Showing results for people park complex
GitHub Repo
https://github.com/Deepak019/SEO
Deepak019/SEO
What is GPS? By definition, Global Positioning System (GPS) is a worldwide network formed by a group of 24 satellites that orbit earth and transmit signals to the ground stations. These satellites were put into orbit by the U.S. Department of Defence with the intention to cater military requirements. Later, it was made available for civilian benefit. What is GPS Tracking? GPS Tracking is the technique that helps you locate a vehicle, person or an asset using the Global Positioning System (GPS). Is GPS Tracking reliable? Immensely. GPS tracking provides you much accurate results knowing the fact that the signals have to travel thousands of kilometres across the space. GPS results may vary within few dozen feet which means that you can pinpoint whether you are at home or in the park across the street. Applications of GPS Tracking Some of the many applications are: Company Fleet Management: Assists a company to manage their fleet, cut back on extra costs and improve their efficiency. Vehicle Tracking: Keep track of your vehicle. Child Tracking: Know the location of your child. Elderly people and Special People Tracking: Be with your elders or disabled loved ones in case of emergency. Prevention of Theft: A vehicle can be easily recovered knowing its location. There are n number of other uses from finding a friend in the market place to track your laptop while travelling. Try it to discover some more. Now, let’s come on to the most widely used application of GPS – Vehicle Tracking. Vehicle tracking Vehicle tracking systems keep an eye on the vehicle in real time to get the full picture of all its travelled locations. Variants of Vehicle Tracking System Passive: Simpler in nature, a tracker (GPS device) is installed on the vehicle, which tracks the location of the vehicle over time. When required, the tracker is removed and data is transferred to computer for analysis and report generation. Active: Bit complex, in this variant tracker transmits the data in real time via satellite to the server. Vehicle tracking software used here evaluates the data constantly as received. Benefits of Vehicle Tracking System Vehicle tracking system uses an integrated vehicle tracking software along with the tracker device to help you achieve the following: Surveillance – Follow your vehicle on go. Monitoring of fuel – Oversee the fuel consumption. Distance Travelled – Distance travelled by vehicle. Asset tracking – Closely monitor the valuable asset movements and its operating status. Trailer tracking – Load carrying unit of the vehicle is known as “Trailer”. Track them too! Transit Tracking – Temporary tracking of cargoes (assets) from one point to another and many more. A number of vehicles under the single ownership or travelling together are termed as “fleet”. So, the above benefits would multiply with the number of vehicles (fleet) and would prove to be of great advantage for fleet management companies. Note: There are various types of vehicle tracking software available in the market and benefits can be enhanced by the choice you make.
GitHub Repo
https://github.com/ftnirmaan/Everything-You-Need-to-Know-Plots-for-Sale-in-Hyderabad
ftnirmaan/Everything-You-Need-to-Know-Plots-for-Sale-in-Hyderabad
Hyderabad is becoming India's most favoured option of investors and home buyers. The megacity is set to increase its real estate and property values as it is set to see a surge of 20 million in its population. One of the reasons why Hyderabad has become a popular destination for foreign investors and home buyers is because of its large IT/ITES presence and large pharmaceutical and biotechnology industries. Hyderabad is the 2nd fastest-growing city in India and is in a fast transformation. Today, Hyderabad is one of the most preferred cities in India for new investments. Hyderabad is famous for its textile, engineering and aerospace industries, along with education and medical service. Hyderabad is increasingly becoming a destination to invest in real estate. The real estate sector in Hyderabad is witnessing a boom and the city is a point of interest for people from various walks of life, whether they are locals or NRIs. The investment required is low and the ROI is Vastly Advanced, especially in 3-4 years. If you are searching for Plots for Sale in Hyderabad then you have picked the right place. With affordable prices, Hyderabad is a promising option for investments. The plots for sale come with unparalleled opportunities for investors who are looking to make a mark in the real-estate market. Hyderabad is one of the most popular metropolises in India. The megacity has Witnessed a lot of developments in the once many times with the smash of mega promenades. The rapid-fire growth of the megacity, along with the of Secunderabad and neighbouring Cosmopolises has redounded in a large metropolitan. However, look no further. If you are Looking for Plots for Sale in Hyderabad, look no further. Buying plots in Hyderabad is easy, with our reliable and efficient service. Hyderabad has seen a noteworthy development in the land business, with a blast of uber shopping centres. The quick development of the city, alongside the development of Secunderabad and adjoining regions, has brought about a huge metropolitan region. Real estate and hospitality have been the most popular sectors in Hyderabad. Hyderabad has a lot to offer to the tourists and the people who are considering relocation. Hyderabad, the capital of the state of Telangana, has witnessed a remarkable growth for the past few years in the real estate business. Hyderabad has seen a 4.2% growth rate in the last 3 months, which is a good sign for the city The real estate market in Shadnagar has grown at a steady pace over the last few years. With a population that has increased to over 50,000 residents, there's a lot of people looking to invest in the area. There is more prominent development in this area of the city, promising more profits from the venture. The reality is that the real estate in Shadnagar is the most profitable investment option with all the benefits of a gated community, and other modern amenities that meet the changing urban lifestyle. plots in Shadnagar but presents the ravishing way of living as it is located in the most fascinating area nearby hard Regional Ring Road, hardly 5 – 10 minutes' drive. With highly efficient transport facilities to the mainline, anyone can find an easy way to travel as the Bus and Railway station is just 5 minutes' walk. If you're looking for Plots in Shadnagar, you might get confused with the plethora of options available. But don't worry, Plot Zone is here to help you. We provide exclusive deals on the best and the most-demanded plots in the area. Real estate is an important investment option for people all around the world. The real estate market is a complex industry with a lot of moving parts, there is greater growth in this part of the city, promising more returns on investment. Real estate in Shadnagar has witnessed a lot of demand with the increasing number of residential and commercial projects coming up. This demand is because of the growing population and increased commercial activities that have turned the real estate in Shadnagar into a promising area. The modern luxury amenities and easy connectivity are the additional factors that have made the real estate in Shadnagar attractive. Everyone wants to invest in real estate. But what's the best way to do that? There are a few things that you need to keep in mind when looking for investment in real estate. One of them is the place where the cash will come from. If you're looking for a property that can have a high potential for profit, you should invest in Shdnagar. Shdnagar is a prime location to invest in real estate. There are a multitude of enterprises that are being initiated around Shadnagar. It's like an ever-evolving melting pot of people, culture and economy. Shadnagar is the principal to profit from this. First class layouts, make your move to capitalize on future! One of the most important things for a home is a good plot of land to build it on. With the increasing population and industries in Hyderabad, housing is becoming more and more difficult. If you have been looking for Residential plots in Hyderabad, then you are at the right place. King's park is providing Residential Plots in Hyderabad with world class Amenities. Where you will get the complete return on investment. King's Park is also provide top quality services like security, water supply, electricity etc. In order to get the maximum value for your money. With the consistent development of land in Hyderabad, the city is going to be probably the best spot to purchase the property. Considering the prospects similar as ample space and long-term appreciation, Hyderabad is an ideal place to invest in real estate. Hyderabad is located in the southern part of India and has a huge population. With that many people, there are a lot of opportunities for real estate investors. It is likewise the second biggest city in India. Hyderabad is known for being an affordable place to live and work. It is a huge market for property investors. Contact us: Hitech City, Hyderabad, Telangana State – 500084 Email - info@ftnirmaan.com Call at - 9866770059, 9849022468 .
GitHub Repo
https://github.com/Aastha2104/Parkinson-Disease-Prediction
Aastha2104/Parkinson-Disease-Prediction
Introduction Parkinson’s Disease is the second most prevalent neurodegenerative disorder after Alzheimer’s, affecting more than 10 million people worldwide. Parkinson’s is characterized primarily by the deterioration of motor and cognitive ability. There is no single test which can be administered for diagnosis. Instead, doctors must perform a careful clinical analysis of the patient’s medical history. Unfortunately, this method of diagnosis is highly inaccurate. A study from the National Institute of Neurological Disorders finds that early diagnosis (having symptoms for 5 years or less) is only 53% accurate. This is not much better than random guessing, but an early diagnosis is critical to effective treatment. Because of these difficulties, I investigate a machine learning approach to accurately diagnose Parkinson’s, using a dataset of various speech features (a non-invasive yet characteristic tool) from the University of Oxford. Why speech features? Speech is very predictive and characteristic of Parkinson’s disease; almost every Parkinson’s patient experiences severe vocal degradation (inability to produce sustained phonations, tremor, hoarseness), so it makes sense to use voice to diagnose the disease. Voice analysis gives the added benefit of being non-invasive, inexpensive, and very easy to extract clinically. Background Parkinson's Disease Parkinson’s is a progressive neurodegenerative condition resulting from the death of the dopamine containing cells of the substantia nigra (which plays an important role in movement). Symptoms include: “frozen” facial features, bradykinesia (slowness of movement), akinesia (impairment of voluntary movement), tremor, and voice impairment. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. Performance Metrics TP = true positive, FP = false positive, TN = true negative, FN = false negative Accuracy: (TP+TN)/(P+N) Matthews Correlation Coefficient: 1=perfect, 0=random, -1=completely inaccurate Algorithms Employed Logistic Regression (LR): Uses the sigmoid logistic equation with weights (coefficient values) and biases (constants) to model the probability of a certain class for binary classification. An output of 1 represents one class, and an output of 0 represents the other. Training the model will learn the optimal weights and biases. Linear Discriminant Analysis (LDA): Assumes that the data is Gaussian and each feature has the same variance. LDA estimates the mean and variance for each class from the training data, and then uses properties of statistics (Bayes theorem , Gaussian distribution, etc) to compute the probability of a particular instance belonging to a given class. The class with the largest probability is the prediction. k Nearest Neighbors (KNN): Makes predictions about the validation set using the entire training set. KNN makes a prediction about a new instance by searching through the entire set to find the k “closest” instances. “Closeness” is determined using a proximity measurement (Euclidean) across all features. The class that the majority of the k closest instances belong to is the class that the model predicts the new instance to be. Decision Tree (DT): Represented by a binary tree, where each root node represents an input variable and a split point, and each leaf node contains an output used to make a prediction. Neural Network (NN): Models the way the human brain makes decisions. Each neuron takes in 1+ inputs, and then uses an activation function to process the input with weights and biases to produce an output. Neurons can be arranged into layers, and multiple layers can form a network to model complex decisions. Training the network involves using the training instances to optimize the weights and biases. Naive Bayes (NB): Simplifies the calculation of probabilities by assuming that all features are independent of one another (a strong but effective assumption). Employs Bayes Theorem to calculate the probabilities that the instance to be predicted is in each class, then finds the class with the highest probability. Gradient Boost (GB): Generally used when seeking a model with very high predictive performance. Used to reduce bias and variance (“error”) by combining multiple “weak learners” (not very good models) to create a “strong learner” (high performance model). Involves 3 elements: a loss function (error function) to be optimized, a weak learner (decision tree) to make predictions, and an additive model to add trees to minimize the loss function. Gradient descent is used to minimize error after adding each tree (one by one). Engineering Goal Produce a machine learning model to diagnose Parkinson’s disease given various features of a patient’s speech with at least 90% accuracy and/or a Matthews Correlation Coefficient of at least 0.9. Compare various algorithms and parameters to determine the best model for predicting Parkinson’s. Dataset Description Source: the University of Oxford 195 instances (147 subjects with Parkinson’s, 48 without Parkinson’s) 22 features (elements that are possibly characteristic of Parkinson’s, such as frequency, pitch, amplitude / period of the sound wave) 1 label (1 for Parkinson’s, 0 for no Parkinson’s) Project Pipeline pipeline Summary of Procedure Split the Oxford Parkinson’s Dataset into two parts: one for training, one for validation (evaluate how well the model performs) Train each of the following algorithms with the training set: Logistic Regression, Linear Discriminant Analysis, k Nearest Neighbors, Decision Tree, Neural Network, Naive Bayes, Gradient Boost Evaluate results using the validation set Repeat for the following training set to validation set splits: 80% training / 20% validation, 75% / 25%, and 70% / 30% Repeat for a rescaled version of the dataset (scale all the numbers in the dataset to a range from 0 to 1: this helps to reduce the effect of outliers) Conduct 5 trials and average the results Data a_o a_r m_o m_r Data Analysis In general, the models tended to perform the best (both in terms of accuracy and Matthews Correlation Coefficient) on the rescaled dataset with a 75-25 train-test split. The two highest performing algorithms, k Nearest Neighbors and the Neural Network, both achieved an accuracy of 98%. The NN achieved a MCC of 0.96, while KNN achieved a MCC of 0.94. These figures outperform most existing literature and significantly outperform current methods of diagnosis. Conclusion and Significance These robust results suggest that a machine learning approach can indeed be implemented to significantly improve diagnosis methods of Parkinson’s disease. Given the necessity of early diagnosis for effective treatment, my machine learning models provide a very promising alternative to the current, rather ineffective method of diagnosis. Current methods of early diagnosis are only 53% accurate, while my machine learning model produces 98% accuracy. This 45% increase is critical because an accurate, early diagnosis is needed to effectively treat the disease. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. With an earlier diagnosis, much of this degradation could have been slowed or treated. My results are very significant because Parkinson’s affects over 10 million people worldwide who could benefit greatly from an early, accurate diagnosis. Not only is my machine learning approach more accurate in terms of diagnostic accuracy, it is also more scalable, less expensive, and therefore more accessible to people who might not have access to established medical facilities and professionals. The diagnosis is also much simpler, requiring only a 10-15 second voice recording and producing an immediate diagnosis. Future Research Given more time and resources, I would investigate the following: Create a mobile application which would allow the user to record his/her voice, extract the necessary vocal features, and feed it into my machine learning model to diagnose Parkinson’s. Use larger datasets in conjunction with the University of Oxford dataset. Tune and improve my models even further to achieve even better results. Investigate different structures and types of neural networks. Construct a novel algorithm specifically suited for the prediction of Parkinson’s. Generalize my findings and algorithms for all types of dementia disorders, such as Alzheimer’s. References Bind, Shubham. "A Survey of Machine Learning Based Approaches for Parkinson Disease Prediction." International Journal of Computer Science and Information Technologies 6 (2015): n. pag. International Journal of Computer Science and Information Technologies. 2015. Web. 8 Mar. 2017. Brooks, Megan. "Diagnosing Parkinson's Disease Still Challenging." Medscape Medical News. National Institute of Neurological Disorders, 31 July 2014. Web. 20 Mar. 2017. Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23 (26 June 2007) Hashmi, Sumaiya F. "A Machine Learning Approach to Diagnosis of Parkinson’s Disease."Claremont Colleges Scholarship. Claremont College, 2013. Web. 10 Mar. 2017. Karplus, Abraham. "Machine Learning Algorithms for Cancer Diagnosis." Machine Learning Algorithms for Cancer Diagnosis (n.d.): n. pag. Mar. 2012. Web. 20 Mar. 2017. Little, Max. "Parkinsons Data Set." UCI Machine Learning Repository. University of Oxford, 26 June 2008. Web. 20 Feb. 2017. Ozcift, Akin, and Arif Gulten. "Classifier Ensemble Construction with Rotation Forest to Improve Medical Diagnosis Performance of Machine Learning Algorithms." Computer Methods and Programs in Biomedicine 104.3 (2011): 443-51. Semantic Scholar. 2011. Web. 15 Mar. 2017. "Parkinson’s Disease Dementia." UCI MIND. N.p., 19 Oct. 2015. Web. 17 Feb. 2017. Salvatore, C., A. Cerasa, I. Castiglioni, F. Gallivanone, A. Augimeri, M. Lopez, G. Arabia, M. Morelli, M.c. Gilardi, and A. Quattrone. "Machine Learning on Brain MRI Data for Differential Diagnosis of Parkinson's Disease and Progressive Supranuclear Palsy."Journal of Neuroscience Methods 222 (2014): 230-37. 2014. Web. 18 Mar. 2017. Shahbakhi, Mohammad, Danial Taheri Far, and Ehsan Tahami. "Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine."Journal of Biomedical Science and Engineering 07.04 (2014): 147-56. Scientific Research. July 2014. Web. 2 Mar. 2017. "Speech and Communication." Speech and Communication. Parkinson's Disease Foundation, n.d. Web. 22 Mar. 2017. Sriram, Tarigoppula V. S., M. Venkateswara Rao, G. V. Satya Narayana, and D. S. V. G. K. Kaladhar. "Diagnosis of Parkinson Disease Using Machine Learning and Data Mining Systems from Voice Dataset." SpringerLink. Springer, Cham, 01 Jan. 1970. Web. 17 Mar. 2017.
GitHub Repo
https://github.com/arenamallbwp/arenamallbwp
arenamallbwp/arenamallbwp
The Arena MALL - First ever Tallest Mall of Bahawalpur with 11 floors is a new gift of gratitude to the city of Bahawalpur with endless features and Facilities. The project of leading Real Estate Company Al Ahmad Builder and Real Estate Bahawalpur. . ARENA MALL BAHAWALPUR A comprehensive shopping complex including: Digital Parking Hyper Market Shopping Mall - National and International Brands Food Court JoyLand / Kid’s Play area Serviced Apartments Health Club / Bowling Alley Deluxe Swimming Pool Rooftop Restaurants Offering endless features that make the Arena Mall, one of its kind development. ARENA MALL HIGHLIGHTED FEATURES - With a Tradition of Excellence ARENA MALL is an impeccable mall with unmatched facilities and immaculate vision. Each Floor of the Mall is planned with a unique perspective. First ever Mall in Bahawalpur that gives the Centrally-Air conditioned system to give the pleasant experience to visitors. First ever Tallest mall (11 floors) in Bahawalpur as well as in south punjab with endless Facilities and features. Arena Mall Bahawalpur brings you the Digital parking system with 2 floors where you can park your vehicles. More than 200 cars and nearly 200 bikes Parking space. In the past and still bahawalpur and as well as south punjab is facing parking issues in malls. Here, Arena Mall Bahawalpur provides a complete solution of parking lots. Everyone wants to invest where it is worth investing for a tranquility living. ARENA MALL Bahawalpur brings you this opportunity at its premiere project where you can invest and live a life that is worth the living at its 1 and 2 Bedrooms Apartments Bowling Alley - It's a unique indoor game for sports lovers. And in south Punjab, for the first time, 𝑨𝑹𝑬𝑵𝑨 𝑴𝑨𝑳𝑳 is giving this opportunity to the people of Bahawalpur with dedicated hall for Bowling Alley. Bowling is played by 120 million people in more than 90 countries. Design And Structure Blending contemporary design with magnificence of the past The Design And Structure of Arena Mall reflects the superior architecture of Bahawalpur, western classic design of Building with modern facilities give a pleasant feeling to the people who visit. Modern Apartments are designed by Keeping in mind the culture and Royal Building of Bahawalpur. Arena MALL is the first ever shopping mall in Bahawalpur with the latest facilities with 2 floor parking space. Along with Unique and attractive architecture. Location https://goo.gl/maps/Zh2qMETXmksSLu5h7 The Arena is strategically located at prime location on the main railway road, adjacent to J. Outlet. This location is the most valuable and strategic in Bahawalpur and its value is well recognized. You can easily access the City's central location. Just 1 to 5 minutes drive from all City's Central Locations 1 Minute Drive to Saraiki Chowk and Fawara Chowk 1 Minute Drive to Model Town A B & C 2 Minutes Drive to Main KLP Road 2 Minutes Drive to Farid Gate & DCO Chowk 5 Drive to Airport and DHA Bahawalpur ARENA Mall NOC ARENA MALL Has Been Approved by 𝐌𝐞𝐭𝐫𝐨𝐩𝐨𝐥𝐢𝐭𝐚𝐧 𝐂𝐨𝐫𝐩𝐨𝐫𝐚𝐭𝐢𝐨𝐧 𝐁𝐚𝐡𝐚𝐰𝐚𝐥𝐩𝐮𝐫 Under Registration (TMA # 185). Arena Mall Official Arena Mall Serviced apartments Arena Mall Bahawalpur best investment in commercial project Arena Mall Construction construction Arena Mall Development status Arena Mall Location Arena Shopping Mall Best investment in commercial Projects Best commercial Projects in Bahawalpur First Ever Tallest Mall in south Punjab Arena Mall project construction update projects of Al Ahmad Builders Bahawalpur’s tallest mall Best real estate investment in South Punjab (Bahawalpur property investment Best shopping mall Bahawalpur
GitHub Repo
https://github.com/luiyusen97/HE3011_WTP_PPC
luiyusen97/HE3011_WTP_PPC
Project to calculate the Total Economic Value of People's Park Complex, Singapore and to find out the determinants of individuals' Willingness To Pay
GitHub Repo
https://github.com/huanqingxu/parkinson-disease-recongnition