2022 3rd International Conference on Innovations in Computer Science & Software Engineering (ICONICS)最新文献_第2页

Predicting Students' Behavior Towards their Degree using Machine Learning Techniques 使用机器学习技术预测学生对学位的行为

2022 3rd International Conference on Innovations in Computer Science & Software Engineering (ICONICS) Pub Date : 2022-12-14 DOI: 10.1109/ICONICS56716.2022.10100533

K. Mahboob, R. Asif, S. Mustafa, Humaira Rana

{"title":"Predicting Students' Behavior Towards their Degree using Machine Learning Techniques","authors":"K. Mahboob, R. Asif, S. Mustafa, Humaira Rana","doi":"10.1109/ICONICS56716.2022.10100533","DOIUrl":"https://doi.org/10.1109/ICONICS56716.2022.10100533","url":null,"abstract":"The phase of professional education is the most crucial part of a student’s life. One’s career might solely depend on how well the performance is. For better performance, a perfect attitude or behavior of the student is needed towards the degree. If a student is having a good mindset for the degree pursued, it will help them gain better results. In this paper, we are investigating the behavior of students toward their degrees. We conducted a survey and collected data from bachelors, masters, and doctorate students. To predict students’ behavior toward their degrees, we applied Machine Learning algorithms. We used a support vector machine, linear regression, k-nearest neighbor, naive bayes, and decision tree techniques to classify and predict the behaviors of students. Out of these techniques, the support vector machine performed well giving an accuracy of 59%. We applied the k-fold method to find the results. According to the results, 52.6% of students are optimistic about their degree, 40% consider it trustworthy, 3.5% think it is untrustworthy and 3.9% are pessimistic about their degree. Knowing the behavior or interest of students in their degree can help in boosting their productivity and increase their performance.","PeriodicalId":308731,"journal":{"name":"2022 3rd International Conference on Innovations in Computer Science & Software Engineering (ICONICS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124894951","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

Machine Learning Based Impostor Detection By Invariant Features From Nir Finger Vein Imaging 基于机器学习的近红外手指静脉图像的不变性特征检测

2022 3rd International Conference on Innovations in Computer Science & Software Engineering (ICONICS) Pub Date : 2022-12-14 DOI: 10.1109/ICONICS56716.2022.10100461

A. Rasool, Faisal Rehman, Nadeem Sarfaraz, Hana Sharif, Rashid Khan, Abdul Manan Khan

{"title":"Machine Learning Based Impostor Detection By Invariant Features From Nir Finger Vein Imaging","authors":"A. Rasool, Faisal Rehman, Nadeem Sarfaraz, Hana Sharif, Rashid Khan, Abdul Manan Khan","doi":"10.1109/ICONICS56716.2022.10100461","DOIUrl":"https://doi.org/10.1109/ICONICS56716.2022.10100461","url":null,"abstract":"Biometrics mostly used as personal identification accordingly securing a client against the unapproved utilization of his or her identity. Acquiring biometric information is getting to be simpler. Smartphones and other advanced technologies exist from which biometric data can collect easily without the knowledge of others. Finger vein authentication is a method for biometric verification that depends on a vein pattern, which is located beneath the human finger’s skin. Veins are covered with skin that cannot be copied by others. In this research, our focus is on these invariant features of finger veins. We have collected invariant features from various recent features extraction techniques and then classified with state-of-the-art machine learning classifiers. For this purpose, we have used publicly available finger vein image databases. The performance has been evaluated by various evaluation metrics and comparative analysis of various machine learning classifiers has been presented to describe the performance of these classifiers on the said data set.","PeriodicalId":308731,"journal":{"name":"2022 3rd International Conference on Innovations in Computer Science & Software Engineering (ICONICS)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130671194","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

Real-time Violence Detection using Deep Learning Techniques 使用深度学习技术的实时暴力检测

2022 3rd International Conference on Innovations in Computer Science & Software Engineering (ICONICS) Pub Date : 2022-12-14 DOI: 10.1109/ICONICS56716.2022.10100551

Gul e Fatima Kiani, Taheena Kayani

{"title":"Real-time Violence Detection using Deep Learning Techniques","authors":"Gul e Fatima Kiani, Taheena Kayani","doi":"10.1109/ICONICS56716.2022.10100551","DOIUrl":"https://doi.org/10.1109/ICONICS56716.2022.10100551","url":null,"abstract":"The subject of violence detection plays a significant role in tackling threats and abuses in society. It is the key element of any security enforcing system. The widespread deployment of video surveillance has facilitated the law enforcement agencies to visually monitor environments and take prompt action in case of any alerting situation. This task requires manual interaction for continuously overseeing the live streams of CCTVs. This paper presents an efficient approach for detecting violence in real-time using different deep learning methods which diminishes the element of human supervision to a higher extent. The existing research on the topic of violence detection using machine learning is either based on specially created videos or immensely relies upon less accurate algorithms and infeasible assumptions. The presented system in the paper is premised on a hybrid approach of employing different algorithms for assessing all distinct aspects of the problem in a viable and effective manner. The proposed system is reliant upon YOLO for real-time object detection and Long Short-Term Memory for developing the classification module. DeepSort algorithm in the proposed approach further augments the efficiency. The model was trained using a relevant violence detection dataset and integrated with different software frameworks for enhancing the interface. As an outcome of the paper, we developed a fully-fledged violence detection system based on deep learning algorithms which passed different tests and evaluations.","PeriodicalId":308731,"journal":{"name":"2022 3rd International Conference on Innovations in Computer Science & Software Engineering (ICONICS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116940313","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

Performance Enhancement in Agriculture Sector Based on Image Processing 基于图像处理的农业部门绩效提升

2022 3rd International Conference on Innovations in Computer Science & Software Engineering (ICONICS) Pub Date : 2022-12-14 DOI: 10.1109/ICONICS56716.2022.10100392

Ali Hassan, Faisal Rehman, M. Ashraf, A. Ashfaq, Hana Sharif, Rana Zeeshan, Salman Akram, Hira Akram

{"title":"Performance Enhancement in Agriculture Sector Based on Image Processing","authors":"Ali Hassan, Faisal Rehman, M. Ashraf, A. Ashfaq, Hana Sharif, Rana Zeeshan, Salman Akram, Hira Akram","doi":"10.1109/ICONICS56716.2022.10100392","DOIUrl":"https://doi.org/10.1109/ICONICS56716.2022.10100392","url":null,"abstract":"In the realm of agriculture and horticulture, machine vision and soft computing approaches have shown promise in overcoming the limitations of traditional methods for identifying plant illnesses utilizing various plant components. In all relevant studies such as fruit grading, leaf lesion area identification, and so on, image segmentation is the first and most important step. In order to diagnose the illness more effectively, a robust approach for numerous crops employing diverse plant components such as Fruit, Flower, and Leaf has been suggested in this study. Before segmentation, the acquired real-time pictures are pre-processed for illumination normalization and color space conversion. To enhance the segmentation outcomes, the conventional ML, image processing and deep learning approaches scheme has been made adaptable, and edge detection transformations have been implemented. To separate the sick regions from photos, the aim function of the Machine Learning approach has been adjusted, and cluster centers have also been upgraded. The many classes of plant illnesses noticed by shooting various types of images of diseases in plants, including many popular plants such as grapes, apple, and tomato. In terms of both broad human observation and computing time, the results achieved are superior.","PeriodicalId":308731,"journal":{"name":"2022 3rd International Conference on Innovations in Computer Science & Software Engineering (ICONICS)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117231644","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

A Systematic Review on the Motivations of Cyber-Criminals and Their Attacking Policies 网络犯罪动机及其攻击策略研究综述

2022 3rd International Conference on Innovations in Computer Science & Software Engineering (ICONICS) Pub Date : 2022-12-14 DOI: 10.1109/ICONICS56716.2022.10100569

Rana Mohtasham Aftab, Mariam Ijaz, Faisal Rehman, Ahmad Ashfaq, Hana Sharif, Naveed Riaz, Shabbir Hussain, Muhammad Arslan, Hadia Maqsood

{"title":"A Systematic Review on the Motivations of Cyber-Criminals and Their Attacking Policies","authors":"Rana Mohtasham Aftab, Mariam Ijaz, Faisal Rehman, Ahmad Ashfaq, Hana Sharif, Naveed Riaz, Shabbir Hussain, Muhammad Arslan, Hadia Maqsood","doi":"10.1109/ICONICS56716.2022.10100569","DOIUrl":"https://doi.org/10.1109/ICONICS56716.2022.10100569","url":null,"abstract":"Cyber-crime is a very hideous crime, which in this digital era of information technology has become a very critical, from one person’s problem to a whole nation’s problem. The very purpose of this review paper is to discuss about motivational values of cyber criminals, it was found that while they might have similarity, that they targeted grey areas of lives, but at the same time, with different cyber criminals there are different motivations behind their acts. In order to It also very important to have to have a better underestimating of physiology of the adversary, as it helps the law enforcers and others, that what might occur from what and what it may lead to. It also helps law enforcers to file a case against the adversary. It is understood that an illegal activity occurs from different people, on different scales, and on different targets, due to weak guardian ship and a strong motivated criminal, and with the mounting number of improvements in information security systems the attacks are increasing and becoming more and more common, and it is getting harder to pin-point the real culprit behind the curtains of vast internet.","PeriodicalId":308731,"journal":{"name":"2022 3rd International Conference on Innovations in Computer Science & Software Engineering (ICONICS)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116019143","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

Quantum Computing and Machine Learning Algorithms - A Review 量子计算和机器学习算法综述

2022 3rd International Conference on Innovations in Computer Science & Software Engineering (ICONICS) Pub Date : 2022-12-14 DOI: 10.1109/ICONICS56716.2022.10100452

Iram Manan, Faisal Rehman, Hana Sharif, Naveed Riaz, Muhammad Atif, Muhammad Aqeel

{"title":"Quantum Computing and Machine Learning Algorithms - A Review","authors":"Iram Manan, Faisal Rehman, Hana Sharif, Naveed Riaz, Muhammad Atif, Muhammad Aqeel","doi":"10.1109/ICONICS56716.2022.10100452","DOIUrl":"https://doi.org/10.1109/ICONICS56716.2022.10100452","url":null,"abstract":"The goal of machine learning (ML) is to develop models that automatically learn from the past without being explicitly programmed. ML has numerous applications, such as pattern recognition, forecasting upcoming trends, and judgement. It can handle massive amounts of huge vectors and tensors representing multidimensional data. To handle these processes on traditional computers, enormous time and computational resources are required. Unlike traditional computers, which use binary bits to compute, quantum computers (Q.C.) use qubits, which can simultaneously hold 0 and 1 combinations through superposition and entanglement. This makes Q.Cs an excellent choice for implementing ML algorithms because they are skilled at handling and post-processing large tensors. Although many of the models used for ML on quantum computers are based on concepts from their classical computing counterparts, the use of quantum computers has made them the better of the two. This paper assess the speed and complexity benefits of using quantum computers and gives a general overview of the state of knowledge regarding the use of ML on Q.C.","PeriodicalId":308731,"journal":{"name":"2022 3rd International Conference on Innovations in Computer Science & Software Engineering (ICONICS)","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132028948","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

Frameworks, Applications and Challenges in Streaming Big Data Analytics: A Review 流大数据分析的框架、应用和挑战综述

2022 3rd International Conference on Innovations in Computer Science & Software Engineering (ICONICS) Pub Date : 2022-12-14 DOI: 10.1109/ICONICS56716.2022.10100410

Syed Muhammad Nabeel Mustafa, Muhammad Umer Farooque, Muhammad Tahir, Shariq Mahmood Khan, Rohail Qamar

{"title":"Frameworks, Applications and Challenges in Streaming Big Data Analytics: A Review","authors":"Syed Muhammad Nabeel Mustafa, Muhammad Umer Farooque, Muhammad Tahir, Shariq Mahmood Khan, Rohail Qamar","doi":"10.1109/ICONICS56716.2022.10100410","DOIUrl":"https://doi.org/10.1109/ICONICS56716.2022.10100410","url":null,"abstract":"Data output has increased dramatically in the twenty-first century. The speed at which data is introduced into the stream has increased as a result of the digitization of practically all industries. Big data refers to this enormous amount of data. A complex network of data with volume, velocity, diversity, authenticity, and value makes up this information. To derive useful insights from the data, it became necessary to examine the data. The term \"streaming Big data\" refers to data that changes very quickly. Big data analytics that are live or streaming have significantly improved analytics. Instantaneous analytics of the real-time data are provided by streaming big data analytics, assisting the decision-makers. Big data analytics in real-time has taken center stage in business. We thoroughly examine the phenomenon of streaming Big data analytics in this review study. We also look at the various streaming analytics frameworks in use. Additionally, we look into the fields in which streaming Big Data analytics are applied as well as the difficulties encountered.","PeriodicalId":308731,"journal":{"name":"2022 3rd International Conference on Innovations in Computer Science & Software Engineering (ICONICS)","volume":"28 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127993890","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

Face-to-camera distance estimation using machine learning 使用机器学习估计与相机之间的距离

2022 3rd International Conference on Innovations in Computer Science & Software Engineering (ICONICS) Pub Date : 2022-12-14 DOI: 10.1109/ICONICS56716.2022.10100618

Syed Ausaf Hussain, Waseemullah, Najeed Ahmed Khan

{"title":"Face-to-camera distance estimation using machine learning","authors":"Syed Ausaf Hussain, Waseemullah, Najeed Ahmed Khan","doi":"10.1109/ICONICS56716.2022.10100618","DOIUrl":"https://doi.org/10.1109/ICONICS56716.2022.10100618","url":null,"abstract":"Distance estimation of moving objects from the camera with accuracy is a challenging task in the digital era where human interaction increases with smart systems and state-of-the-art applications. The wide-ranging applications of distance estimation include the “zooming” effect in a document reader and finding the power of the correction lens for eyesight (eyesight testing). A lot of research has already been done on different methods of distance estimation like the most widely-used methods of \"mono-vision\" and \"stereo-vision\". The purpose of this study is to introduce a novel approach to finding the distance between the face and the camera with a high degree of accuracy and speed. The proposed method is based on detecting, measuring, and calculating the size of irises on an image of a human face obtained from a single camera, and a Supervised Machine Learning algorithm. The open-source Mediapipe Python package has been employed to extract the irises from the images. The proposed method has given the results of the estimated distance with an average accuracy of 95.6%.","PeriodicalId":308731,"journal":{"name":"2022 3rd International Conference on Innovations in Computer Science & Software Engineering (ICONICS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134177533","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

A Quantitative Assessment of Emerging Trends in IoT Botnet Attacks 物联网僵尸网络攻击新趋势的定量评估

2022 3rd International Conference on Innovations in Computer Science & Software Engineering (ICONICS) Pub Date : 2022-12-14 DOI: 10.1109/ICONICS56716.2022.10100587

Muhammad Hassan Nasir, M. M. Khan, J. Arshad

{"title":"A Quantitative Assessment of Emerging Trends in IoT Botnet Attacks","authors":"Muhammad Hassan Nasir, M. M. Khan, J. Arshad","doi":"10.1109/ICONICS56716.2022.10100587","DOIUrl":"https://doi.org/10.1109/ICONICS56716.2022.10100587","url":null,"abstract":"In recent years, the world has witnessed a great increase in IoT Botnet attacks. Since these IoT devices are heterogeneous and typically resource-constrained with less processing and memory resources available to employ an efficient security mechanism. Moreover, the attack vectors and malicious adversaries have become advanced both in terms of complexity and diversity. This implies an effective mechanism to efficiently identify and prevent IoT Botnet attacks. This study aims to present a methodological quantitative assessment of the research conducted in the area of IoT Botnets from the year 2015 to 2022. Typically, the ISI Web of Science (WoS) along with VOSviewer (a software tool for constructing and visualizing bibliometric networks) are used to conduct the overall bibliometric assessment. Our assessment shows that the research in this area encompasses multiple domains with the USA leading in terms of research publication with English as the leading language. This bibliometric assessment provides an in-depth quantitative analysis based on the 9 research questions formulated during our research. This consequently helps the research community to collaborate with other researchers, institutions and funding agencies to make further progress in this area.","PeriodicalId":308731,"journal":{"name":"2022 3rd International Conference on Innovations in Computer Science & Software Engineering (ICONICS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131984582","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

Intrusion Detection using Deep Learning Techniques 使用深度学习技术的入侵检测

2022 3rd International Conference on Innovations in Computer Science & Software Engineering (ICONICS) Pub Date : 2022-12-14 DOI: 10.1109/ICONICS56716.2022.10100584

Syeda Fareeha Batool, Faisal Rehman, Hana Sharif, Maheen Jaffer, Anza Gul, Sameen Butt

引用次数: 0