Cardiovascular Disease Prediction Using ML and DL Approaches

Healthcare is very important aspects of human life. Cardiovascular disease, also known as the coronary artery disease, is one of the many deadly infections that kill people in India and around the world. Accurate predictions can prevent heart disease, but incorrect predictions can be fatal. Therefore, here this paper describes a method for predicting cardiovascular disease that makes use of Machine Learning (ML) and Deep Learning (DL). In this paper, SMOTE-ENN (Synthetic Minority Oversampling Technique Edited Nearest Neighbor) was used to equalize the distribution of training data. The K-Nearest Neighbor method (KNN), Naive Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM), XGBoost (Extreme Gradient Boosting), Artificial Neutral Network (ANN), and Convolutional Neutral Network (CNN) are among the classifiers used in this paper. From Public Health Dataset required data is collected and focused on recognizing the best approach for predicting the disease in preliminary phase. This experiment end results show that the use of Artificial Neural Networks can be of much useful in prediction with better accuracy (95.7%) than compared to any other ML approaches

Classification of Breast Cancer Histopathological Images Using Semi-Supervised GANs

Breast cancer is diagnosed more frequently than skin cancer in women in the United States. Most breast cancer cases are diagnosed in women, while children and men are less likely to develop the disease. Various tissues in the breast grow uncontrollably, resulting in breast cancer. Different treatments analyze microscopic histopathology images for diagnosis that help accurately detect cancer cells. Deep learning is one of the evolving techniques to classify images where accuracy depends on the volume and quality of labeled images. This study used various pre-trained models to train the histopathological images and analyze these models to create a new CNN. Deep neural networks are trained in a generative adversarial fashion in a semi-supervised environment by extracting low-level features that improve classification accuracy. This paper proposes an eloquent approach to classifying histopathological images accurately using Semi-Supervised GANs with a classification accuracy greater than 93%

Attack-Resistance and Reliability Analysis of Feed-Forward and Feed-Forward XOR PUFs

University of Minnesota M.S.E.E. thesis.May 2019. Major: Electrical/Computer Engineering. Advisor: Keshab Parhi. 1 computer file (PDF); ix, 75 pages.Physical unclonable functions (PUFs) are lightweight hardware security primitives that are used to authenticate devices or generate cryptographic keys without using non-volatile memories. This is accomplished by harvesting the inherent randomness in manufacturing process variations (e.g. path delays) to generate random yet unique outputs. A multiplexer (MUX) based arbiter PUF comprises two parallel delay chains with MUXs as switching elements. An input to a PUF is called a challenge vector and comprises of the select bits of all the MUX elements in the circuit. The output-bits are referred to as responses. In other words, when queried with a challenge, the PUF generates a response based on the uncontrollable physical characteristics of the underlying PUF hardware. Thus, the overall path delays of these delay chains are random and unique functions of the challenge. The contributions in this thesis can be classified into four main ideas. First, a novel approach to estimate delay differences of each stage in MUX-based standard arbiter PUFs, feed-forward PUFs (FF PUFs) and modified feed-forward PUFs (MFF PUFs) is presented. Test data collected from PUFs fabricated using 32 nm process are used to learn models that characterize the PUFs. The delay differences of individual stages of arbiter PUFs correspond to the model parameters. This was accomplished by employing the least mean squares (LMS) adaptive algorithm. The models trained to learn the parameters of two standard arbiter PUF-chips were able to predict responses with 97.5% and 99.5% accuracy, respectively. Additionally, it was observed that perceptrons can be used to attain 100% (approx.) prediction accuracy. A comparison shows that the perceptron model parameters are scaled versions of the model derived by the LMS algorithm. Since the delay differences are challenge independent, these parameters can be stored on the server which enables the server to issue random challenges whose responses need not be stored. By extending this analysis to 96 standard arbiter PUFs, we confirm that the delay differences of each MUX stage of the PUFs follow a Gaussian probability distribution. Second, artificial neural network (ANN) models are trained to predict hard and soft-responses of the three configurations: standard arbiter PUFs, FF PUFs and MFF PUFs. These models were trained using silicon data extracted from 32-stage arbiter PUF circuits fabricated using IBM 32 nm HKMG process and achieve a response-prediction accuracy of 99.8% in case of standard arbiter PUFs, approximately 97% in case FF PUFs and approximately 99% in case of MFF PUFs. Also, a probability based thresholding scheme is used to define soft-responses and artificial neural networks were trained to predict these soft-responses. If the response of a given challenge has at least 90% consistency on repeated evaluation, it is considered stable. It is shown that the soft-response models can be used to filter out unstable challenges from a randomly chosen independent test-set. From the test measurements, it is observed that the probability of a stable challenge is typically in the range of 87% to 92%. However, if a challenge is chosen with the proposed soft-response model, then its portability of being stable is found to be 99% compared to the ground truth. Third, we provide the first systematic empirical analysis of the effect of FF PUF design choices on their reliability and attack resistance. FF PUFs consist of feed-forward loops that enable internally generated responses to be used as select-bits, making them slightly more secure than a standard arbiter PUFs. While FF PUFs have been analyzed earlier, no prior study has addressed the effect of loop positions on the security and reliability. After evaluating the performance of hundreds of PUF structures in various design configurations, it is observed that the locations of the arbiters and their outputs can have a substantial impact on the security and reliability of FF PUFs. Appropriately choosing the input and output locations of the FF loops, the amount of data required to attack can be increased by 7 times and can be further increased by 15 times if two intermediate arbiters are used. It is observed adding more loops makes PUFs more susceptible to noise; FF PUFs with 5 intermediate arbiters can have reliability values that are as low as 81%. It is further demonstrated that a soft-response thresholding strategy can significantly increase the reliability during authentication to more than 96%. It is known that XOR arbiter PUFs (XOR PUFs) were introduced as more secure alternatives to standard arbiter PUFs. XOR PUFs typically contain multiple standard arbiter PUFs as their components and the output of the component PUFs is XOR-ed to generate the final response. Finally, we propose the design of feed-forward XOR PUFs (FFXOR PUFs) where each component PUF is an FF PUF instead of a standard arbiter PUF. Attack-resistance analysis of FFXOR PUFs was carried out by employing artificial neural networks with 2-3 hidden layers and compared with XOR PUFs. It is shown that FFXOR PUFs cannot be accurately modeled if the number of component PUFs is more than 5. However, the increase in the attack resistance comes at the cost of degraded reliability. We also show that the soft-response thresholding strategy can increase the reliability of FFXOR PUFs by about 30%

PERCUSSION RECEPTIVE AND HIT FREE FOLDER SCHEDULING FOR MARINE AURAL LOCALIZATION

While most of the huddle arrive on the courtesy of submerged localization bit work was done to find out how the anchors behoves lug their files vis-à-vis sensor nodes. Our work makes an idea of bar agitate of binder scheduling repeatedly self-localization in submerged negotiation sensor accompany common nodes instinctively. Concerning wrap scheduling, our assist sends diminish localization time, and behaviour so we make a strife of two crate conductance procedures like collision-free method, anew collision-tolerant study. The collision-tolerant restrain less time for localization when correlated to collision-free one for indistinguishable likelihood of localization. Exclusive of systematic concentration consumed by anchors, the program of collision-tolerant includes specifically wonderful advantages

Separation, Identification and Structural Elucidation of a New Impurity in the Drug Substance of Amlodipine Maleate Using LC-MS/MS, NMR and IR

Amlodipine maleate is a maleate salt of 3-ethyl 5-methyl 2-[(2-aminoethoxy)methyl]-4-(2- chlorophenyl)-6-methyl-1,4-dihydropyridine-3,5-dicarboxylate. An unknown impurity at m/ z 392.2 for [M+H]+ ion has been detected during the accelerated stability analysis (40 °C /75 % RH) of amlodipine maleate drug substance by reverse-phase high performance liquid chromatography–mass spectrometry (RP-HPLC-MS). MS and MS/MS spectra of amlodipine maleate and unknown impurity are obtained using HPLC-MS/MS equipped with positive electrospray ionization (ESI). The nuclear magnetic resonance (NMR) and infrared (IR) spectra of the unknown impurity are recorded after isolation of the impurity by preparative HPLC. Based on MS, NMR and IR spectral data, the structure of the unknown impurity was proposed as 5-ethyl-7-methyl-6-(2-chlorophenyl)-8-methyl-3,4,6,7-tetrahydro-2H-1,4-benzoxazine-5,7- dicarboxylate

Theoretical and electrochemical analysis of L-serine modified graphite paste electrode for dopamine sensing applications in real samples

In this study, the carbon paste electrode (CPE) was modified by grinding L-serine in a pestle and mortar. L-serine (L-s) was shown to be an effective electrocatalyst at the modified CPE (MCPE) interface for detecting dopamine (DA). L-sMCPE showed excellent activity to detect DA in commercial injection samples with a recovery range of 98.9 to 100.5 %. Theoretical studies were used to understand the electrocatalysis of L-serine at the atomic level using frontier molecular orbitals (FMO) and analytical Fukui assay. According to theoretical findings, the amine group of L-serine works as an extra oxidation site (reason for enhanced reduction peak DA) and the carboxylic acid group acts as an additional reduction site (reason for enhanced oxidation peak DA) at the L-sMCPE interface

Identification of degradation products in Aripiprazole tablets by LC-QToF mass spectrometry

This paper describes the separation, identification and proposed structures of the degradation products formed during degradation analysis of aripiprazole in its final dosage form by high performance liquid chromatography (HPLC) coupled with quadrupole time-of-flight mass spectrometry (QToF-MS). The drug product was subjected to stress conditions including acid, base, thermal, oxidation, humidity and photolytic degradations. Aripiprazole was found to be stable in all conditions except in thermal and peroxide degradations. The degradation impurities were first separated by HPLC and then identified using QToF mass spectrometry. QToF mass spectrometer provided high order of mass accuracy for unknown impurities and their fragment ions to explore the elemental composition. Based on the fragmentation pattern, the possible structures of the unknown impurities were proposed. To the best of our knowledge, there were no methods available to identify the impurities during degradation of aripiprazole tablets by liquid chromatography-mass spectrometry

Active-site solvent replenishment observed during human carbonic anhydrase II catalysis

Human carbonic anhydrase II (hCA II) is a zinc metalloenzyme that catalyzes the reversible hydration/dehydration of CO2/HCO3-. Although hCA II has been extensively studied to investigate the proton-transfer process that occurs in the active site, its underlying mechanism is still not fully understood. Here, ultrahigh-resolution crystallographic structures of hCA II cryocooled under CO2 pressures of 7.0 and 2.5 atm are presented. The structures reveal new intermediate solvent states of hCA II that provide crystallographic snapshots during the restoration of the proton-transfer water network in the active site. Specifically, a new intermediate water (W IHTC/SUBTAG'FORTITLEHTC_RETAIN) is observed next to the previously observed intermediate water W-I,W- and they are both stabilized by the five water molecules at the entrance to the active site (the entrance conduit). Based on these structures, a water network-restructuring mechanism is proposed, which takes place at the active site after the nucleophilic attack of OH- on CO2. This mechanism explains how the zinc-bound water (W-Zn) and W-1 are replenished, which are directly responsible for the reconnection of the His64-mediated proton-transfer water network. This study provides the first 'physical' glimpse of how a water reservoir flows into the hCA II active site during its catalytic activity