Lahore is a historical and the second largest city of Pakistan. It has a unique geographical location as it is located on the main trade and invasion routes to South Asia. Its history dates back to 1000BC, when its foundations were laid by the Hindu prince Loh, son of Rama Chandra. After the invasion of Mahmud of Ghazni in 1000AD, the city of Lahore has grown, flourished, suffered invasions and destruction, and yet survived through the Sultanate (1206-1524), the Mughal (1524-1712) and Sikh (1764-1849) periods with an uneven, yet unbroken, cultural evolution. This is evident in the form of monuments and artefacts that developed and evolved over time. The research paper discusses how architecture and contemporary arts in Lahore developed with time through the examples of representative buildings as case studies. It also discusses the impacts of cultural, religious and social factors on the art and architecture during different rules and how they are embodied in the city of Lahore to contribute towards its unique identity. The Mughals, who ruled for almost three centuries, were famous as great builders. They laid the infrastructure of Lahore and built finest architectural monuments. They were succeeded by the Sikh dynasty, but with minor architectural impacts. However on the palimpsest set by the Mughals, the British managed to transform the city of Lahore into modern lines. Hence, through the introduction of new building types, the British presented art and architectural style that was not known before to give Lahore a new identity.
Cloud Computing (CC) is considered the latest emerging computing paradigm and has brought revolutionary changes in computing technology. With the advancement in this field, the number of cloud users and service providers is increasing continuously with more diversified services. Consequently, the selection of appropriate cloud service has become a difficult task for a new cloud customer. In case of inappropriate selection of a cloud services, a cloud customer may face the vendor locked-in issue and data portability interoperability problems. These are the major obstacles in the adoption of cloud services. To avoid these complexities, a cloud customer needs to select an appropriate cloud service at the initial stage of the migration to the cloud. Many researches have been proposed to overcome the issues but problems still exist in intercommunication standards among clouds and vendor locked-in issues. This research proposed an IEEE multi-agent FIPA (Foundation for Intelligent Physical Agent) compliance multi-agent reference architecture for cloud discovery and selection using cloud ontology. The proposed approach will mitigate the prevailing vendor locked-in issue and also alleviate the portability and interoperability problems in Cloud Computing. To evaluate the proposed reference architecture and compare it with the state-of-the-art existing approaches, several experiments have been performed by utilizing the commonly used performance measures. Analysis indicates that the proposed approach enables significant improvements in cloud service discovery and selection in terms of search efficiency, execution and response time.
Recent developments in communication and information technologies, especially in the internet of things (IoT), have greatly changed and improved the human lifestyle. Due to the easy access to, and increasing demand for, smart devices, the IoT system faces new cyber-physical security and privacy attacks, such as denial of service, spoofing, phishing, obfuscations, jamming, eavesdropping, intrusions, and other unforeseen cyber threats to IoT systems. The traditional tools and techniques are not very efficient to prevent and protect against the new cyber-physical security challenges. Robust, dynamic, and up-to-date security measures are required to secure IoT systems. The machine learning (ML) technique is considered the most advanced and promising method, and opened up many research directions to address new security challenges in the cyber-physical systems (CPS). This research survey presents the architecture of IoT systems, investigates different attacks on IoT systems, and reviews the latest research directions to solve the safety and security of IoT systems based on machine learning techniques. Moreover, it discusses the potential future research challenges when employing security methods in IoT systems.
A detected type of cancer is breast cancer commonly in women. According to some estimate one in nine women is diagnosed with breast cancer. It is unfortunate that due to a lack of proper facilities, the diagnosis of breast cancer in patients is being delayed, which is leading to an increase in the possible death rate. Many different statistical methods and Machine Learning algorithms are often employed in the study to make breast cancer detection more accurate. Machine learning (ML) has allowed doctors to achieve remarkable results, and healthcare is using ML-based models to detect breast cancer in women. This allows analyzing the healthcare data and uses the traditional computer-aided detection (CAD) to assess breast cancer. Machine learning has become an accepted clinical practice and allows doctors to evaluate the ML model to detect breasts at an early stage. A major aim is to diagnose patients with breast cancer by analyzing the data of patients and classifying them into two categories, having diagnosis results as Benign "B" or Malignant “M”. In this study different machine learning algorithms are used to classify cancer as either its malignant or benign. The Kaggle data set was used for applying these algorithms to get the best accuracy. MLP is more efficient and accurate algorithm to classify the breast tumor. And here also fitted the matthews_corrcoef for MLP is 0.89% and accuracy score for the random forest is 0.94%.
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