2022 Mohammad Ali Jinnah University International Conference on Computing (MAJICC)最新文献

Feature Selection via GM-CPSO and Binary Conversion: Analyses on a Binary-Class Dataset 基于GM-CPSO和二值转换的特征选择:基于二值类数据集的分析

2022 Mohammad Ali Jinnah University International Conference on Computing (MAJICC) Pub Date : 2022-10-27 DOI: 10.1109/MAJICC56935.2022.9994150

Şevval Çeli̇k, Hasan Koyuncu

{"title":"Feature Selection via GM-CPSO and Binary Conversion: Analyses on a Binary-Class Dataset","authors":"Şevval Çeli̇k, Hasan Koyuncu","doi":"10.1109/MAJICC56935.2022.9994150","DOIUrl":"https://doi.org/10.1109/MAJICC56935.2022.9994150","url":null,"abstract":"Feature selection is oft-used to upgrade the system performance in classification-based applications. For this purpose, wrapper-based methods reserve an important place and are designed with efficient optimization methods so as to observe the highest performance. In this paper, a state-of-the-art optimization method named Gauss map-based chaotic particle swarm optimization (GM-CPSO) is handled. Binary conversion is considered to adapt the GM-CPSO to the feature selection. In classification part of the proposed method, k-nearest neighborhood (k-NN) is operated due to its fast and robust performance on classification-based implementations. In experiments, seven metrics (accuracy, sensitivity, specificity, g-mean, precision, f-measure, AUC) are utilized to objectively evaluate the performances, and 80%/20% training-test split is fulfilled to effectively assign the necessary features. Our wrapper-based method is tested on a balanced dataset that is based on Parkinson's disease (PD). As a result, our method presents promising scores by means of seven metrics, and especially, it improves the classification performance about 14.59% concerning the accuracy and AUC rates in comparison with the k-NN method.","PeriodicalId":205027,"journal":{"name":"2022 Mohammad Ali Jinnah University International Conference on Computing (MAJICC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114366490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating Blockchain with IoT for Mitigating Cyber Threat In Corporate Environment 将区块链与物联网相结合，减轻企业环境中的网络威胁

2022 Mohammad Ali Jinnah University International Conference on Computing (MAJICC) Pub Date : 2022-10-27 DOI: 10.1109/MAJICC56935.2022.9994206

Ali Dharani, S. M. Khaliq-ur-Rehman Raazi

引用次数: 0

An IoT Based Heart Healthcare Platform for the Sultanate of Oman 阿曼苏丹国基于物联网的心脏医疗保健平台

2022 Mohammad Ali Jinnah University International Conference on Computing (MAJICC) Pub Date : 2022-10-27 DOI: 10.1109/MAJICC56935.2022.9994183

Vimal Kumar Stephen, Mathivanan Virutachalam, Antonio Rutaf Manalang, Mohammed Tariq Shaikh

{"title":"An IoT Based Heart Healthcare Platform for the Sultanate of Oman","authors":"Vimal Kumar Stephen, Mathivanan Virutachalam, Antonio Rutaf Manalang, Mohammed Tariq Shaikh","doi":"10.1109/MAJICC56935.2022.9994183","DOIUrl":"https://doi.org/10.1109/MAJICC56935.2022.9994183","url":null,"abstract":"The evolution of the Internet of Things (IoT) has been from the convergence of different forms of digital technologies such as embedded systems, real-time analytics, wireless communication, and sensors. The rise of cardiovascular disease (CVD) among adults in Oman has become a growing concern. All IoT-driven healthcare and wellness systems facilitate a continuous form of monitoring of several chronic conditions. The use of IoT healthcare platforms has a huge positive impact in providing timely help and improvement in general well-being. An abnormal situation caused due to irregular heartbeat rate is called arrhythmia and this may become dangerous as the cardiac system is affected due to aging and other pathological and sociological factors. In order to diagnose this abnormality, electrical impulses produced by the heart are recorded by equipment called Electrocardiogram (ECG). A wearable ECG device is used to monitor the patients heartbeats through the IoT platform. The ECG signals to arrhythmia classes are classified using Convolutional Neural Networks (CNN). 1D CNN techniques is used in state-of-the-art modern research, in order to classify this signal. Tabu Search (TS) algorithm with CNN, is used in this work to classify ECG signal image. The evaluation of the technique is done based on performance evaluation matrices which can produce enhanced outcomes, when compared to the present literature.","PeriodicalId":205027,"journal":{"name":"2022 Mohammad Ali Jinnah University International Conference on Computing (MAJICC)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128460765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Heart Failure Prediction Using Machine learning Approaches 使用机器学习方法预测心力衰竭

2022 Mohammad Ali Jinnah University International Conference on Computing (MAJICC) Pub Date : 2022-10-27 DOI: 10.1109/MAJICC56935.2022.9994093

A. Abbas, Azhar Imran, Abdulkareem A. Najem Al-Aloosy, Safa Fahim, Abdulkareem Alzahrani, Samia Khalood Muzaffar

{"title":"Heart Failure Prediction Using Machine learning Approaches","authors":"A. Abbas, Azhar Imran, Abdulkareem A. Najem Al-Aloosy, Safa Fahim, Abdulkareem Alzahrani, Samia Khalood Muzaffar","doi":"10.1109/MAJICC56935.2022.9994093","DOIUrl":"https://doi.org/10.1109/MAJICC56935.2022.9994093","url":null,"abstract":"Heart Failure (HF) is a familiar disease that can rise to a dangerous situation in today's world. It is currently one of the most dangerous heart diseases in humans, and it seriously shortens people's lives. Heart failure can be prevented in its early stages and will increase the patient's survival if human heart disease is accurately and quickly identified. Manual methods are biased and subject to interexaminer variability when used to diagnose cardiac disease. To Predict heart failure at the correct time is difficult from the perspective of a heart specialist and surgeon. Luckily, prediction and classification models exist, which can assist the medical industry and demonstrate how to effectively use medical data. In this regard, machine learning algorithms are effective and efficient methods to identify and classify patients with heart disease and healthy individuals. According to the proposed study, we used a variety of machine learning algorithms to identify and predict human heart disease, and we used the heart disease dataset to evaluate the performance of those algorithms using various metrics, including classification accuracy, F measure, sensitivity, and specificity. Several types of machine learning algorithms are used to estimate the probability of having heart failure in a medical database. For this purpose, we used nine machine learning classifiers, including DT, LR, GBe, NB, KNN, SVM, ADB, RF, and XGB, to the final dataset before and after hyperparameter tuning. By successfully completing preprocessing, dataset standardisation, and hyperparameter tuning, we also check their accuracy on the standard heart disease dataset. Last but not least, the experimental results indicated that data standardisation and hyperparameter tuning of the machine learning classifiers significantly improved the prediction classifiers' accuracy.","PeriodicalId":205027,"journal":{"name":"2022 Mohammad Ali Jinnah University International Conference on Computing (MAJICC)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121138780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Evaluating Automatic CV Shortlisting Tool For Job Recruitment Based On Machine Learning Techniques 基于机器学习技术的自动简历筛选工具评估

2022 Mohammad Ali Jinnah University International Conference on Computing (MAJICC) Pub Date : 2022-10-27 DOI: 10.1109/MAJICC56935.2022.9994112

Muntaha Mehboob, M. S. Ali, Saif Ul Islam, Syed Sarmad Ali

引用次数: 1

An Automated Question Answering(Q/A)System For Academic Environment 一种适合学术环境的自动问答系统

2022 Mohammad Ali Jinnah University International Conference on Computing (MAJICC) Pub Date : 2022-10-27 DOI: 10.1109/MAJICC56935.2022.9994223

Mubasher Yousuf, Syed Imran Jami

{"title":"An Automated Question Answering(Q/A)System For Academic Environment","authors":"Mubasher Yousuf, Syed Imran Jami","doi":"10.1109/MAJICC56935.2022.9994223","DOIUrl":"https://doi.org/10.1109/MAJICC56935.2022.9994223","url":null,"abstract":"Almost every Internet user has daily many generals enquires. Users can solve these queries from the Internet Search Engines (ISE). These search engines provide the articles or web pages as result. Users need to expend time on the Internet to get the exact result. Sometimes users may also physically visit the location to get resolve their queries. The academic domain has applicants and students who have more queries about academics. These both have lots of queries and ISEs can't solve these queries. Ontology is used to represent domain-specific data. In this study, Ontology has the format of functional-style syntax. Using Ontology with Semantic Web Rule Language (SWRL) technology. We introduce a domain-specific general Academic Question Answering (Q/A) System for academics. This Q/A System can cover most of the important departments of academics. A user can ask from the system through the submission of data values for a query. Academia Ontology is generated through the Protégé tool. The answer data is retrieved from data properties for applying the relevant rules. Rules are generated with Semantic Web Rule Language (SWRL). This is open to getting focused on Academics Q/A Systems and further applying to other domains with the Semantic Web and Ontology-based approaches. The experimental results explain the feasibility of the Q/A System. This system can respond with more accurate and exact results with an F-Measure of 48%. This Academic Q/A System has the potential to solve the general queries of most of the stakeholders in academia. This Q/A System has been produced to answer a variety of questions in academia. This system can further be extended to embed a natural language interface by employing Natural Language Processing. Further work includes the extension of systems to other domains as well e.g. financial institutions, and government bodies.","PeriodicalId":205027,"journal":{"name":"2022 Mohammad Ali Jinnah University International Conference on Computing (MAJICC)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123423351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Gender Classification Using Smartphone Sensors and Machine Learning Approaches 使用智能手机传感器和机器学习方法进行性别分类

2022 Mohammad Ali Jinnah University International Conference on Computing (MAJICC) Pub Date : 2022-10-27 DOI: 10.1109/MAJICC56935.2022.9994132

Abdul Basit, Muhammad Yaseen Khan, Syed Sarmad Ali, Muhammad Suffian, Abdul Wajid, Sumra Khan

引用次数: 0

Multi Attributes Recognition from Human Gait Analysis using MotionSense Dataset 基于MotionSense数据集的人体步态多属性识别

2022 Mohammad Ali Jinnah University International Conference on Computing (MAJICC) Pub Date : 2022-10-27 DOI: 10.1109/MAJICC56935.2022.9994092

Kainat Ibrar, A. Shaikh, Shakeel Zafar

{"title":"Multi Attributes Recognition from Human Gait Analysis using MotionSense Dataset","authors":"Kainat Ibrar, A. Shaikh, Shakeel Zafar","doi":"10.1109/MAJICC56935.2022.9994092","DOIUrl":"https://doi.org/10.1109/MAJICC56935.2022.9994092","url":null,"abstract":"Human Gait analysis is a very prodigious and flourishing field of research nowadays, due to its immense importance in clinical and medical studies, rehabilitation, security and surveillance, crime investigation, health, sports, development of marketing applications and product optimization etc. Every human has a distinctive gait pattern, which with critical scrutiny may exhibit a lot of information about his identity and personal traits. Although researchers have made remarkable efforts in this field of research but there is a lack of work regarding sensorial gait analysis for identifying multi-attributes of a person. This paper proposes a novel framework to recognize multi-attributes i.e., user, gender, age and weight of a person based on gait analysis using smartphone built-in sensors including accelerometer, gyroscope and motion sensor. We have used an existing dataset named “MotionSense” for human activity and attributes recognition. Multi-class machine learning algorithms are applied for training the dataset. We have achieved the accuracy of 99.75% for User, 99.74% for gender, 99.61% for age and 99.74% for weight recognition respectively. Experimental results and performance evaluation of the applied machine learning classifiers reveals the efficacy of the proposed scheme.","PeriodicalId":205027,"journal":{"name":"2022 Mohammad Ali Jinnah University International Conference on Computing (MAJICC)","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130741899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Corporate Information Security Policies Targeting Ransomw are Attack 针对勒索攻击的企业信息安全政策

2022 Mohammad Ali Jinnah University International Conference on Computing (MAJICC) Pub Date : 2022-10-27 DOI: 10.1109/MAJICC56935.2022.9994155

Syed Naeem Ahmed, Raazi M. K. Syed, Rashid Kamal, Mubashir Khan

{"title":"Corporate Information Security Policies Targeting Ransomw are Attack","authors":"Syed Naeem Ahmed, Raazi M. K. Syed, Rashid Kamal, Mubashir Khan","doi":"10.1109/MAJICC56935.2022.9994155","DOIUrl":"https://doi.org/10.1109/MAJICC56935.2022.9994155","url":null,"abstract":"The ransomware attacks have created challenges for the entire world today and industries are getting affected from such sophisticated attacks, whether they are healthcare, educational, financials or any other service sectors, they are not safe from these malware attack. In these types of attacks, user data is encrypted or inaccessible to the victim, the hacker then demands money from the victim to give them access to their data after payment is done. This study guides how to mitigate ransomware attacks by adopting corporate information security policies in the organization with timely complete compliance. Ransomware is often designed to spread across networks and target information asset of organization, in healthcare Electronic Medical Record, HIMS, Database Server, File Servers, Application Server, Web Server, Domain Controllers and all associated & connected devices including IOT device, SCADA (Supervisory Control and Data Acquisition), once they are targeted the entire organization operations can be halted and paralyzed. Ransomware frequently changes its techniques to exploit the vulnerability, this research is based on technical & administrative controls, security standards, procedures, guidelines, best practices by following security frameworks i.e. (ISO 27001, HIPPA, and NIST) and the objective is to mitigate the attacks.","PeriodicalId":205027,"journal":{"name":"2022 Mohammad Ali Jinnah University International Conference on Computing (MAJICC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127467535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LSTM Based Deep Learning Model for Blood Sugar Prediction 基于LSTM的深度学习血糖预测模型

2022 Mohammad Ali Jinnah University International Conference on Computing (MAJICC) Pub Date : 2022-10-27 DOI: 10.1109/MAJICC56935.2022.9994178

Muhammad Muneeb Siddiqui, Rauf Ahmed Shams Malick, Ghufran Ahmed

引用次数: 0