U.S.-Soviet relations 1980-88: The politics of trade pressure

The United States applied trade pressure on the Soviet Union on a large scale during the 1980s for the attainment of political objectives. Although initially triggered by the Soviet intervention in Afghanistan, trade pressure became a concerted American policy aimed at influencing Soviet domestic and international behaviour, and expressing displeasure with Moscow's actions at home and abroad. The thesis looks at the ability of U.S. trade pressure to influence or shape Soviet behaviour and policy. The thesis, which is based upon a combination of American and Soviet primary sources as well as memoir and other publications, assesses both the economic and political effect (with greater emphasis on the latter) of Washington's application of economic measures in the early 1980s on Soviet policy on human rights, dissent, Jewish emigration, and the Third World. Two introductory chapters chart the overall development of Soviet-American relations during the years concerned. Four further chapters analyse the degree of economic and political success generated by American trade pressure. Two of these look at the economic effect of U.S. trade pressure in terms of denying the Soviet Union access to both strategic and non - strategic goods. The other two chapters concentrate on the political success of trade pressure, with particular reference to human rights and regional conflicts in the third world. A final chapter reviews the major literature on trade pressure, and sums up the results of the thesis which aims to alleviate some of the shortcomings prevalent in works on trade pressure and argues that U.S. trade pressure on the Soviet Union largely failed to have the desired effect of influencing Soviet domestic and international behaviour

Predicting Pulsars from Imbalanced Dataset with Hybrid Resampling Approach

Pulsar stars, usually neutron stars, are spherical and compact objects containing a large quantity of mass. Each pulsar star possesses a magnetic field and emits a slightly different pattern of electromagnetic radiation which is used to identify the potential candidates for a real pulsar star. Pulsar stars are considered an important cosmic phenomenon, and scientists use them to study nuclear physics, gravitational waves, and collisions between black holes. Defining the process of automatic detection of pulsar stars can accelerate the study of pulsar stars by scientists. This study contrives an accurate and efficient approach for true pulsar detection using supervised machine learning. For experiments, the high time-resolution (HTRU2) dataset is used in this study. To resolve the data imbalance problem and overcome model overfitting, a hybrid resampling approach is presented in this study. Experiments are performed with imbalanced and balanced datasets using well-known machine learning algorithms. Results demonstrate that the proposed hybrid resampling approach proves highly influential to avoid model overfitting and increase the prediction accuracy. With the proposed hybrid resampling approach, the extra tree classifier achieves a 0.993 accuracy score for true pulsar star prediction

Heart failure patients monitoring using IoT-based remote monitoring system

Intelligent health monitoring systems are becoming more important and popular as technology advances. Nowadays, online services are replacing physical infrastructure in several domains including medical services as well. The COVID-19 pandemic has also changed the way medical services are delivered. Intelligent appliances, smart homes, and smart medical systems are some of the emerging concepts. The Internet of Things (IoT) has changed the way communication occurs alongside data collection sources aided by smart sensors. It also has deployed artificial intelligence (AI) methods for better decision-making provided by efficient data collection, storage, retrieval, and data management. This research employs health monitoring systems for heart patients using IoT and AI-based solutions. Activities of heart patients are monitored and reported using the IoT system. For heart disease prediction, an ensemble model ET-CNN is presented which provides an accuracy score of 0.9524. The investigative data related to this system is very encouraging in real-time reporting and classifying heart patients with great accuracy

RFCNN: Traffic Accident Severity Prediction Based on Decision Level Fusion of Machine and Deep Learning Model

Traffic accidents on highways are a leading cause of death despite the development of traffic safety measures. The burden of casualties and damage caused by road accidents is very high for developing countries. Many factors are associated with traffic accidents, some of which are more significant than others in determining the severity of accidents. Data mining techniques can help in predicting influential factors related to crash severity. In this study, significant factors that are strongly correlated with the accident severity on highways are identified by Random Forest. Top features affecting accidental severity include distance, temperature, wind_Chill, humidity, visibility, and wind direction. This study presents an ensemble of machine learning and deep learning models by combining Random Forest and Convolutional Neural Network called RFCNN for the prediction of road accident severity. The performance of the proposed approach is compared with several base learner classifiers. The data used in the analysis include accident records of the USA from February 2016 to June 2020. Obtained results demonstrate that the RFCNN enhanced the decision-making process and outperformed other models with 0.991 accuracy, 0.974 precision, 0.986 recall, and 0.980 F-score using the 20 most significant features in predicting the severity of accidents

Spectroscopic Analysis of Au-Cu Alloy Nanoparticles of Various Compositions Synthesized by a Chemical Reduction Method

Au-Cu alloy nanoparticles were synthesized by a chemical reduction method. Five samples having different compositions of Au and Cu (Au-Cu 3 : 1, Au-Cu 2 : 1, Au-Cu 1 : 1, Au-Cu 1 : 2, and Au-Cu 1 : 3) were prepared. The newly synthesized nanoparticles were characterized by electronic absorption, fluorescence, and X-ray diffraction spectroscopy (XRD). These alloy nanoparticles were also analyzed by SEM and TEM. The particle size was determined by SEM and TEM and calculated by Debye Scherrer’s equation as well. The results revealed that the average diameter of nanoparticles gets lowered from 80 to 65 nm as the amount of Cu is increased in alloy nanoparticles. Some physical properties were found to change with change in molar composition of Au and Cu. Most of the properties showed optimum values for Au-Cu alloy nanoparticles of 1 : 3. Cu in Au-Cu alloy caused decrease in the intensity of the emission peak and acted as a quencher. The fluorescence data was utilized for the evaluation of number of binding sites, total number of atoms in alloy nanoparticle, binding constant, and free energy of binding while morphology was deduced from SEM and TEM

An economic evaluation of impact of soil quality on Bt (Bacillus thuringiensis) cotton productivity

The study was conducted with the aim to determine the impact of soil quality on the Bt cotton productivity. Asample of 150 farmers was selected by using multi-stage sampling technique from three districts i.e. Rahim YarKhan, Multan and Mianwali. A Cobb Douglas production function was employed to assess the effect of variousagronomic and demographic variables on the Bt cotton productivity. Results of the analysis indicated that landpreparation cost, seed cost, fertilizer cost, labour cost and dummy variable of soil quality were significant andpositively contributing towards higher Bt cotton yield. While the spray cost and irrigation cost variable were foundpositive but non-significant. Findings of the study suggested that focusing on maintaining and improving the qualityof soils is necessary to obtain higher crop yields. All this needs attention of agricultural extension department toprovide information about advance techniques to farmers for improving soil quality

Brain Tumor Detection Using 3D-UNet Segmentation Features and Hybrid Machine Learning Model

Machine learning has significantly improved disease diagnosis, enhancing the efficiency and accuracy of the healthcare system. One critical area where it proves beneficial is diagnosing brain tumors, a life-threatening disease, where early and accurate predictions can save lives. This study focuses on deploying a machine learning-based approach for brain tumor detection, utilizing Magnetic Resonance Imaging (MRI) features. We train the proposed model using 3D-UNet and 2D-UNet segmentation features extracted from MRI, encompassing shape, statistics, gray level size zone matrix, gray level dependence matrix, gray level co-occurrence matrix, and gray level run length matrix values. To improve performance, we propose a hybrid model that combines the strengths of two machine learning models, K-nearest neighbor (KNN) and gradient boosting classifier (GBC), using soft voting criteria. We combine them because, in cases where KNN exhibits poor performance for certain data points, GBC demonstrates significant performance, and vice versa, where GBC shows poor results, KNN performs significantly better. With 2D-UNet segmentation features, the model achieves a 64% accuracy. By training it on 3D-UNet segmentation features, we achieve a significant accuracy of 71% which surpasses existing state-of-the-art models that utilize 3D-UNet segmentation features

Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction

Cardiovascular diseases (CVDs) have been regarded as the leading cause of death with 32% of the total deaths around the world. Owing to the large number of symptoms related to age, gender, demographics, and ethnicity, diagnosing CVDs is a challenging and complex task. Furthermore, the lack of experienced staff and medical experts, and the non-availability of appropriate testing equipment put the lives of millions of people at risk, especially in under-developed and developing countries. Electronic health records (EHRs) have been utilized for diagnosing several diseases recently and show the potential for CVDs diagnosis as well. However, the accuracy and efficacy of EHRs-based CVD diagnosis are limited by the lack of an appropriate feature set. Often, the feature set is very small and unable to provide enough features for machine learning models to obtain a good fit. This study solves this problem by proposing the novel use of feature extraction from a convolutional neural network (CNN). An ensemble model is designed where a CNN model is used to enlarge the feature set to train linear models including stochastic gradient descent classifier, logistic regression, and support vector machine that comprise the soft-voting based ensemble model. Extensive experiments are performed to analyze the performance of different ratios of feature sets to the training dataset. Performance analysis is carried out using four different datasets and results are compared with recent approaches used for CVDs. Results show the superior performance of the proposed model with 0.93 accuracy, and 0.92 scores each for precision, recall, and F1 score. Results indicate both the superiority of the proposed approach, as well as the generalization of the ensemble model using multiple datasets