On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors

Deep learning based medical image classifiers have shown remarkable prowess in various application areas like ophthalmology, dermatology, pathology, and radiology. However, the acceptance of these Computer-Aided Diagnosis (CAD) systems in real clinical setups is severely limited primarily because their decision-making process remains largely obscure. This work aims at elucidating a deep learning based medical image classifier by verifying that the model learns and utilizes similar disease-related concepts as described and employed by dermatologists. We used a well-trained and high performing neural network developed by REasoning for COmplex Data (RECOD) Lab for classification of three skin tumours, i.e. Melanocytic Naevi, Melanoma and Seborrheic Keratosis and performed a detailed analysis on its latent space. Two well established and publicly available skin disease datasets, PH2 and derm7pt, are used for experimentation. Human understandable concepts are mapped to RECOD image classification model with the help of Concept Activation Vectors (CAVs), introducing a novel training and significance testing paradigm for CAVs. Our results on an independent evaluation set clearly shows that the classifier learns and encodes human understandable concepts in its latent representation. Additionally, TCAV scores (Testing with CAVs) suggest that the neural network indeed makes use of disease-related concepts in the correct way when making predictions. We anticipate that this work can not only increase confidence of medical practitioners on CAD but also serve as a stepping stone for further development of CAV-based neural network interpretation methods.Comment: Accepted for the IEEE International Joint Conference on Neural Networks (IJCNN) 202

Flow-Based Rules Generation for Intrusion Detection System using Machine Learning Approach

Rapid increase in internet users also brought new ways of privacy and security exploitation. Intrusion is one of such attacks in which an authorized user can access system resources and is major concern for cyber security community. Although AV and firewall companies work hard to cope with this kind of attacks and generate signatures for such exploits but still, they are lagging behind badly in this race. This research proposes an approach to ease the task of rules generationby making use of machine learning for this purpose. We used 17 network features to train a random forest classifier and this trained classifier is then translated into rules which can easily be integrated with most commonly used firewalls like snort and suricata etc. This work targets five kind of attacks: brute force, denial of service, HTTP DoS, infiltrate from inside and SSH brute force. Separate rules are generated for each kind of attack. As not every generated rule contributes toward detection that's why an evaluation mechanism is also used which selects the best rule on the basis of precision and f-measure values. Generated rules for some attacks have 100% precision with detection rate of more than 99% which represents effectiveness of this approach on traditional firewalls. As our proposed system translates trained classifier model into set of rules for firewalls so it is not only effective for rules generation but also give machine learning characteristics to traditional firewall to some extent.&nbsp

THE RELATIONSHIP STUDY BETWEEN COHESION AND PERFORMANCEOF PLAYERS OF HOCKEY, IN PAKISTAN

This paper represents the findings of a larger study which highlights the relationship between cohesion and the performance among hockey players of Pakistan in view of socio-interactional context. Pakistan won laurels in Field hockey with four World Cup and three Olympics titles to its credit but no effort has been made to find out the factors which have turned Pakistan (as a team) from the status of a giant into a pygmy during the last two decades. The foremost objective of this paper is to scrutinize the relationship between cohesion and players’ performance. A sample of 296 players from 14sport departments was chosen as respondents. Adopted questionnaire was used to collect the survey data. The findings highlighted the significant (p=.001) relationship between cohesion and players’ performance. It is concluded that the cohesiveness among players is to be developed and expanded regarding players’ performance to fulfil sport requirements. Recommendations have been made to raise the excellence, relevance, and legitimacy in team regarding cohesion with players’ performance

THE RELATIONSHIP STUDY BETWEEN COHESION AND PERFORMANCEOF PLAYERS OF HOCKEY, IN PAKISTAN

This paper represents the findings of a larger study which highlights the relationship between cohesion and the performance among hockey players of Pakistan in view of socio-interactional context. Pakistan won laurels in Field hockey with four World Cup and three Olympics titles to its credit but no effort has been made to find out the factors which have turned Pakistan (as a team) from the status of a giant into a pygmy during the last two decades. The foremost objective of this paper is to scrutinize the relationship between cohesion and players’ performance. A sample of 296 players from 14sport departments was chosen as respondents. Adopted questionnaire was used to collect the survey data. The findings highlighted the significant (p=.001) relationship between cohesion and players’ performance. It is concluded that the cohesiveness among players is to be developed and expanded regarding players’ performance to fulfil sport requirements. Recommendations have been made to raise the excellence, relevance, and legitimacy in team regarding cohesion with players’ performance

THE SPORT PARTICIPATIONS AND SOCIAL CLASS RELATIONSHIP AMONG FEMALE ATHLETE OF PAKISTAN

The drive of the existing research was to determine the relationship between social class (gender inequality and dress code) and sports participation among successful female athletes of secondary schools of rural areas of Pakistan. The nature of the study was purely quantitative. The adopted and modified survey questionnaire was employed for the purpose of collecting the data. Simple random sampling was used as a sampling technique. In the sense of statistical techniques, descriptive statistics and correlation (Pearson) analysis was utilized to analyze the survey data. The results revealed that gender inequality and dress code had strong and significant relationships with sports participation (health, personal drive and interest), therefore, the direction of all relationships was found negative. It was concluded that both gender inequality and dress code found big hurdles in the way to female sports participation