Foundation University Journal of Engineering and Applied Sciences
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Nanotechnologies: AI Weapons Governing the Military Battle Field
Get PDFWith major advantages and concerns, nanotechnology (NT) is expected to change severalindustries. New risks could emerge due tomilitary advancements, requiring additional planning and work to contain them. When it comes to military R&D, the NT is moving forward quickly. Future uses could benefit all military branches. Microrobots and new biological weapons could endanger stability and arms control. Manypeople are interested in nanotechnology as a scientific subject because of all the opportunities it offers. Nanorobots can be employed in variousfields, including materials science, space exploration, ecology, information technology, electronics, and communications.On the other hand, these novel uses for nanorobotics in military applications and armament are revolutionary. An essay on the most recent developments in military nanorobot applications has been made available. Due to its fundamentally revolutionary advantages, military nanotechnologies have been argued to be more lethal than nuclear weapons for the entire planet and capable of being used in all conflict zones
Multi-Criteria Decision Support System for Recommendation of PhD Supervisor
Get PDFA decision support system (DSS) is a computerized system used to discover determinations, judgments, and courses of action within a business or an organization. In every arena of life, DSS play an important role. In higher education, DSSs are recognized for diverse reasons, for example to conjugate data and intelligence, to pull off the unrivaled and likely interpretations, and to fine-tune decisions under hesitancy. The PhD supervisorselection is itself a very difficult and mind tickling task which makes the student very nervous and sometime make him desperate because he is unable to make the right decision. This work will facilitate the scholars to get proper guidance to make it successful. For the selection of particular supervisor there are some set of criterions which a supervisor should also follow while selecting a particular scholar. Though, the lack of information about the supervisor can hamper scholars in making the selection of the supervisor. Correspondingly, the identification of thought-provoking criteria might be stimulating for potential scholars due to their level of immaturity. Therefore, a system is required which can facilitate scholars in selecting the research work advisors in accordance with the research topic based on multi-criteria like relevancy of the supervisor’s research area of interest and the relevance of the publications. In this research effort, a user-friendly conceptual DSS framework has been suggested to recommend the Ph.D. supervisor to scholars in academics. A multi-criteria DSS framework has been proposed for the facilitation of the scholars while selecting their Ph.D. supervisor. This recommendation is based on several criteria of selecting potential supervisor including the area of research interest along with the publications i.e., in journals and conferences in addition to number of Ph.Ds. produced so far. Finally, the publications of the Ph.D. produced so far and the research projects involved. Preliminary results of the proposed work has been discussed along with the future directions
Decision Support System for Measuring the User Sentiment towards Different COVID-19 Vaccines
Get PDFIt's been a long time since the (COVID-19) engulfed the entire planet, upsetting normal schedules, destroying economies, and killing millions of people all over the world. The pandemic brought the entire world together in an endeavorto discover a cure and promote inoculation. The first round of vaccines began near the end of 2020, contrary to popular belief, and various nations began the inoculation drive very quickly while others keptit together fully expecting an effective preliminary. Web-based media is blockedwith a wide scope of both positive and negative stories in the developing Covid conditions. Numerous individuals were anticipating the vaccination, while others were mindful about the side effects and the fear-inspirednotions bringing about mixed emotions. This article performs sentiment analysis, which will be utilized in a choice emotionally supportive network in discovering the viability of COVID-19 vaccines among various nations.We have trained deep long short-term memory (LSTM) models to achieve state-of-the-art accuracy in estimating sentiment polarity and emotional state from extracted tweets.Theproposed technique decides public sentiments towards COVID-19 vaccines assisting the healthcare authorities with breaking down their reaction. The results show the mentality of individuals towards various vaccine brands as for their various responses to the Covid-19 vaccines
Automatic Facial Expression Analysis and Recognition using Zone Based Active and Salient Facial Patches
Get PDFRecognition of facial expression has many useful applications that have drawn researcher’s interest over the past decade. Extraction of features is a major step in the analysis of expression which leads to fast and accurate recognition of expression. Recognition of facial expressions is not an easy issue for methods of machine learning, as different people can vary in the way they show their expressions and for one expression the image of the same person can differ for brightness, background and position. Recognition of facial expression is therefore still a challenging computer vision problem. In this thesis work, we aim to design a robust technique of automatic facial expression analysis and recognition using zone based active and salient patches of the human face by combining various techniques from computer vision and pattern recognition. Expression recognition is closely related to face recognition where a lot of research has been done and a vast array of algorithms have been introduced. Facial expression recognition (FER) can also be considered as a special case of a pattern recognition problem and many techniques are available. In the designing of an FER system, we divided the system into 4 modules, i.e. preprocessing, active and salient patch extraction and classification. Voila Jones algorithm is used for face detection and after that features are extracted from the facial patches. The active facial patches are located on the facial regions that during different expressions undergo a major change. The active patches are located after detection of facial landmarks and hybrid features are determined from these patches. The use of small parts of face instead of the whole face for extracting features reduces the computational cost. Zoning is applied and got remarkable results. The dimensionality of the function is reduced by using linear discriminant analysis, which is further defined using the support vector machine (SVM). On the basis of classification expression is recognized. We evaluated our algorithm on Extended Cohn-Kanade (CK+) dataset
Facial Based Gender Classification for Real Time Applications
Get PDFAppearance and facial features play an important role in gender recognition through images. For gender classification, multiple techniques were presented to acquire better results in which preprocessing part is one of the major and very important for gender classification as it removes noise, enhances, images, and eliminates any unnatural colors from an image. Another major aspect is the efficient feature extraction method. If features extracted accurately then the result of classification will improve. Over the past few years, gender classification techniques work perfectly for a controlled environment. However, challenges occurred for real-time applications due to low resolution, off-angle poses, faces with occlusion, and various expressions. The main focus of this study is to overcome existing challenges and propose a method that can be implemented in real-time applications. This research work proposed a novel method in which CNN has been used for classification of gender for real-time application. To assess the performance of proposed method experiments were conducted on static images and video data sets. The proposed research work achieved 98% of accuracy during the experiments
Data Mining Assisted Purchase Prediction
Get PDFWith the revolution from physical businesses to shopping online, predicting client behavior in e-commerce is becoming increasingly important. It can increase customer satisfaction and sales, resulting in higher conversion, by enabling a more individualized shopping process. Today, most users want to save their time using and they prefer to shop using the platform provided for e-commerce. Millions of transaction records are available in the databases of such websites using which, a customer shops something. Using the transaction to find something can be helpful for the organization or merchants. Using the available databases or datasets, to find some useful pattern can increase the business, to check out the customer satisfaction level, to check the customer behavior about the product, etc. Some of the useful information can be to find out which item will be purchased by the customer in the next visit, or which new items can be purchased by the customer in the next visit. Using this information, an organization or Shops can control the quantity and increase the maximum purchased items, improving the quality of products for the customers. For this purpose, we use supervised learning techniques for prediction. Because most of the data which we will use will be labeled. Many researchers used supervised methods but some of the researchers also used unsupervised methods too. We created a supervised model for predicting the basket items. Due to the large dataset, it was very difficult to extract the features and it takes a lot of time. We have performed feature engineering, to choose the best ones. After the training model, our model shows better performance than the previous results. 
Urdu Sentiment Analysis Using Deep Attention-Based Technique
Get PDFSentiment analysis (SA) is a process that aims to classify text into positive, negative or neutral categories. It has recently gained the research community's attentionbecause of the abundance of opinion data on the internet. Deep learning techniques are widely used for language processing but are seen as black boxes, and their effectiveness comes ininterpretability. The major goal of this article is to create an Urdu SA model that can comprehend review semantics without the need of language resources. Wedesign an attention-based neural network for the review level Urdu SA. For better results, we used atransfer learning approach that uses pre-trained embedding’s. The Visualization of attention weights isalso measuredthat uncovers the black box of the models and confirms their intuition, which aids in the interpretation of the model's learned representations. The proposed model is tested and evaluated in terms of accuracy and F1 score. The proposed model archives 91% accuracy and 88% F1 score,respectively
Intrusion Detection in Cyber Space Using Machine Learning Based Algorithm
Get PDFNow a day, the fast growth of Internet access and the adoption of smart digital technology has resulted in new cybercrime strategies targeting regular people and businesses. The Web and social activities take precedence in most aspects of their lives, but also poses significant social risks. Static and dynamic analysis are inefficient in detecting unknown malware in standard threat detection approaches. Virus makers create new malware by modifying current malware using polymorphic and evasion tactics in order to fool. Furthermore, by utilizing selection of features techniques to identify more important features and minimizing amount of the data, these Machine Learning models' accuracy can be increased, resulting in fewer calculations. In the previous study traditional machine learning approaches were used to detect Malware. We employed Cuckoo sandbox, a malware detection and analysis system for detection and categorization, in this study we provide a Machine Learning based Intrusion analysis system to calculate exact and on spot Intrusion classification. We integrated feature extraction and component selection from the file, as well as selecting the much higher quality, resulting in exceptional accuracy and cheaper computing costs. For reliable identification and fine-grained categorization, we use a variety of machine learning algorithms. Our experimental results show that we achieved good, classified accuracy when compared to state-of-the-art approaches. We employed machine learning techniques such as K-Nearest Neighbor, Random Forest, Support Vector Machine, and Decision Tree. Using the Random Forest classifier on 108 features, we attained the greatest accuracy of 99.37 percent. We also discovered that Random Forest outscored all other classic machine learning techniques during the procedure. These findings can aid in the exact and accurate identification of Malware families
The Power of Networks: A Pre-Requisite to Social Network Analysis
Get PDFSocial network analysis has attained significant attention in recent times. It is a contemporary data driven technique which maps and measures social structures using network and graph theory for people analytics. A pre-requisite to social network analysis is to explore the network genesis and its evolution. Most of the social network studies in the past have focused primarily on the impact of social networks on employees but none of the studies have identified the reasons why do employees bond with specific network members in the first place and what are the different types of workplace social networks. For this reason we wanted to conduct a systematic literature review for identifying the reasons why specific workplace social networks exists, classify different types of workplace networks and highlight network ties which are beneficial for achieving organizational goals. By employing social network theory, this study investigates the intra-organizational networks of corporate business environment. Multiple databases were used to extract relevant papers for this review such as Science Direct, IEEE Xplore, Scopus and Web of Science databases and Directory of Open Access Journals. Some of the major trends identified from the 48 past papers included in this review are: (1) different types of workplace social networks are created based on specific employee interdependency; (2) strong and weak ties both are beneficial for fast information diffusion; (3) negative ties have a stronger impact on organizational outcomes then positive ties. This review will provide a new broader conceptualization of social networks and its influence on employees.  
Effect of Preprocessing and No of Topics on Automated Topic Classification Performance
Get PDFThe emergence of the Internet has caused an increasing generation of data. A high amount of the data is of textual form, which is highly unstructured. Almost every field i.e, business, engineering, medicine, and science can benefit from the textual data when knowledge is extracted. The knowledge extraction requires the extraction and recording of metadata on the unstructured text documents that constitute the textual data. This phenomenon is regarded as topic modeling. The resulting topics can ease searching, statistical characterization, and classification. Some well-known algorithms for topic modeling include Latent Dirichlet Allocation (LDA), Nonnegative Matrix Factorization (NMF), and Probabilistic Latent Semantic Analysis (PLSA). Different parameters can affect the performance of topic modeling. An interesting parameter could be the time required to perform topic modeling. The fact that time is affected by many factors applicable to topic modeling as well; however, measuring the time concerning some constraints can be beneficial to provide insight. In this paper, we alter some preprocessing steps and topics to study their impact on the time taken by the LDA and NMF topic models. In preprocessing, we limit our study by altering only the sampling and feature subset selection whereas in the second step we have changed the number of topics. The results show a significant improvement in time