) pandemic has caused a catastrophic effect on health and global economy. Early screening and diagnosis of COVID-19 pneumonia are the critical steps to stop the further spread of the virus. The most common standard for confirming the virus relies on RT-PCR tests. This method generates false-negative results if there is limited viral load. Recent radiological findings suggest that the distinct distribution of ground-glass opacities (GGOs), which are found on certain parts of lungs, can determine the status of the infection among patients. As a complement to RT-PCR, Computed tomography (CT) can be used for diagnosing COVID-19. In this study, the authors have described a Mask R-CNN (region-based convolution neural network) approach for the detection of the ground glass opacities (GGOs) in chest CT images of COVID-19 infected persons. The proposed approach provides an accuracy of 98.25% during instance segmentation. Therefore, the authors believe this proposed method will aid health professionals to fasten the screening and validation of the initial assessment towards COVID-19 patients.
US Based COVID-19 Tweets Sentiment Analysis Using TextBlob and Supervised Machine Learning Algorithms

In December 2019, a highly contagious disease, Coronavirus disease 2019 (COVID-19) was first detected in Wuhan, China. The disease has spread to 212 countries and territories worldwide. While this epidemic has continued to infect millions of people, several nations have resorted to complete lockdowns. People took social networks during this shutdown to share their opinions, feelings, and find a way to calm down. This study proposed a US-based sentiment analysis of the tweets using machine learning and the lexicon analysis approach. This US-based tweets dataset was collected by RStudio software from 30 January 2020 to 10th May 2020, contains 11858 tweets. We find the label corresponding to each tweet using TextBlob, that is to say, positive, negative, or neutral. To clean up the facts we pre-process the tweets. In a later step, different feature techniques such as bag-of-words (BoW) and term frequency-inverse document frequency (TF-IDF) are used to preserve expressive information. Finally, the random forest, gradient boosting machine, extra tree classifier, logistic regression, and support vector machine models are used to categorize beliefs as being positive, negative, or neutral. Our suggested pipeline output is assessed using accuracy, precision, recall & F1 score. This research study shows how TF-IDF features can increase the performance of the supervised machine learning models and in this work, the gradient boosting machine outperforms the others and achieves high accuracy of 96% when paired with TF-IDF features. This analysis was done to analyze how the situation is being handled by citizens of the United States. The results of the experiments validate the approach’s effectiveness.
Elastic Resource Allocation, Provisioning and Models Classification on Cloud Computing A Literature Review

Cloud Computing (CC) system faces new challenges every day, due to the complex structure of system clusters and high volume of data processed by the systems. The ability of acquiring resources in an elastic manner is considered as the primary rationale for adopting CC system. Elasticity mainly supports the facility to grow and shrink the virtual resources dynamically according to the requirement of cloud users. Elastic resource allocation plays a vital part in managing cloud resources efficiently according to user demands as well as cloud service provider’s capacity. For gaining an efficient allocation of resource allocation in cloud, this paper has discussed the study of improving the load distribution and resource utilization with elasticity. However, literature shows that several works in CC and its benefits but there is a dearth of survey in analyzing detail about elasticity present in the cloud. This study has an attempt for fulfilling an existing available gap and introducing this survey based on adapting elasticity in CC. This paper has involved with various elasticity mechanism inclusive of definition, tools to measure, elasticity evaluation and existing elastic solutions with some issues faced. Finally, this paper has presented few open issues and new solutions. To the best knowledge, this study is novel to identify the issues faced in the model of elasticity solution using systematic review method.
Improving Business Intelligence through Machine Learning Algorithms

Machine learning (ML) is the study of various algorithms that help in making computers learn and work on itself without any specific programming done beforehand. It is a branch of artificial intelligence in which devices are built or programmed in order to function mostly on its own. In order to be able to predict or make decisions, mathematical models are built by the algorithms. Since machine learning is close to computational statistics, it is useful in developing computers to access data and derive the required outputs. Business Intelligence (BI) consists of the technologies and applications that are used to collect, integrate, analyze raw data and present information of a particular business. These systems help in making strategies and decisions in order to get better results for the future by comparing the past and present performances. It also helps organizations identify the market trends and business problems that have to be looked upon. Along with many advantages like boosting productivity and accountability, it also has a few drawbacks such as it being costly, complex and takes a long time to be implemented. In this paper, we have discussed how combining machine learning with business intelligence would be a true game-changer for companies who can afford it. It can not only help improve operational processes and provide better customer services, but also analyze large amounts of data and achieve real-time data analysis, as well as prevent the number of cybercrimes as these systems can learn to differentiate between what is the threat and what is not. Paper also represents the algorithms that are being used by the company in the process, and if it has been beneficial to them or not. One of the algorithms of machine learning that is used is linear regression. It is easy to implement and is used for purposes such as predicting real estate prices, financial performances and traffic. Cluster analysis is also used for the purpose of understanding the relationship between consumers and the products they mostly search for. In this paper a case study has been presented to demonstrate the business operations that take place in a major airport retailer in the region, and how they take the advantage of Business Intelligence to get better outcomes.
Liquid–Vapor Phase Behavior of Asphaltene-like Molecules

Molecular simulation is used to predict liquid–vapor saturation properties of asphaltene-like molecules. We examine nine compounds that are adapted from a structure produced by Boek and co-workers [Energy Fuels 2009, 23, 1209] via an optimization technique that generates quantitative molecular representations of asphaltenes. We first consider a sequence of four fused ring structures differentiated by their aromaticity. Next, a collection of structures with a single aromatic core decorated with side chains of variable length and position is examined. Finally, we consider a group of structures with heteroatoms. The general AMBER force field (GAFF) and the CHARMM general force field (CGenFF) are employed to describe molecular interactions. A general histogram-based approach introduced by Rane and co-workers [J. Chem. Theory Comput. 2013, 9, 2552] is used to calculate saturated densities, vapor pressures, and enthalpies of vaporization over a wide range of temperatures. Simulation results are compared to esti.
Automated Detection of Schizophrenia from Brain MRI Slices using Optimized Deep-Features

In humans, the incident rate of mental illness is gradually rising due to various causes. Schizophrenia is one of the chronic mental illness and its happing rate also rising in the current era. The patient with Schizophrenia will experience a confused mental condition and a timely recognition and treatment is essential to reduce the risk. The proposed work aims to implement a methodology to support the automated detection of Schizophrenia from the brain MRI slices of T1 modality (T1W). The assessment of brain MRI is executed using the pre-trained VGG16 system and the deep-features extracted are optimized with the Slime-Mould-Algorithm (SMA) and the reduced features are then considered to train, test and validate the binary classifiers employed in this work. This research is implemented using 500 images of each case (healthy/abnormal) and the attained result with the SVM-Cubic is superior compared to other classifiers considered in the automated disease detection system.
Queue Mining and Prediction Based on Context-Fusion Scenario

To solve the problems of business process mining and process execution time prediction based on event logs more accurately and efficiently, this paper proposes a queue mining and prediction method that integrates contextual scenarios. Firstly a scenario model that integrates contextual information is established. Then typical queues is mined from event log data based on the scenario model, and then maps tasks to the corresponding scenarios of the specific queues to predict the completion time. Finally, according to the real data set of a financial institution’s actual process executing, the feasibility and effectiveness of the algorithm is verified.
Towards Deep Learning: A Review On Adversarial Attacks

Attacker determines their targets strategically and deliberately depend on vulnerabilities they have ascertained. Organization and individuals mostly try to protect themselves from one occurrence or type on an attack. Still, they have to acknowledge that the attacker may easily move focus to advanced uncovered vulnerabilities. Even if someone successfully tackles several attacks, risks remain, and the need to face threats will happen for the predictable future. Machine learning algorithms have earned much popularity in artificial intelligence (A.I) in the modern era. Large organizations like Google, Facebook, and Microsoft use large volumes of user data to train machine learning models. Then they use it for social ads. Like these days, Whatsapp will make a new privacy policy to share their data on Facebook. That data may be used for companies advertisements in future. So, in this way, the privacy of an individual might be breech out. Due to the high probability of attacks and the leakage of sensitive data in deep learning on distributive computation, adversarial examples demonstrated the vulnerability of machine learning techniques in terms of robustness. Besides, this allowed adversaries to make use of the vulnerability to target machine learning systems. Although adversarial attacks on real-world applications did not occur until recently, it is difficult to inject an artificial adversary to the model that is being hosted without infringement of the reliability. Recently few attacks occur in terms of facial recognition, road signs classification by finally, the difference between theoretical methodologies for the generation of adversarial examples and practical schemes of attacks on real-world applications. To direct future studies in the real defence of adversarial examples in real-world applications, For realistic attacks, we integrate the threat model with adversarial examples and give an overview with future direction.
Using Technology in Smart and Intelligent Food Packages as a Communicational Tool with Consumers

This exploratory research aims to track the recent trends in food packaging design, through multiple case studies, to examine the core concepts of creative packaging and the implementation of the recent technology in the packaging industry. The research focuses on technology and its impact on design, functionality, and user experience, to highlight recommendations that could help designers and marketers to determine the essential information and elements in packaging. Also, to consider the possibility of using these new trends to stimulate, attract, and convince customers, and motivate their buying behavior and decision-making. The study also emphasizes the vast role of the smart and intelligent package to increase the interaction with consumers and help them to ensure the quality of the product and interact with them technologically through sensors of the active packaging. The researchers suggest that companies and designers should design packages in line with the consumers' experience process and invest in the smart packages to increase interaction in three main stages, sensation, Attraction, and Functionality; to raise consumers satisfaction, especially through smart technology. Moreover, to reduce the amount of food waste and support recycling, by connecting them through their smartphone to review the detailed information in that context.
Precise Indoor Positioning Based on Acoustic Ranging in Smartphone

Existing indoor positioning systems based on Wi-Fi or Bluetooth confront difficulties and problems, such as low positioning accuracy, unstable, and small-signal coverage. A system using an acoustic ranging signal is presented in this article, and it is available for almost all commercial off-the-shelf (COTS) smartphones. A solution was developed to detect the modulated signals transmitted by specially designed anchor stations at each consisting of a speaker. The detector is a two-stage algorithm based on a generalized cross correlation (GCC) method and a time–frequency-based feature extraction. The detected timestamps are converted to time-difference-of-arrivals (TDOAs) observables, which can be applied with a least-squares (LS) estimator to obtain the smartphone positions in static cases. Furthermore, an extended Kalman filter (EKF) is introduced to integrate data from inertial measurement units (IMUs) and acoustic TDOA ranging observables for estimating a better trajectory in dynamic cases. Experimental results show that the signal detection algorithm is accurate with a mean error of about 0.3–1 m in terms of ranging estimation. The proposed method offers a static positioning accuracy of better than 0.5 m. For dynamic tests, positioning accuracy of 0.5 m at 50% and 1.6 m at 95% is achieved with four anchors in the first scene. In the second scene with a large test site, it achieves a dynamic positioning accuracy of 0.5 m at 50% and 1.82 m at 95% with eight anchors, which is comparable with the positioning accuracy of the ultrawideband (UWB) technology.
Reciprocity Aided CSI Feedback for Massive MIMO

A potential showstopper for reciprocity-based beamforming is that the uplink SNR often is much smaller than the downlink SNR, making it hard to estimate channels on the uplink. We analyze this problem by considering a "grid-of-beams world" with a finite number of possible channel realizations. We assume that the terminal can accurately detect the channel and we propose a method of improving the channel detection from uplink pilots by designing a mapping between the channel and the pilots. We find a simple metric that is to be minimized to maximize performance. Further, we propose an algorithm that draws pilot sequences from a distribution aimed to minimize the metric. We see that we can come close to optimal performance, which requires long sequences, with significantly shorter sequences.
Suicide Trend Analysis and Prediction in India using Facebook Prophet

Suicide analysis is an area of vital importance to the National Institute of Mental Health and various other agencies working in the field of suicide prevention. Studying on this aspect helps to analyze the suicide pattern and trends that suicides follow over the years. This paper explores time-series data of the suicides that occurred in India to find whether there is a notable change in trend after a certain time point. A predictive approach is applied to forecast into the future of the suicide trend. The paper applies Facebook Prophet, a time-series prediction algorithm for drawing inferences and conclusions. The paper also suggests an inflection point algorithm that highlights the suicide trend between two points in time. Additionally, the model is also capable of predicting the trend for “n” number of years to come. We have used MAPE and SMAPE error techniques for accurate measurement. The mean absolute percentage error (MAPE) is a predictive accuracy measure while the symmetric mean absolute percentage error (SMAPE) is a percentage (or relative) error-dependent accuracy measure. The values of MAPE and SMAPE were found to be in the range of 0.1-0.2 and less than 12 respectively. The conclusion derived is that the result is an increasing nature in the current year and there is a need for utmost attention.