Upaya Peningkatan Kualitas Udara Akibat Emisi Kendaraaan Bermotor di Kota Makassar Menggunakan Interpretative Structural Modeling (ISM)

Abstrak: Pencemaran udara merupakan permasalahan lingkungan yang mengancam kota-kola besar di Indonesia, terutama yang bersumber dari emisi kendaraan bermotor. Penelitian ini beriujuan untuk meneniukan allernatif strategi peningkatan kualitas udara di Kota Makassar. Pemilihan allernattf dan analisis strategi menggunakan metode Interpretative Structural Modelling (ISM), dimana metode ini menggunakan penilaian pakar dalam bentuk kuesioner dalam pengambilan datanya. Hasil penelitian menunjukkan hahwa Mass Rapid Transportation (MRT) merupakan allernatif strategi yang mempunyai prioritas utama dalam peningkatan kualitas udara di Kota makassar, sedangkan faktor kunci dalam pengendalian pencemaran adalah melakukan efisiensi bahan bakar. Keterbatasan dana pemerinlah merupakan elemen kunci yang berpengaruh menimbulkan kendala, sedangkan aktor kunci yang berperan adalah Pemerintah pusat, Pemda, DPRD dan LSM

Deep Cognitive Neural Network (DCNN)

Embodiments of the present systems and methods may provide a more efficient and low-powered cognitive computational platform utilizing a deep cognitive neural network (DCNN), incorporating an architecture that integrates convolutional feedforward and recurrent networks , and replaces multi - layer perceptron (MLP) based sigmoidal neural structures with a queuing theory-driven design. For example, in an embodiment, a circuit may comprise a plurality of layers of neural network circuitry, each layer comprising a plurality of neuron circuits, each neuron comprising a plurality of computational circuits, and each neuron connected to a plurality of other neurons in the same layer by synapse circuitry, wherein the plurality of layers of neural network circuitry are adapted to process symbolic and conceptual information.United State

Data mining using the crossing minimization paradigm

Our ability and capacity to generate, record and store multi-dimensional, apparently unstructured data is increasing rapidly, while the cost of data storage is going down. The data recorded is not perfect, as noise gets introduced in it from different sources. Some of the basic forms of noise are incorrect recording of values and missing values. The formal study of discovering useful hidden information in the data is called Data Mining. Because of the size, and complexity of the problem, practical data mining problems are best attempted using automatic means. Data Mining can be categorized into two types i.e. supervised learning or classification and unsupervised learning or clustering. Clustering only the records in a database (or data matrix) gives a global view of the data and is called one-way clustering. For a detailed analysis or a local view, biclustering or co-clustering or two-way clustering is required involving the simultaneous clustering of the records and the attributes. In this dissertation, a novel fast and white noise tolerant data mining solution is proposed based on the Crossing Minimization (CM) paradigm; the solution works for one-way as well as two-way clustering for discovering overlapping biclusters. For decades the CM paradigm has traditionally been used for graph drawing and VLSI (Very Large Scale Integration) circuit design for reducing wire length and congestion. The utility of the proposed technique is demonstrated by comparing it with other biclustering techniques using simulated noisy, as well as real data from Agriculture, Biology and other domains. Two other interesting and hard problems also addressed in this dissertation are (i) the Minimum Attribute Subset Selection (MASS) problem and (ii) Bandwidth Minimization (BWM) problem of sparse matrices. The proposed CM technique is demonstrated to provide very convincing results while attempting to solve the said problems using real public domain data. Pakistan is the fourth largest supplier of cotton in the world. An apparent anomaly has been observed during 1989-97 between cotton yield and pesticide consumption in Pakistan showing unexpected periods of negative correlation. By applying the indigenous CM technique for one-way clustering to real Agro-Met data (2001-2002), a possible explanation of the anomaly has been presented in this thesis.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

DNN Driven Speaker Independent Audio-Visual Mask Estimation for Speech Separation

Human auditory cortex excels at selectively suppressing background noise to focus on a target speaker. The process of selective attention in the brain is known to contextually exploit the available audio and visual cues to better focus on target speaker while filtering out other noises. In this study, we propose a novel deep neural network (DNN) based audiovisual (AV) mask estimation model. The proposed AV mask estimation model contextually integrates the temporal dynamics of both audio and noise-immune visual features for improved mask estimation and speech separation. For optimal AV features extraction and ideal binary mask (IBM) estimation, a hybrid DNN architecture is exploited to leverages the complementary strengths of a stacked long short term memory (LSTM) and convolution LSTM network. The comparative simulation results in terms of speech quality and intelligibility demonstrate significant performance improvement of our proposed AV mask estimation model as compared to audio-only and visual-only mask estimation approaches for both speaker dependent and independent scenarios

Comparison of QTC Interval Prolongation in Cirrhotic and Non-Cirrhotic Chronic Hepatitis C Patients

Objective: To determine frequency of QTc interval prolongation in hepatitis C infection. To compare QTc interval prolongation in patients with cirrhotic and non-cirrhotic chronic hepatitis c infectionStudy design: Descriptive Case SeriesSetting: Medical Unit-III, Fauji Foundation Hospital, RawalpindiDuration of study: 06 months duration of study i.e 10th May, 2017 to 10th Nov, 2017Methodology: Patients having chronic hepatitis c infection with cirrhosis was taken from medical ward and non-cirrhotic chronic hepatitis c infection was taken from general medical OPD. Consent was taken. For QTc interval calculation ECG was performed by ECG technician having 22 years of experience as ECG technician in Fauji Foundation Hospital Rawalpindi. Information was recorded on the form. The variable of interest was age, gender, cirrhosis, QTc interval and comparison of prolongation of QTc between hepatitis c positive cirrhotic and non-cirrhotic patients. Results: Total 110 patients were included according to the inclusion criteria of the study. Mean age (years) in the study was 56.84+11.05. There were 48 (43.6) male and 62 (56.4) female patients who were included in the study according to the inclusion criteria. Mean duration of QTc interval was 0.48+0.04. Out of 110 patients, there were 27 (24.5) patients who have prolonged QTc interval. The frequency of QTc interval prolongation in patients with cirrhotic and non-cirrhotic chronic hepatitis C infection was 22 (57.9) and 05 (6.9) respectively which was statistically significant (p-value 0.000).Conclusion: The study concludes that QTc interval prolongation in cirrhotic patients was high which showed that cirrhotic patients are at risk of developing ventricular arrhythmias due to cardiomyopathy, so a simple ECG test can be used to diagnose and prevent cardiac events in cirrhotic patients as it is simple as well as easily available.Keywords: Cirrhosis, Hepatitis C, Prolonged QT interval, non-cirrhotic Chronic Hepatitis C Infectio

Association of Dietary Practices and Lifestyle Modifications in Gastroesophageal Reflux Disease in Pakistani Women

Background: Gastroesophageal Reflux Disease (GERD) incidence is increasing day by day due to lifestyle changes and living standards that resulted in esophagitis, esophageal adenocarcinoma, Barrett’s esophagus and many other illness worldwide. Patients with GERD live with poor quality life and have low work capacity.  Aims: Main aim of the study is to diagnose GERD in early stages for the reduction in mortality and morbidity at different age groups. Methods: The pre-tested questionnaire was used to collect data from Sir Ganga Ram Hospital Lahore. A total of 230 female patients screened for GERD symptoms were included in this study. The collection of demographic data, dietary intake, lifestyle habits, physiology, and physical analysis were gathered during the 4 months.   Results: Data analysis shows us that GERD is highly significant with age, occupation. Moreover, burping is highly significant in these patients. Fried fatty foods, spicy foods, fizzy drinks, garlic intake were also correlated to GERD symptoms. These subjects also suffer from more skin problems.  Conclusion: From our results, we infer that GERD has a very strong bond with dietary and lifestyle patterns. If these parameters are kept under control, GERD patients will be less agonize from complications and minimize our morbidity and mortality.&nbsp

Towards Arabic multi-modal sentiment analysis

In everyday life, people use internet to express and share opinions, facts, and sentiments about products and services. In addition, social media applications such as Facebook, Twitter, WhatsApp, Snapchat etc., have become important information sharing platforms. Apart from these, a collection of product reviews, facts, poll information, etc., is a need for every company or organization ranging from start-ups to big firms and governments. Clearly, it is very challenging to analyse such big data to improve products, services, and satisfy customer requirements. Therefore, it is necessary to automate the evaluation process using advanced sentiment analysis techniques. Most of previous works focused on uni-modal sentiment analysis mainly textual model. In this paper, a novel Arabic multimodal dataset is presented and validated using state-of-the-art support vector machine (SVM) based classification method