Inability to speak or to talk is called muteness or mutism; often caused by speech disorder, loss of hearing or in some cases, surgery. The families of these people suffering from such disease often learn sign languages like American Sign Language (ASL) to communicate, but if a mute person meets someone who doesn’t know ASL then there will be a communication barrier between them. So, to overcome this communication barrier we require a system through which a mute person can communicate with everyone, hence breaking down a speech barrier. In this regard, we proposed a system based on image processing to identify the sign and perform some common speech patterns using the speaker.

M-Commerce is widely known as wireless network technology, use as the essential means of communication for business transactions. Mobile commerce is seen as an augmentation of E-commerce empowered by cell phones. Transactions either indirectly or directly carried out or supported through mobile phones. ABC Brokerage House is the pioneer in Online Trading Services. In online trade, customers can trade using the online trade software and website. Before switching users, from traditional trading system to mobile trading system, the weaknesses of infrastructure, security breaches, and risk factors should be considered carefully. The infrastructure solution should be designed with respect to interoperability, security, scalability, non-repudiation of ABC Brokerage House on the behalf of best practices for Mobile Commerce Trading System. The solution has its distributed nature and is a suitable architecture for managing business processes of ABC Stock Brokerage House. The solution is well planned, designed and it is in accordance with ISO recommendation. The solution provides more flexibility and also provides redundancy by using clustering of database server. The proposed solution provides more flexibility and also provides redundancy plan and clustering of database server. This solution also switches ABC Brokerage House infrastructure to the real world infrastructure by removing risk factors and implementing the latest technologies.

In Pakistan, construction and demolition waste (CDW) is generated in voluminous amount each year. CDW is widely ill-handled and ultimately fed to landfills causing harm to the already alarming environmental conditions. In order to search for the solution of this drastic matter, a study was done, which is explained in this paper. This paper presents the study done at a demolition site near Karachi, in Sindh while the demolition works were being carried out. At the site there were old barracks which were being demolished. Before the demolition works were commenced, the site was surveyed and structural components of the barracks were counted and their dimensions were measured. When the demolition was over, the demolished waste was calculated which comprised of concrete and masonry rubble, steel round bars, steel doors, steel windows, steel ceiling, steel girders, steel main gate, and plastic water tank. This study interpreted that construction and demolition (C&D) works were progressing considering the works’ deadline and the clients’ requirements but the ecosystem’s ecology and the environmental health were not taken into account. Recommendations are made to handle CDW properly throughout its lifecycle. These recommendations aim to provide technological and logical solutions to grip CDW. The recommendations include waste reduction and reusing waste, life cycle assessment and costing, environmental and economic impact, material flow analysis, and advanced computerized-tools.

In Pakistan, the majority of the local construction projects are opted without feasibility analysis on the basis of previously worked data without examining the current scenario. However, for foreign-funded project feasibility analysis along with standard guidelines is conducted, but in case of domestically funded projects, it is often omitted. This research has uncovered critical factors affecting feasibility for the local construction industry of Pakistan. Field data are extracted from ongoing construction projects which are domestically funded. Relative Importance Index (RII) analysis was carried out first to rank the factors in terms of their perceived importance. Spearman’s rank correlation analysis was also done in this regard. The findings of the study might promote construction practitioners to focus on the need for feasibility analysis for local projects too.

Brain cancer has remained one of the key causes of deaths in people of all ages. One way to survival amongst patients is to correctly diagnose cancer in its early stages. Recently machine learning has become a very important tool in medical image classification. Our approach is to examine and compare various machine learning classification algorithms that help in brain tumor classification of Magnetic Resonance (MR) images. We have compared Artificial Neural Network (ANN), K-nearest Neighbor (KNN), Decision Tree (DT), Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers to determine the accuracy of each classifier and find the best amongst them for classification of cancerous and noncancerous brain MR images. We have used 86 MR images and extracted a large number of features for each image. Since the equal number of images, have been used thus there is no suspicion of results being biased. For our data set the most accurate results were provided by ANN. It was found that ANN provides better results for medium to large database of Brain MR Images.