Miracle-dz/UniFAR-FTUs huggingface dataset:
Disease pattern recognition using modified prefix span algorithm

The phenomenal advances in health and biotechnology have been produces huge amount of data like clinical data and high throughput information that makes Electronic Health records(EHRs) expansive and complex. For handling this AI and data mining techniques have been utilizes along with health services. Today Data mining is utilized to detect diseases using various informational datasets along with machine learning algorithms. There are many techniques available which is utilized for diagnosis of diabetes disease like FP growth, Apriori, spade algorithm. These techniques discover unknown patterns or relationships from large amount of data and these are utilized for making decisions for preventive and suggestive medicine. The main disadvantage of these techniques is it discovers fewer patterns. In this paper we proposed modified spade algorithm that discover more patterns to detect disease accurately. The results will help in predicting quicker and more accurate disease so that it lead timely treatment of the patients.
American Thyroid Association Statement on Surgical Application of Molecular Profiling for Thyroid Nodules: Current Impact on Perioperative Decision Making

Background: Recent advances in research on thyroid carcinogenesis have yielded applications of diagnostic molecular biomarkers and profiling panels in the management of thyroid nodules. The specific utility of these novel, clinically available molecular tests is becoming widely appreciated, especially in perioperative decision making by the surgeon regarding the need for surgery and the extent of initial resection. Methods: A task force was convened by the Surgical Affairs Committee of the American Thyroid Association and was charged with writing this article. Results/Conclusions: This review covers the clinical scenarios by cytologic category for which the thyroid surgeon may find molecular profiling results useful, particularly for cases with indeterminate fine-needle aspiration cytology. Distinct strengths of each ancillary test are highlighted to convey the current status of this evolving field, which has already demonstrated the potential to streamline decision making and reduce unnecessary surgery, .
Assistive Interpreter for Specially Abled People Implemented using Arduino

: Addressing the issues of people with visual hearing and vocal impairment through a single aiding system is a tough job. Many modern day researches focus on addressing the issues of one of the above challenge but not all. The work focuses on finding a unique technique that aids the visually impaired by letting them hear what is represented as text converts the text available as voice signals. The project provides a way for the people with hearing impairment to visualized/read which is in audio from by speech to text conversion technique and we also provide a way for the vocally impaired to represent their voice by the aid of text to voice conversion technique. All these three solutions are modulated to be in a single unique system. All these activities are coordinated with the use of ardino. The visually impaired people are helped by the process in which the image is converted to text and text to speech. The deaf people can read from a monitor which displays the message. Vocally impaired people can convey their message by text so the other person can hear their message through speaker. Using arduino, GSM, flex sensor.
Investigation of traditional and deep neural sequence models for biomedical concept recognition

The goal of this dissertation is to improve state-of-the-art biomedical concept recognition. The task has a long history, but performance improvement is still an open research question. The dissertation has three main aims—in the first aim, which is presented in Chapter III, we investigate traditional biomedical concept recognition systems. The challenges in the task are identified and five post-processing components are introduced to improve precision by removing false positive annotations. In the second aim, which is described in Chapter IV, a two-stage machine learning system for biomedical concept recognition is introduced. The first stage detects spans of textual mentions and is called a span detection stage. The second stage assigns concept identifiers to the automatically detected span of text in the first stage, and is called concept normalization. We adapt the idea of sequence to sequence mapping from neural machine translation for concept normalization. Spans of textual mentions are treated as an input sequence, and their unique identifiers are the output of the sequence. The task of concept normalization is to map the textual mentions to their identifiers. The novelty of this aim is two-fold—(1) the problem formulation, unlike previous work that heavily depends on dictionary lookups, we split concept recognition into two-stages and approach each stage as a machine learning problem. (2) to the best of our knowledge, the notion of sequence to sequence mapping for biomedical terms is introduced in this dissertation for the first time. In the third aim, we explore Open Source tools to improve each of the stages mentioned above. In Chapter V, we evaluate deep learning methods vs. conditional random fields for the span detection stage. In Chapter VI, we evaluate OpenNMT, which learns a vector representation for the input sequence automatically vs. our own implementation, which uses one-hot-code encoding to represent the input sequence, for the concept normalization stage. Apart from representation differences, there is an architectural difference between our own implement and OpenNMT. The
Optimized Method for Electrical Impedance Tomography to Image Large Area Conductive Perturbation

The objective of the study was to develop an optimized method for dynamic electrical impedance tomography (EIT) to image large area conductive perturbation (LACP), a new type of imaging target that we found during the monitoring and evaluation of the mannitol dehydration treatment of patients with brain edema based on brain EIT. Previously, we reconstructed LACP images with the commonly used NOSER algorithm with polar driven pattern. However, conductivity changes near the center of the LACP were blurred or remained undetected, and blocky artifacts appeared in the reconstructed images, making the interpretation of the results difficult. To solve this problem, we, for the first time, propose an optimized algorithm for imaging LACP. This algorithm comprises a modified sensitivity matrix to compensate for the blurred conductivity changes near the center. It also uses the Markov random field constraint to reduce blocky artifacts. To verify the performance of the proposed method, we conducted experiments based on head models and human subjects. Specific metrics, including shape error (SE) and image fluctuation (IF) artifacts, were also proposed to evaluate the image quality of the LACP. The experimental results demonstrated that, compared with the NOSER algorithm, the proposed method could respectively reduce SE and IF by 51.7% and 47.5%. Therefore, it can optimize the imaging of LACP and provide references for other applications at present or in the future which involve imaging LACP.
Compiling Irregular Software to Specialized Hardware

Compiling Irregular Software to Specialized Hardware Richard Townsend High-level synthesis (HLS) has simpli ed the design process for energy-e cient hardware accelerators: a designer speci es an accelerator’s behavior in a “high-level” language, and a toolchain synthesizes register-transfer level (RTL) code from this speci cation. Many HLS systems produce e cient hardware designs for regular algorithms (i.e., those with limited conditionals or regular memory access patterns), but most struggle with irregular algorithms that rely on dynamic, data-dependent memory access patterns (e.g., traversing pointer-based structures like lists, trees, or graphs). HLS tools typically provide imperative, side-e ectful languages to the designer, which makes it di cult to correctly specify and optimize complex, memory-bound applications. In this dissertation, I present an alternative HLS methodology that leverages properties of functional languages to synthesize hardware for irregular algorithms. The main contribution is an optimizing compiler that translates pure functional programs into modular, parallel data ow networks in hardware. I give an overview of this compiler, explain how its source and target together enable parallelism in the face of irregularity, and present two speci c optimizations that further exploit this parallelism. Taken together, this dissertation veri es my thesis that pure functional programs exhibiting irregular memory access patterns can be compiled into specialized hardware and optimized for parallelism. This work extends the scope of modern HLS toolchains. By relying on properties of pure functional languages, our compiler can synthesize hardware from programs containing constructs that commercial HLS tools prohibit, e.g., recursive functions and dynamic memory allocation. Hardware designers may thus use our compiler in conjunction with existing HLS systems to accelerate a wider class of algorithms than before.
L'epidemiologia della broncopneumopatia cronica ostruttiva

(Epidemiology of chronic obstructive pulmonary disease). - Chronic obstructive pulmonary disease (COPD) is an important cause of morbidity and mortality, all over the world. COPD, which was the sixth leading cause of death worldwide in 1990, will become the third one in 2020. COPD is a complex disease, influenced by genetic, behavioral, and environmental factors. The most important factor for developing COPD is tobacco smoke. Also environmental conditions represents risk for developing COPD. Furthermore, diet and a low socioeconomic status are correlated to the disease. Genetic factors, familial history, and childhood lower respiratory tract infections play an important role in the etiology of COPD. Burden of COPD is very high for community. Furthermore, the disease often is under-diagnosed and treated only at advanced stages, whilst it is a substantial health problem even among young adults. This needs to be taken into account by health personnel and decision-makers.
Power Quality Improvement of Grid Connected Wind Energy System Using DSTATCOM-BESS

Electricity generation from wind energy has increased rapidly in recent years and the trend is likely to continue. Due to intermittent environmental factors and integration of wind power generation system with the grid, creates power quality issues including voltage swells, voltage dips, harmonics, power factor and poor voltage regulation. When wind power is injected into the grid, it reduces the performance of utility as well as consumer’s equipment. To improve the power quality, a Distribution Static Compensator (DSTATCOM) is used. This paper itroduces the power quality improvement technique for grid connected wind power plant using DSTATCOM with battery energy storage system (BESS). The proposed scheme mainly depends on the reactive power supply requirements of the load and the induction generator. The proposed scheme is simulated in MATLAB/Simulink software.
On Self‐dual and LCD Double Circulant Codes over a Non‐chain Ring*

In this paper, we study double circulant codes of length 2n over the non-chain ring R = 𝔽 q + v𝔽 q + v 2𝔽 q ; where q is an odd prime power and v 3 = v. Exact enumerations of self-dual and LCD double circulant codes of length 2n over R are derived. When n is an odd prime, using random coding, we obtain families of asymptotically good self-dual and LCD codes of length 6n over 𝔽 q .
Biomechanical analysis of the shoulder during tennis activities.

: Biomechanical analysis of the shoulder in tennis is still in early stages; however, the available data do allow some conclusions and some recommendations for conditioning, evaluation, and rehabilitation. Normal shoulder biomechanical function requires an intact kinetic chain to create the energy, produce the forces and stabilize the joint in tennis activities. Only through this mechanism can optimum performance with minimal injury risk be maintained. Conditioning of the shoulder for tennis should take this into account. Exercises should involve force generation by the large leg and trunk muscles, scapular stabilization, and closed chain co-contraction activity for the shoulder stabilizers. Similarly, clinical evaluation for shoulder problems must include assessment of areas distant to the shoulder. Kinetic chain failure can cause extra stress on the shoulder, causing or exacerbating clinical symptoms at the shoulder. Clinical evaluation of shoulder joint structures also is enhanced by knowledge of the integration of the constraint systems, and the fact that more than one system may be involved in shoulder pathology. Finally, rehabilitation efforts for shoulder problems need to focus on allowing functional return of the shoulder joint in the context of the entire kinetic chain of tennis specific activity. Rehabilitation of all areas of kinetic chain failure, such as trunk inflexibility or scapulothoracic dyskinesis, should be undertaken in conjunction with rehabilitation techniques for the shoulder. The sports medicine clinician will have a more functional framework for assessing shoulder activity and injury in tennis through the understanding of these biomechanical principles.
Feature Identification of Hepatic Cancer Ultrasound Image using Gaussian Filtering Combined with Intensity Adjustment

Ultrasound images are widely used in medical domain due to its relatively low cost, non-invasive characteristics, and real-time imaging capabilities. Ultrasound images have grayscale color values that are susceptible to development of speckle noise in the image. Medical diagnostic images are usually corrupted by noise during their acquisition and most of the noise is speckle noise. Improved preserved features are very important in medical ultrasound images of liver cancer and we need an image enhancement process to achieve the aims. In this research we improve the efficiency of denoising and maintain shape features and texture features properly by using Gaussian Filtering and Adjustment Intensity methods. The proposed approach also improve visual quality of the ultrasound images. The enhancement techniques is to bring out detail that is obscured, or simply to highlight certain features of interest in an image The results demonstrate the ability of proposed method for noise suppression as well as feature and edge preservation in terms of a variety of performance measures. Keywords— Gaussian Filter; Histogram Equalization; Image Enhancemen; Intensity Adjustment; Ultrasound image
A New Re-Ranking Method Based on Convolutional Neural Network and Two Image-to-Class Distances for Remote Sensing Image Retrieval

With the rapid growth of remote sensing image data, it has become necessary to effectively and efficiently retrieve images from a big image database for managing and exploiting such data. This paper presents a novel method for content-based remote sensing image retrieval (CBRSIR) that re-ranks the initial retrieval result using two image-to-class distances, which are the similarity between an image and an image class. One is the image-to-training-class distance between an image and each image class of the training dataset, which uses the information included in the train samples with known-label. It is used to calculate the weight of the image classes and estimating the class of an image. The other is the image-to-query-class distance between an image and the query class that is obtained using the k-nearest neighbor ( $k$ NN) method. The proposed method first obtains the initial retrieval results via sorting the distances of the CNN feature between the query image and each retrieved image in ascending order. Then, the image-to-query-class distance is calculated according to the initial result, and the weight of each class is calculated according to the class probability of images obtained by CNN and the image-to-training-class distances. Finally, the initial result is re-ranked to get the final retrieval result via the weighted image-to-class distances. The performance of the proposed method is tested on UCMD and PatternNet databases. Experimental results show the effectiveness of the proposed re-ranking scheme in image retrieval in comparison to other state-of-the-art techniques.
Spectrum Sharing Framework for 5G Cellular System and Satellite Service

The ambitious fifth generation (5G) cellular system requirements and performance targets motivated standardisation bodies to consider wide bandwidth allocations for 5G in the mm-wave band. Nevertheless, parts of the considered band are already allocated to satellite services in several regions. We tackle this challenge by proposing a co-existence framework for 5G and fixed satellite services (FSS). We focus on the uplink of both systems and consider realistic 5G deployment scenarios with multiple users and multiple radio access network (RAN) cells. We propose a generic and controllable co-existence constraint applicable to different 5G numerologies and configurations. In addition, we derive a protection distance to guarantee the co-existence constraint and utilise several 5G system features to define soft constraints. The 5G/FSS coexistence is investigated based on performance of the 5G user plane. Simulation results show that the 5G deployment scenario is a key factor in setting the protection distance. In addition, the FSS elevation has a significant effect on the identified distance. The results suggest that both systems can operate in the same band without very large protection distance at a controllable expense of a small, e.g., 1% - 5%, performance loss.
A Generic Parallel Map : Extending the List Map Higher Order Function to Arbitrary Data Structures

The standard map higher order function is commonly used by the parallel functional programming community to apply a function to the elements of a list data structure in parallel. In this work, a generic parallel map was developed. The design and implementation of the generic parallel map was based on the integration of a sequential generic functional programming language, a data distribution algorithm, a parallel master slave template and generic sends. The new generic parallel map can take a function and apply it on any data type. Results are shown for parallelizing a ray tracer using the generic parallel map. Speedup results are good.
Acoustic Topology of Infant Proto-Syllables.

A corpus of CV-like proto-syllables was obtained from the archival recordings of three first-born infants, which were made in conjunction with earlier research on the acoustic and interactive aspects of infant language acquisition. Tokens were limited to proto-syllables with perceptual evidence of stop-like or glide-like qualities and an identifiable vowel. Following A/D sampling, measures of developmental change were made including F1/F2 steady states, transition durations, CV durations, maximum F2 velocities, F2 onset, fundamental frequency, shimmer, time-to-peak amplitude velocity, and time-to-peak F2 velocity within the transition. Contrary to expectations, the orderliness of the infant protoTM syllables was remarkable with few acoustic events indicative of loss or reduction of articulatory control. M ith the exception of transition duration, all intrinsic temporal measures were positively correlated with overall syllable duration. The modal adult and infant syllable durations overlapped at approximately 240 msec with infants exhibiting a strong skew towards
Mastering Omni-Channel Retailing Challenges with Industry 4.0 Concepts

Omni-Channel Management is an important trend, which allows retailers to improve customer experiences. Notwithstanding, entirely seamless integration of all channels, for example, in terms of customer or pricing data or consistent product offerings, is still a challenging endeavor. Technological developments, such as Industry 4.0 (I4.0), lead to innovation opportunities in the production industry. As there are intersections between I4.0 and Omni-Channel retailing, we propose that prominent Omni-Channel retailing challenges can be overcome by integrating knowledge from both research domains. Therefore, the purpose of this article is to investigate, which I4.0 concepts are utilized in scientific literature to overcome challenges and how these concepts can be transferred to OmniChannel Management. To make this knowledge tangible for retailers, this article deduces opportunities on the application of I4.0 concepts in Omni-Channel retailing. The results show that especially IoT networks offer numerous deployment options and even Cyber-Physical Systems and Smart Factories provide related potentials.
Adaptive Speech Noise Cancellation using Wavelet Transforms

A new method is presented for single-channel noise reduction. The proposed method is a combination of wavelet transform and adaptive filter. The speech signal is initially decomposed by wavelet transform into frequency sub-bands and then the noise is removed by an adaptive filter based on the LMS algorithm. Noise is removed separately for approximation and details coefficients. In the adaptive filter, the noisy speech sub-band is considered as the desired signal and the delayed one as the filter input. Filter output is the improved sub-band. After noise reduction, the sub-bands are reconstructed to achieve improved speech. The improved speech quality was evaluated by two objective measures, and also was compared with the unprocessed noisy speech and improved speeches by wavelet and adaptive filtering techniques. The results show that in the noises with low autocorrelation (such as white noise) the proposed algorithm shows much higher performance than other methods for improving speech quality in all noisy conditions.
The application of machine learning, big data techniques,and criminology to the analysis of racist tweets.

Racist tweets are ubiquitous on Twitter. This thesis aims to explore the creation of an automated system to identify tweets and tweeters, and at the same time gain a theoretical understanding of the tweets. To do this a mixed methods approach was employed: machine learning was utilised to identify racist tweets and tweeters, and grounded theory and other qualitative techniques were used to gain an understanding of the tweets’ content. 84 million tweets that all contained racist words were collected from Twitter. 84,000 of these were hand annotated as racist or not. The machine learning was performed in a Hadoop cluster, utilising Spark and Hive. To identify racist tweets, systematic comparison of seven different algorithms, and a large number of textual, user derived and geographical features was performed. New features: time of day and day of week were also evaluated. The 84,000 hand annotated tweets were used as input to the machine learning supervised classification processes. It was found that the combination of support vector machines with hour of day as additional feature was optimal for accuracy (0.93) and AUPRC (0.86). A qualitative exploration of tweets was also performed, including a grounded theory analysis. A novel machine learning system to identify racist accounts was created using metrics from the racist tweets, concepts from the grounded theory and a combination of the two as feature inputs. All three sets of features gave accuracy of at least 0.82. The ambiguity of the tweets meant they were difficult to classify, for both humans and machines, as to whether the tweeter’s intentions were racist or not, the word ‘nigga’ being particularly problematic. Grounded theory analysis of the tweets showed extremely narrow rhetoric that could be summarised in a single theoretical concept: the defence of the in-group.