Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing最新文献_第8页

Optimal and Dynamic Scheduling using Multiple Mobile Chargers in Rechargeable Sensor Networks: An MADM-based Approach 可充电传感器网络中多个移动充电器的最优动态调度:一种基于madm的方法

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549313

Riya Goyal, Abhinav Tomar

{"title":"Optimal and Dynamic Scheduling using Multiple Mobile Chargers in Rechargeable Sensor Networks: An MADM-based Approach","authors":"Riya Goyal, Abhinav Tomar","doi":"10.1145/3549206.3549313","DOIUrl":"https://doi.org/10.1145/3549206.3549313","url":null,"abstract":"In Wireless Rechargeable Sensor Networks (WRSN), wireless energy transfer procedures are provided to recharge sensor batteries using chargers. WRSN provide energy replacement and prolong network lifetime to the sensors. Most of the earlier work focussed on sensor node’s sensing task instead of charging utilities. So, an efficient partitioning approach is required to lower the cost of network deployment, to reduce the energy consumption rate and to increase the lifespan of network. Initially, to reduce the energy consumption rate, deadline and penalty values of nodes are computed. Then, considering the criticality of sensor nodes accurate number of multiple mobile chargers are deployed in the network in such a way that the network is partitioned into several number of subregions. Then, use of on-demand recharging and multi-node recharging, is the most efficient way of charging numerous sensor nodes at a time which results in the increases in the lifespan of network. Finally, using Multiple Attribute Decision Making (MADM) approach the scheduling of sensor node is achieved. The usefulness of our scheme is then demonstrated through comprehensive simulations. The results show that the proposed technique reduces charging latency by 23.52 percent, increases network lifetime by 72.35 percent and charging efficiency is increased by 80.90 percent.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127784093","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

An approach for classifying benign and malignant skin lesions using Optimized Deep Learning and SVM 基于优化深度学习和支持向量机的皮肤良恶性病变分类方法

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549281

Bagesh Kumar, Amritansh Mishra, Subham Raj, Aditya Kumar, Om Suhas Vibhandik, Aayush Talesara, Shubham Kumar, O. P. Vyas

引用次数: 2

Dynamic Target Monitoring of Load Balancers in Cloud Computing 云计算中负载均衡器的动态目标监控

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549228

Ayushi Verma, Tapas Badal, Indrajeet Gupta

引用次数: 0

CiAFP: Category-based Classification of iOS Apps by mining frequent permissions CiAFP:通过挖掘频繁的权限，基于类别的iOS应用分类

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549211

Neetu Sardana, Arpita Jadhav Bhatt

{"title":"CiAFP: Category-based Classification of iOS Apps by mining frequent permissions","authors":"Neetu Sardana, Arpita Jadhav Bhatt","doi":"10.1145/3549206.3549211","DOIUrl":"https://doi.org/10.1145/3549206.3549211","url":null,"abstract":"With the presence of 1.96 million apps, on the App Store, iOS is one of the most prevalently used mobile operating systems in terms of its users and after its counterpart Android. These apps have penetrated every aspect of our lives. They are used in payments, e-shopping, navigation, instant messaging and are also integrated with IoT devices to transmit data worldwide. These apps provide great convenience however they raise privacy and security concerns since a lot of users’ personal information can be accessed by the apps. Although Apple adopts a permission-based access control mechanism to confine apps from retrieving users’ personal information like address book, geo- coordinates, and photo gallery, the user still faces a significant menace of privacy leakage due to over-privileged permission, which means extra permissions affirmed by an app but then has nothing to do with its functionality. Unfortunately, the unavailability of tools to detect over-privilege permissions compels the user to grant all permissions declared by an app intensifies the risk of a privacy breach. Previous studies to detect over-privileged apps have been conducted for Android but minimal work has been done for iOS. To combat this problem, we have proposed a framework CiAFP that employs Association Rule Mining (ARM) technique to mine frequent permission patterns of iOS apps. This helps to determine over-privilege permissions. We have mined permission patterns for 331 iOS apps from 3 different categories namely Education, Finance, and Health & Fitness, and found that these categories have 40%,26%, and 24% over-privileged permissions. Additionally, the classification of iOS apps as malicious or benign is performed using frequent permissions. Our experimental results show that frequent permission-based classification gives better precision in comparison to the classification performed by total permissions.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125493272","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

Web Application Based Text Encryption 基于Web应用程序的文本加密

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549207

R. Kushwah, Rishabh Rajpurohit, Paul Jonathan, G. Kumar

{"title":"Web Application Based Text Encryption","authors":"R. Kushwah, Rishabh Rajpurohit, Paul Jonathan, G. Kumar","doi":"10.1145/3549206.3549207","DOIUrl":"https://doi.org/10.1145/3549206.3549207","url":null,"abstract":"In today’s world we are using cryptography everywhere. Cryptography ensures that the information is secured and can only be accessed by the sender and the receiver of the message. We use it to securely send passwords over vast networks for online purchases. During recent time, we have seen a rise in the number of cyberattacks. Our data is becoming less and less secure. Also, the encryption and decryption process has become really cumbersome which has made it really difficult for it to be used in daily life. The aim of our paper is to protect the privacy of users. We have proposed a novel RKJ algorithm which is based on keyless encryption. In this algorithm the message is converted to binary and divided into groups of 32 bits. Every group is used as its own key after round shifting the bits by a random amount. This is done for a number of times (rounds). Apart from the proposed algorithms, we have also provided the users to create an id and use our proposed web application to store important data in the form of notes. We have used the blowfish algorithm to store these passwords in our database. The proposed RKJ algorithm has been used to encrypt these notes. To read the notes we would have to use login and password. Finally, we have hosted proposed web application on Heroku.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124018998","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

Advanced malware and their impact on virtualization: A case study on hybrid feature extraction using deep memory introspection 高级恶意软件及其对虚拟化的影响:使用深度内存自省的混合特征提取的案例研究

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549223

Mohit Bhatt, Avantika Gaur, Saksham Badoni, P. Mishra

{"title":"Advanced malware and their impact on virtualization: A case study on hybrid feature extraction using deep memory introspection","authors":"Mohit Bhatt, Avantika Gaur, Saksham Badoni, P. Mishra","doi":"10.1145/3549206.3549223","DOIUrl":"https://doi.org/10.1145/3549206.3549223","url":null,"abstract":"As pandemic has hit the world, virtualization has become the hot topic of today’s era. Almost every organization has shifted to the virtual environment (specially cloud computing). However, the security concerns in virtualization are the central issue for the researchers as well as organizations. Attackers use different tactics to exploit the vulnerabilities present in virtual components. In this paper, we provided a detailed study on the malware families & along with their impact on virtualization. In addition, malware log extraction using deep memory introspection has been explored. Various plugins have been explained, along with the variety of features that are essential for malware analysis purposes. A case study has also been provided using the testbed set up in our lab to provide the practical insight on deep memory introspection using open source tools such as LibVMI, DRAKVUF, etc., along with their usage to extract different features outside the VM at the hypervisor. We hope that our work will help readers to understand the malware logs and extract important features for malware analysis in a virtual environment.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115158791","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

Investigation of Subword-Based Bilingual Automatic Speech Recognition for Indian Languages 基于子词的印度语双语自动语音识别研究

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549251

Aditya Yadavalli, Shelly Jain, Ganesh S Mirishkar, A. Vuppala

{"title":"Investigation of Subword-Based Bilingual Automatic Speech Recognition for Indian Languages","authors":"Aditya Yadavalli, Shelly Jain, Ganesh S Mirishkar, A. Vuppala","doi":"10.1145/3549206.3549251","DOIUrl":"https://doi.org/10.1145/3549206.3549251","url":null,"abstract":"Modelling Indian languages is difficult as the number of word forms they present is high. In such cases, prior work has proposed using subwords instead of words as the basic units to model the language. This provides the potential to cover more word forms. Additionally, previous work revealed that all Indian languages have several phonetic similarities. Consequently, multilingual acoustic models have been proposed to counter the data scarcity issue most Indian language datasets have. However, these models use monolingual language models (LM). They do not exploit the common basic tokens that certain Indian languages have. This motivated us to implement a bilingual subword-based Automatic Speech Recognition (ASR) system for Hindi and Marathi. Further, we try a combination of word-based monolingual LM with bilingual acoustic model to examine the reason for degradation in word-based multilingual ASRs. Our experiments show that multilingual subword-based ASR models outperform their word-based counterparts by upto 9.77% and 26.35% relative Character Error Rate (CER) in the case of Hindi and Marathi respectively.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132801460","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

Sarcasm Detection in News Headlines using Voted Classification 使用投票分类的新闻标题中的讽刺检测

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549245

S. Bharti, R. Gupta, Nikhlesh Pathik, Ashish Mishra

引用次数: 1

SVM and Logistic Regression for Facial Palsy Detection Utilizing Facial Landmark Features 基于人脸地标特征的支持向量机和逻辑回归面瘫检测

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549216

Anuja Arora, Anubhav Sinha, Kaushal Bhansali, Rachit Goel, Isha Sharma, Ambikesh Jayal

{"title":"SVM and Logistic Regression for Facial Palsy Detection Utilizing Facial Landmark Features","authors":"Anuja Arora, Anubhav Sinha, Kaushal Bhansali, Rachit Goel, Isha Sharma, Ambikesh Jayal","doi":"10.1145/3549206.3549216","DOIUrl":"https://doi.org/10.1145/3549206.3549216","url":null,"abstract":"Facial Palsy is a problem related to temporary or permanent damage of facial nerve. Conventional technique for facial paralysis is physical detection and manual measurement for reconstruction of facial features in order to provide perfect balance of patient’s face. These Conventional techniques need to be strengthen using computational process. The present research work is carried out in this same direction. Facial palsy data collection and in continuation landmark coordination generation are challenging task. Landmark coordination is an input for learning model. Two machine learning models – Support Vector Machine and Logistic Regression are applied and these machine learning models will train the system using generated facial landmark features. The two important tasks for handling the facial palsy detection using machine learning are Landmark feature generation and effective machine learning model training. The outcome for facial palsy detection using support vector machine is better than logistic regression. The average accuracy achieved by support vector machine is 76.87%","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121686310","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: 3

A Systematic Survey on COVID 19 Detection and Diagnosis by Utilizing Deep Learning Techniques and Modalities of Radiology 利用深度学习技术和放射学模式对COVID - 19检测和诊断的系统调查

Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing Pub Date : 2022-08-04 DOI: 10.1145/3549206.3549283

Shrishtee Agrawal, Abhishek Singh, A. Tiwari, Anushri Mishra, Abhinandan Tripathi

{"title":"A Systematic Survey on COVID 19 Detection and Diagnosis by Utilizing Deep Learning Techniques and Modalities of Radiology","authors":"Shrishtee Agrawal, Abhishek Singh, A. Tiwari, Anushri Mishra, Abhinandan Tripathi","doi":"10.1145/3549206.3549283","DOIUrl":"https://doi.org/10.1145/3549206.3549283","url":null,"abstract":"One of the most difficult aspects of the present COVID19 pandemic is early identification and diagnosis of COVID19, as well as exact segregation of non-COVID19 individuals at low cost and the sickness is in its early stages. Despite their widespread use in diagnostic centres, diagnostic approaches based solely on radiological imaging have flaws given the disease's novelty. As a result, to evaluate radiological pictures, healthcare practitioners and computer scientists frequently use machine learning and deep learning models. Based on a search strategy, from November 2019 to July 2020, researchers scanned the three different databases of Scopus, PubMed, and Web of Science for this study. Machine learning and deep learning are well-established artificial intelligence domains for data mining, analysis, and pattern recognition. Deep learning in which data is passed through many layers and automatically learning the composition of each layer from large dataset and it enables a new way that evaluates the complete image without human guidance to discern which insights are valuable, with applications ranging from object detection to medical image. Deep learning with CNN may have a significant effect on the automatic recognition and extraction of crucial features from X-ray and CT Scan images related to Covid19 analysis. According to the results, models based on deep learning possess amazing abilities to offer a precise and systematic system for detecting and diagnosing COVID19. In the field of COVID19 radiological imaging, deep learning software decreases false positive and false negative errors in the identification and diagnosis of the disease. It is providing a once-in-a-lifetime opportunity to provide patients with quick, inexpensive, and safe diagnostic services while also reducing the epidemic's impact on nursing and medical staff.","PeriodicalId":199675,"journal":{"name":"Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125045472","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