bluuebunny/crossref_metadata_2025 huggingface dataset:
Strategi Taboo for Taboo pada Penerjemahan Kata-Kata Tabu dalam Novel Where There’s Smoke
Authors: Ngaliyah Himmatul , Cholsy Hayatul
Published: January 2024

The purpose of this research is to analyze taboo for taboo strategy and factors that influence in translating taboo words in the novel Where There's Smoke into Indonesian. The data for this research are taboo words in the novel Where There's Smoke and its translation in Indonesian entitled Pencarian which uses the taboo for taboo strategy in its translation. The taboo for taboo strategy maintains taboo from the source language (SL) into the target language (TL). Data collection techniques are carried out by reading and taking notes. The data analysis in this research is based on Davoodi’s theory (2009). From the research results, it was found that the taboo for taboo strategy can translate taboo words at the same taboo level as SL and taboo for taboo strategy with a lower taboo level than SL. Factors that influence the taboo for taboo strategy are the use of the same function of taboo words between SL and TL, the message and meaning conveyed are the same, the difference language systems of SL and TL, the translator's understanding of the culture of SL, the readers of the target text, translator's sociolinguistic abilities, and editor’s role.
Materi Teks dalam Modul English for Beginners untuk Pembelajar Bahasa Asing di Konsultan Bahasa Denpasar
Authors: Dwi Rahayu Ni Kadek
Published: January 2024

This study aimed at examining the text forms and the text genres in the module English for Beginners which was used as a learning media at Language Consultant or English Foundation in Denpasar. This study used Qualitative research. The method of collecting data that is documentation and observation methods by note-taking technique. Then, the analysis which used was content analysis. The result of this study used informal method by words and sentences interpretatively. The result showed that consist of 77 (seventy-seven) text forms in the form of 53 (fifty-three) dialogue texts and 34 (thirty-four) monologue texts. Then the genre of text which appeared in this module is that 22 (twenty-two) descriptive genres and 14 (fourteen) narrative genres. The genre criteria appeared is that social function and linguistic reality which already appropriated with the criteria, however the generic structure had not fulfilled the structure criteria yet generally and tended to the structure were made in short paragraph so as the comprehension of the text were not incomplete
Hubungan Gaya Belajar Terhadap Keterampilan Menulis Esai Kelas XI SMAN 9 Padang
Authors: Junaidi Rissa Amelia Putri , Hartati Yulia Sri , Pebriani Yulia
Published: January 2024

The aim of this study is to examine the correlation between the learning preferences and essay writing abilities among 11th-grade students at SMA Negeri 9 Padang in class XI F7 for the learning style questionnaire trial and class XI F5 for the learning style questionnaire test and essay writing skill test using a random sample with a total of 32 students. This research employs a quantitative approach utilizing the correlation method to investigate potential associations between the two variables, namely learning style variables and essay writing skills variables. The results of this study are that in the data requirements test, the data normality test and homogeneity test are carried out. The normality test for the two data obtained a value of 0.427> 0.05 for learning style and 0.068> 0.05 for essay writing skills so that the two variables are normally distributed. Furthermore, for the data homogeneity test, a value of 0.218> 0.05 was obtained so that the data was homogeneous. Then, the result of this research hypothesis is 0.002< 0.05, it can be inferred that a connection exists between the learning style and essay writing skills of class XI students of SMA Negeri 9 Padang.
Pengaruh Model Pembelajaran Problem Based Learning Berbantuan Media Strip Story terhadap Kemampuan Menulis Teks Biografi Siswa Kelas X SMA Negeri 8 Medan
Authors: Waruwu Vivin Juliandra , Hanum Inayah
Published: January 2024

Research objectives describe the effect of the problem based learning model assisted by strip story media on the X-grade students of SMA Negeri 8 Medan. Using an experimental method with a two-group post-test-only design The research instruments include biography text and language rules. Data collection techniques included observation, interviews, and tests. The data analysis technique uses the analysis requirement test, normality test, homogeneity test, and hypothesis test. The research results of the post-test class of the control class obtained an average score of 64.71, which is included in the good enough category. The experimental class post-test research results obtained an average value of 82.43, including the good category. The problem based learning learning model assisted by strip story media has a significant effect on the ability to write biography text in class X SMA Negeri 8 Medan, as evidenced by the results of the hypothesis test, which obtained tcount> ttable, namely 9.79 > 1.69, then H0 is rejected and Ha is accepted.
Korean- English Code Mixing and Code Switching Of New Jeans’s Song
Authors: Margaretta Witness Eunike , Rangkuti Rahmadsyah
Published: January 2024

This paper reviewed code-mixing and code-switching along with the worldwide recognition of K-Pop songs by analyzing the English usage of "Hype Boy" and "OMG" by New Jeans, a fourth-generation K-Pop group. All songs were examined using Muysken's (2000) code-mixing theory, Stockwell's (2007) code-switching theory, and Fernandez-Martinez et al.'s (2014) and Kwon's (2019) comment analysis. The results revealed that the majority of code-mixing in "Hype Boy" was insertion and the majority of code-mixing in "OMG" was alternation, but there was no congruent lexicalization found in the two songs. However, the majority of code switches in "Hype Boy" were inter-sentential, whereas "OMG" was intra-sentential, and two tag-switching data were found. Aside from that, based on audience responses in the comment section, the audience responses showed up that they were impacted by the singers' word pronunciation errors, however, a lot were also impressed with their easy-to-listen-to songs. The study finds that the accurate pronunciation and the new English language switching function seem to have played a significant role in the success of "Hype Boy" and "OMG" among bilingual audiences.
K-Nearest Neighbor Classifier for Classifying user Reviews on Social Media Networks
Authors: Alotaibi Fahad , Alromema Nashwan Ahmed
Published: January 2024

Gigantic content generated on social media networking sites have made the online users enabled to communicate their opinions and sentiments about products and other entities like political events etc. Opinion mining applications aim to provide facilities to user and companies for know about products in which they are interested. In this work, opinion mining system for comparative reviews is developed using supervised machine learning approach. For this purpose, K-nearest neighbor classifier is trained on a publicly available dataset. Effectiveness of the system is validated by comparing its performance with other classifiers.
AI and Machine Learning-based practices in various domains: A Survey
Authors: Ghulam Ali , Sikander Rahu , Ali Farman
Published: January 2024

In several projects in computational biology (CB), bioinformatics, health informatics(HI), precision medicine(PM) and precision agriculture(PA) machine learning(ML) has become a primary resource. In this paper we studied the use of machine learning in the development of computational methods for top ﬁve research aeras. The last few years have seen an increased interest in Artiﬁcial Intelligence (AI), comprehensive ML and DL techniques for computational method development. Over the years, an enormous amount of research has been biomedical scientists still don’t have more knowledge to handle a biomedical projects eﬃciently and may, therefore, adopt wrong methods, which can lead to frequent errors or inﬂated tests. Healthcare has become a fruitful ground for artiﬁcial intelligence (AI) and machine learning due to the increase in the volume, diversity, and complexity of data (ML). Healthcare providers and life sciences businesses already use a variety of AI technologies. The review summarizes a traditional machine learning cycle, several machine learning algorithms, various techniques to data analysis, and effective use in ﬁve research areas. In this comprehensive review analysis, we proposed 10 ten rapid and accurate practices to use ML techniques in health informatics, bioinformatics, computational and systems biology, precision medicine and precision agriculture, avoid some common mistakes that we have observed several hundred times in several computational method works.
Identifying disease genes based on machine learning approaches for classiﬁcation
Authors: Sikander Rahu , Ghulam Ali , Rehman Mujeeb -ur-
Published: January 2024

In recent years, researchers have become increasingly interested in disease-gene association prediction. In the postgenomic era, this is one of the toughest jobs around. It is also challenging to determine biological research since complex disorders sometimes have very varied genotypes. Machine learning methods are used widely in the identiﬁcation of crawl marks, but their images depend heavily on their quantity and quality. In crawling studies, we ﬁnd that the recognition of genes reconciling diseases can be improved by an machines classiﬁer qualiﬁed in practical gene seamlessness from gene ontology (GO). In order to predict the genes of the disease, we’ve developed a supervised machine learning system. In the proposed pipeline, the use of autism spectrum disorder (ASD) is assessed. Similarity tests from various semantics have been used to quantitatively measure similarity in gene function. In this paper we suggest various techniques for classifying data from one-hot encoding method. This experiment is complicated by the fact that the into training and test sets. This is generally called an algorithm evaluation divided-train-test split method. ASD is a disease associated with high health care costs and early intervention will signiﬁcantly minimize these costs. ASD is a neurodevelopment disorder. Unfortunately, wait times are lengthy for an ASD diagnosis and treatments are not cheap. The economic effects of autism and an increase in ASD cases worldwide show an urgent need to establish methods of screening that are quickly enforced and eﬃcient. A timely and affordable ASD screening is therefore imminent to help health practitioners and to let individuals know whether they will be formally diagnosed clinically. Classiﬁers qualiﬁed and validated for ASD and non-ASD genes work better than ASD classiﬁers previously reported. For instance, in order to predict new ASD genes, the complementary forest classiﬁcation (CF) classiﬁcation reached AUC 0.80 above the reported classiﬁcation (0.73). Continuing, 73 novel ASD candidate bases can be predicted by the classiﬁer function. Such genes enrich the central ASD syndrome, such as autism and compulsion.
Pathway-disease Association Prediction Based on Graph Regularized Logistic Matrix Factorization (PDA-GRLMF)
Authors: Ghulam Ali
Published: January 2024

Complex alterations to the cellular machinery occur as a result of diseases. There are distinctive patterns associated with a disease in the gene expression proﬁle of the affected cells. As a result, these proﬁles can be used to extract additional biological information about an illness, which helps us better identify and evaluate disease risks. Human pathway-disease interaction research is a recurrent area of interest for the biomedical community. Finding the processes or connections between diseases and pathways can be aided by this association. This paper provides an overview of human pathway and human disease, with the accuracy of disease identiﬁcation has been less than satisfactory. In predicting disease-pathway interactions, this study suggests a computer model. In this research study we proposed the Graph Regularized Logistic MatrixFactorization (GRLMF) method for pathway-disease association prediction. A cutting-edge computational model called the PDA-GRLMF disease-pathway associationmodel can predict probable pathway-disease associations. The model can also assist pathologists in comprehending the relationships between diseasepathway linkages, therapies, and outcomes. In order to increase the associationbetween disease variation and new molecular correlations between genetic mutations, we carried out a pathway-based investigation. On the basis of shared gene interactions among pathways-disease, we created a biological network, and then we used network analysis to try and understand how a disease constructed the pathway-pathway network and then disease-disease network. To merge the gathered biological data, which was based on the pair similarity of sequence expression weights, we employed the heterogeneous network of pathway-disease relationships. The ROC (AUC) score achieved for the best prediction results was 0.8018%, and the precision-recall curve had two classes. These ﬁndings suggest that our strategy outperforms previously suggested methods in terms of scientiﬁc performance. By contrasting them with established connections and conducting a literature search, we projected relationships between pathogen, DD, and disease-pathway.
Design and Analysis of an Anthropomorphic Finger with Three Points Pneumatic Actuation
Authors: Bhand Imran Ali , Samo Saifullah , Memon Noor Fatima , Memon Saifullah , Nizamani Raheel Ahmed , Soomro Muhammad Ali
Published: January 2024

The human being’s hand is a set of fingers and can achieve any shape to hold or grip or grasp the object of different shapes. In the industrial sectors, various robot manipulators with different types of end-effectors (robot grippers) are used to perform tasks like gripping the object, picking & dropping the object, holding the tool, etc. The different types of end-effectors are required for grasping according to the geometry of the object. In this paper, an anthropomorphic finger is designed to mimic the human being’s finger to achieve any shape as the human’s finger and this finger can be a part of the robotic hand easily. This single type of robotic hand can perform all tasks for grasping any geometry of the object. The proposed finger consists of three pin joints with three pneumatic muscles, which help to achieve the desired shape with maximum holding/grasping power. The CAD model is developed to visualize and analyze finger motion. The comparison of the proposed finger and a real human finger is presented for validation of results.  The results show that the proposed system can mimic the human finger and shape modulation.
Application of Blockchain Technology in Energy Sector for Sustainable Future
Authors: Farhi Lubna , Laila Umme , Ahmed Afshaar , Ahmed Mishaal , Ahmed Manzar , Siddiqui Fahad Ahmad
Published: January 2024

In old energy control system, there was no freedom for users to sale or purchase energy with their own choice and rate because system was centralized and also there were many issues such as electricity theft cases and losses and burden of these losses directly inculdded in public bills. Now after the introduction of blockchain these issues has been resolved such as the blockchain technology give freedom to user to sale or purchase energy according to the own rate with secure transaction system. The conventional energy system with centralizedcontrol technique fails to resolve many issues such as freedom to user’s to sale or purchase energy in network in secure way and combination of blockchain and Microgrid has all feature to resolve all present isssues. In this paper, the blockchain technology features and application of blockchain with clean energy system will be investigate to provide cheap electricity to users by using Microgrid because Blockchain provide complete solutions for energy distribution and trading. In addition, somemajor present issues will be focus such as low eﬃciency, losses and ant algorithm model to distribute the energy in the network eﬃciently.
Pneumonia Classification from Chest X-ray Images Using Pre-Trained Network Architectures
Authors: Shahzad Aqsa , Arshed Muhammad Asad , Liaquat Farrukh , Tanveer Muhammad , Hussain Mahmood , Alamdar Rabbia
Published: January 2024

Pneumonia is a serious disease caused by a lung infection that affects young and old people and approximately cause of 4 million deaths each year. Patients that are facing disorders such as weak immune systems, asthma, and babies all are at risk specifically if pneumonia is not detected at an early stage. An early diagnosis of pneumonia is required to plan a potential treatment strategy to control and treat the condition. The objective of this study is to analyze chest radiograph images to identify lung abnormalities using pretrained architecture. After extracting features from the images using convolutional neural network models that have been pre-trained on a large dataset called ImageNet, they are typically passed through a classifier for further processing and diagnosis. Pre-trained networks variants including VGG16, VGG19, DenseNet121, ResNet50, and InceptionV3 architecture were utilized in this study & results show that VGG-16 architecture performance is effective with a test accuracy of 90% and validation accuracy of 93.98% than other pretrained architectures.
Formal Verification of Twin Clutch Gear Control
Authors: Zaman Muhammad , Atif Muhammad , Naseer Mudassar
Published: January 2024

Twin clutch model enables the power-shifts as conventional planetary automatic transmission and eradicates the disadvantages of single clutch transmission. The automatic control of the dual clutches is a synchronization problem. Particularly, to control the clutching component that engages torque when running in one direction of revolution and disengages when running in the other direction, which exchange the torque smoothly during torque phase of the gear-shifts on planetary-type automatic transmissions, seemed for quite a while hard to compensate through clutch control. Another problem is to skip gears during multiple gearshifts. However, the twin clutch gear control described in ["M Goetz, M C Levesley and D A Crolla. Dynamics and control of gearshifts on twin clutch transmissions, Proceedings of the Institution of Mechanical Engineers, Journal of Automobile Engineering 2005"], a significant improvement in twin clutch gear control system is discussed. We formally specify the algorithm for the twin clutch gear control system and verify it using the model-checking. Formal methods have a high potential to measure correctness of communicating protocols. We use UPPAAL for formal specification and verification. Our results show that the twin clutch gear control model partially fulfills its functional requirements
Performance Prediction for Undergraduate Degree Programs Using Machine Learning Techniques - A Preliminary Review
Authors: Nisa Waqar Un , Naseer Mudasser , Atif Muhammad , Akhtar Salwa Muhammad , Nisa Mehr Un
Published: January 2024

Academic Performance prediction for undergraduate students is considered as one of the hot research areas since last couple of decades. An accurate and timely prediction of the student’s performance can directly influence the three participants; learner, instructor and the institution. This study presents a brief, preliminary review to explore existing literature from 2010 to 2022 in the context of performance prediction for Undergraduate Degree Programs (UDP). This review is organized according to Online and Traditional Education Systems (TES), and granularity level of performance output i.e., Degree program (Final CGPA), Next-semester, and the Course level grades. Aggregate analysis of the extracted data reveals that course level prediction is highly worked area deploying classification and regression techniques using data from academic domain. Existing empirical studies are mostly evaluated using accuracy, precision, recall and F1-measure and are validated with 10-fold cross validation. Contribution of this study is the novel categorical distribution of studies with respect to education system and granularity levels. Another important finding was the Success ratio of different Machine learning (ML) techniques used for these prediction studies. It is concluded that further research is required for TES to discover interdependent group of courses and Course Clusters for a certain degree program and then to develop prediction models for those course clusters.
Business Growth Prediction Using Social Media Data Analytics
Authors: Bux Hadi , Ali Babar , Alvi Fizza Abbas , Memon Irfana , Rajput Muhammad Awais
Published: January 2024

Marketing has become an important aspect of the growth of any business. Without good strategies for sharing content across social media platforms, no business can ﬂourish and will lose its customers. Many social media platforms provide various analytics tools for free. These tools provide analytics of content engagement. Many proprietary tools provide such features with enhanced capabilities. These tools require shared content of users for analysis. These tools do not exactly state the content being shared. Businesses need marketing teams for creating attractive content. However, a lot of money is needed for hiring skilled personnel. This research proposed an eﬃcient platform that provides analysis of content shared by successful businesses on social media platforms. Firstly, it’s free and public shared data, no surveys to be done because the analysis is done on businesses who are experts in marketing, and secondly, there is no headache for searching trends because the analysis is done on real-time data.
Questgator: A Platform for Content Aggregation and Text Classification
Authors: Shaikh Abdul Latif , Alvi Fizza Abbas , Ali Babar , Rajput Ubaidullah , Bux Hadi
Published: January 2024

The Web has witnessed a surge in content over recent years. Content is revolutionizing the way people conduct business, communicate, and make informed decisions. However, the vast amount of data used for communication today is oftenunstructured and challenging to comprehend. Content aggregators provide a solution to this problem by collecting data from various sources and organizing it into a structured format in one place. This research proposed the content aggregator "Questgator" that extracts content for example news, scholarships, jobs, books, video content, and research papers. In this paper Naive Bayes theorem is used for text classiﬁcation. Moreover, paper also provides comparison with other platforms to show the eﬃciency of proposed content aggregator.
XGboost-Ampy: Identification of AMPylation Protein Function Prediction Using Machine Learning
Authors: Khan Swati Zar Nawab , Ghulam Ali , Sohail Muhammad , Arshed Jawad Usman , Sikander Rahu , Malik Muhammad Shahid , Khan Nauman
Published: January 2024

A developing post-translational modification known as AMPylation involves the formation of a phosphodiester bond on the hydroxyl group of threonine, serine, or tyrosine. Adenosine monophosphate is covalently attached to the side chain of an amino acid in a peptide during this process, which is catalyzed by AMPylation. We used AMPylation peptide sequence data from bacteria, eukaryotes, and archaea to train the models. Then, we compared the results of several feature extraction methods and their combinations in addition to classification algorithms to obtain more accurate prediction models. To prevent additional loss of sequence information, the PseAAC feature is employed to construct a fixed-size descriptor value in vector space. The basic feature set is received from 2nd features extraction method. All of this was accomplished by deriving the protein characteristics from the evolutionary data and sequence of the BLOUSM62 amino acid residue. The eXtreme Gradient Boosting (XGBoost) technique was used to create a novel model for the current study, which was then compared to the most popular machine learning models. In this research, we proposed framework for AMPylation identification that makes use of the XGBoost algorithm (AMPylation) and sequence-derived functions. XGBoost -Ampy has an accuracy of 86.7%, a sensitivity of 76.1%, a specificity of 97.5%, and a Matthews’s correlation coefficient (MCC) of 0.753 for predicting AMylation sites. XGBoost -Amp, the first machine learning model developed, has shown promise and may be able to help with this problem.
Formal Specification and Verification of Distributed Denial of Service (DDoS)
Authors: Hafeez Sara , Atif Muhammad , Naseer Mudasser
Published: January 2024

DDoS (Distributed Denial of Service) attack is the main cause for interrupting the requests of users. DDoS uses more than one IP addresses. It makes botnets (machines that are affected with malware), through which interruption of a service begins and the requests are either denied or delayed to the legitimate users. A protocol is introduced in [DDoS Attack Detection Method Based on Network Abnormal Behavior in big data environment, CoRR, vol. abs/1903.11844, 2019] for the detection of DDoS in which a threshold is defined for detection of illegitimate users. We exhibited simulating of the protocol using a model checker UPPAAL and formally verified the functional requirements of the protocol to determine the system’s accuracy.
The Signiﬁcance of IoT: A Healthcare Systems Perspective
Authors: Rasheed Mansoor Ahmad , Murtaza Hudabia , Awan Hamza Shahab , Ikram Shahzaib , Rasheed Mannan Ahmad , Rasheed Mehnaz
Published: January 2024

In the current era of modern technologies, the health of the patient demands real time monitoring system. This dynamic system can be developed by using efficient sensors, network and internet cloud either wire or wireless. For example, for heart patient blood pressure and pulse must be measure constantly, in case if the patient is in moving and changing his position. For this purpose, an efficient system is required. In future there will be many other problems such as viruses attach detection, dingy fever detection, and sugar problems. For all these problems there will be multiple parameters of patient must me monitor and control. In this paper a method will be device to monitor all these parameters in real time. Moreover, we are concentrating on using mobile agents to provide patient assistance and healthcare services in order to help with the diagnosis of patient’s illnesses Furthermore, platform-agnostic solutions for healthcare data collection and dissemination over NoSQL are being studied. The Apache Jena Fuseki NoSQL database with the JAVA Example Application Framework -JADE client platform was used in testing environment. The consequences show that No Structure Query Language version beats the rel-database implementation.
Research in Collaborative Tagging Applications: Choosing the Right Dataset
Authors: Jabeen Dr. Fouzia , Khusro Shah , Anjum Nasreen
Published: January 2024

Collaborative tagging is an interesting approach that provides the flexibility to add description(s) to a resource according to the user’s own perception about that resource. These applications are the hottest in the areas of Social Bookmarking, Media Content Sharing and E-Commerce. Being favorite among users, these applications accumulate users’ interactions in the form of embedded datasets very quickly. These datasets are very important for further improving these applications and subsequently facilitating the user in better performing his/her activities. We feel there is a need to study these datasets to help researchers test their proposed algorithms on the right dataset and make valuable assessment and informed decisions. In this paper, we have identified measures for evaluating collaborative tagging applications’ datasets suitability for research experiments. The appropriateness of the identified measures is tested through experiments. Based on the results, recommendations are made on the suitability of the available datasets and how future dataset should look like. Researchers working not only in tagging but also in other disciplines can utilize these datasets to test their proposed algorithms without developing their own. This article provides measures which we dig out by reviewing existing available datasets. These measures are significant in selection of suitable and appropriate dataset(s), as selection of inappropriate dataset leads to errors in the results researchers are expecting. This work will prove extremely relevant and beneficial to all researchers who wish to use datasets of collaborative tagging applications for their research experiments.
Identification of an Optimized Google PageSpeed Audit-Rule-Sequence to Optimize Page Speed
Authors: Ghaffar Abdul , Abid Fazeel , Ashraf Mohsin , Jamil Abdul , Abbas Ali , Malik Farah Rauf
Published: January 2024

World Wide Web is a collection of online resources and websites including e-commerce, social sites, educational content, etc. To find relevant online resources, people search these by using search engines by providing their desired keywords. After filtering those keywords, search engine list the most relevant websites which are more optimized and efficient in terms of loading speed. Search engine optimization is a set of techniques used to make a website optimized and relevant to those keywords, and set the rank of a website. An online resource or website will be on the top of the search result set if it has a higher rank in search engines. Page speed is one of the most important on-page search engine optimization techniques that is used to make web site efficient in load time, so the user will get the content of the websites in a minimum time. Google has set page speed as the main factor in a higher ranking in search engines. Getting higher page speed is not an easy task, as several performance matrices must be optimized to get efficient loading time. There are many audit rules which are irrelevant or have less impact on the performance score. So selection of audit rules to be optimized is one of the main decisions before starting page speed optimization work. It will waste of time to investigate audit rules for their impact on performance scores. In this paper, we have analyzed all of the audit rules and identified the most important and relevant audit rules in optimizing page speed. A tool is used to generate the best sequence of relevant audit rules based on weighted performance benefit scores in the execution of each audit rule. The same audit rule sequence is applied on five different websites and is found more than 80% improvement in performance scores by applying the first three audit rules only and above 90% performance score obtained by using the first five to seven audit rules in our proposed audit rules sequence.
A Review of Cellphone Healthcare Applications: Content Privacy and Safety Issues, Challenges and Recommendations
Authors: Murtaza Hudabia , Khan Muhammad Zubair , Rasheed Mansoor Ahmad , Ikram Shahzaib , Rasheed Mannan Ahmad , Rasheed Mehnaz
Published: January 2024

Nowadays, cellphone healthcare applications are receiving a considerable amount of attentiveness from both researchers and developers.  Advances in communication technologies have aided the evolution in the usage of cellphones and digital devices and present challenges in terms of the accuracy and reliability of cellphone healthcare applications. Nevertheless, these applications may compromise crucial threats related to the seclusion and safety of users’ health content. Awareness of these challenges can help cellphone and cellphone application producers to manufacture efficient tools to allow patients and users to access the services of these technologies very effectively. The main objective of this research paper is to recognize the potential limitations and strengths regarding content seclusion and safety for the development and widespread utilization of effective cellphone healthcare applications. We have employed a literature survey and a relative comparison of the top ten top-rated cellphone healthcare applications to recognize security threats and characteristics that can support developers in building healthcare applications with necessary seclusion and safety standards and allow the users to select the appropriate application for their personal use.
Comparative Analysis of Privacy Preserving Location Based Services Mechanisms
Authors: Hussain Muzamil , Alvi Fizza Abbas , Rajput Ubaidullah
Published: January 2024

Recent trends in computing have enabled the provision of location-based services, offering practicality and convenience to users. Moreover, this has also given rise to new challenges and vulnerabilities that can potentially compromise user privacy. As these services are predominantly used on handheld devices, the risk of security breaches is higher. This research collates existing studies that have conducted quantitative and qualitative comparisons and analyses on how to address related challenges, with a particular focus on protecting user privacy in location-based services.
Detection of Crackle and Wheeze in Lung Sound using Machine Learning Technique for Clinical Decision Support System
Authors: Ali Syed Waqad , Asif Muhammad , Rashid Munaf , Tanvir Sania , Shams Sarmad , Abid Sidra
Published: January 2024

This study aims to develop a computer-based clinical decision support system that will help clinicians and healthcare personnel to make an early and correct decision to prevent the patient from nontransmissible respiratory diseases. The main contribution of this study is to analyze, investigate, and extraction of the useful feature of pathological respiration and Classification of Crackle and Wheeze from recorded lungs sound by using machine learning techniques. In the particular spectrogram, Time-frequency and Mel-Frequency cepstral coefficient (MFCC)technique is applied for feature analysis and data conversion into a format that can be useful for feature extraction and training models. PCA dimensional reduction technique is used to reduce the dimensionality of the extracted feature. In order to apply various machine learning techniques a widely used dataset freely available dataset ICBHI-2017 is used. The respiratory lungs sound is comprised of 126 patients with 920 Chest sound annotations that include adventitious sounds such as “Crackle” and “Wheeze”. Machine learning algorithms such as MusicANN, VGGish, and OpenL3 were applied for testing the better accuracy of the classification model. The accuracy of the utilized classifier with the extracted feature set is determined as 72%, 81%, and 69% respectively.
A Survey on Blockchain-based Intrusion Detection Systems for IoT
Authors: Hassan Jawad , Abid Muhammad Kamran , Ahmad Mughees , Ghulam Ali , Fakhar Muhammad Salman , Asif Muhammad
Published: January 2024

The Internet of Things (IoT) is a contemporary concept that unifies the Internet and physical objects across various domains, such as home automation, manufacturing, healthcare, and environmental monitoring. This integration enables users to leverage Internet-connected devices in their daily routines. Despite its numerous advantages, IoT also presents several security challenges. As the popularity of IoT continues to grow, ensuring the security of IoT networks has become a critical concern. While encryption and authentication can enhance the security of IoT networks, protecting IoT devices against cyber-attacks remains a complex task. A successful cyber-attack on an IoT system may not only result in information loss but also potentially cripple the entire system. Intrusion detection systems (IDS) are instrumental in identifying malicious activities that could compromise or disrupt network performance. Consequently, there is a pressing need for effective IDS solutions to safeguard IoT systems. Blockchain, an emerging technology, bolsters security systems to counter modern threats. In this paper, we provide an extensive review of state-of-the-art blockchain-based intrusion detection systems for IoT applications. Additionally, we present recent advancements in addressing security concerns in a tabular format. Lastly, we identify open challenges and current limitations that warrant further exploration.
DeepImmuno-PSSM: Identification of Immunoglobulin based on Deep learning and PSSM-Profiles
Authors: Ghulam Ali , Nawab Khan Swati Zar , Ali Farman , Tunio Saima , Jabeen Nida , Iqbal Natasha
Published: January 2024

Immunoglobulin has a close connection to a number of disorders and is important in both biological and medicinal contexts. Therefore, it is crucial for illness research to employ efficient techniques to increase the categorization accuracy of immunoglobulins. Computational models have been used in a small number of research to address this important issue, but the accuracy of the predictions is not good enough. As a result, we use a cutting-edge deep learning technique with convolutional neural networks to enhance the performance results. In this study, the immunoglobulin features were extracted using the dipeptide acid composition (DPC) with the position-specific scoring matrix (DPC-PSSM) and position-specific scoring matrix-transition probability composition (PSSM-TPC) methods. we apply extracted features information from the DPC-PSSM profiles and PSSM-TPC profile by using a 1D-convolutional neural network (CNN) over an input shape.  The outcomes demonstrated that the DeepImmuno-PSSM method based on sequential minimal optimization was able to properly predict DPC-PSSM accuracy score 93.44% obtained and of the immunoglobulins using the greatest feature subcategory produced by the PSSM-TPC feature mining approach accuracy score 89.92% obtained. Our findings indicate that we are able to provide a useful model for enhancing immunoglobulin proteins' capacity for prediction. Additionally, it implies that employing sequence data in deep learning and PSSM-based features may open up new path for biochemical modelling.
Statistical Methods in Computer Science: A Comparative Review of Inductive and Deductive Approaches
Authors: Niaz Sidra , Jabeen Dr. Fouzia , Salam Dr.Tabinda , Nazir Dr. Shah
Published: January 2024

Combination issues lie at the core of computer science and modern discrete mathematics. Artificial intelligence lays the foundation for induction and deduction techniques. Induction in computer science involves establishing statements based on specific to general observations, while deduction operates in the opposite direction. In this paper, we analyze induction and deduction using three criteria: the difference in space and time complexity, inconsistencies within induction and deduction, and their application areas within the computer science domain. Induction enhances the power of observation and analysis, whereas deduction plays a crucial role in the logical realm of computer science. Deduction is primarily employed for quantitative analysis, including statistical analysis.
A Blockchain-Enabled Machine Learning Mask Detection method for Prevention of Pandemic Diseases
Authors: Sathio Anwar Ali , Awan Shafiq Ahmed , Panhwar Ali Orangzeb , Aamir Ali Muhammad , Brohi Ariz Muhammad , Burdi Asadullah
Published: January 2024

During the COVID-19 pandemic, finding effective methods to prevent the spread of infectious diseases has become critical. One important measure for reducing the transmission of airborne viruses is wearing face masks but enforcing mask-wearing regulations can be difficult in many settings. Real-time and accurate monitoring of mask usage is needed to address this challenge. To do so, we propose a method for mask detection using a convolutional neural network (CNN) and blockchain technology. Our system involves training a CNN model on a dataset of images of people with and without masks and then deploying it on IoT-enabled devices for real-time monitoring. The use of blockchain technology ensures the security and privacy of the data and enables the efficient sharing of resources among network participants. Our proposed system achieved 99% accuracy through CNN training and was transformed into a blockchain-enabled network mechanism with QR validation of every node for authentication. This approach has the potential to be an effective tool for promoting compliance with mask-wearing regulations and reducing the risk of infection. We present a framework for implementing this technique and discuss its potential benefits and challenges
Humanoid Robots: Cybersecurity Concerns And Firewall Implementation
Authors: Munir Safa , Khan Kashaf , Aslam Dr Naeem , Abid Kamran , Rehman Mustajib-ur-
Published: January 2024

Technology has grown more important in our lives, and scientists are developing new products to make people’s life easier and more pleasant. One of these innovations is the humanoid robot. The use of humanoid robots in our daily lives is expanding at an unprecedented rate as robots are being used in different aspects of life. The market is becoming more automated and optimized, Robotics serves as one of the primary instruments used for these reasons. Yet, security continues to pose a concern for robotics. As humanoid robots begin to function "in the open," we must assess the threats they will confront. Through the literature review, researchers found that security assessments were not performed on the robots which cause the robots to be weak against cybersecurity attacks. In this research, we perform different security assessments to identify the vulnerabilities in humanoid robots. Furthermore, different metrics were used to check and perform security assessments on the robot as well as the results of security assessments has been shown. It was shown that humanoid robots are vulnerable as anyone will be able to hack the login credentials of robot’s website as well as there are some open ports in the robot’s network which can be used by the hackers to exploit robot’s working. Based on the results of assessment methods and our findings, we gave the firewall framework which will be helpful to protect the humanoid robot against those security vulnerabilities and attacks.This firewall framework will be able to protect the humanoid robots in aspects of both network and website/webpage exploitation.
A Study Towards Exploring Access Control Mechanisms and its Limitations in Cloud Computing
Authors: Marina Baby , Memon Irfana , Alvi Fizza Abbas , Nabi Mairaj , Rajper Adnan Manzor , Rajput Ubaidullah
Published: January 2024

Cloud computing technologies are growing fast day by day. Cloud technologies are attracting enterprises to themselves by providing great and enhanced services. There is no doubt that cloud technologies reduced the burden of the digital world by giving manageable computing services, huge room for unlimited data storage, on-demand software services, great platforms, and access control management systems. To use cloud-based manageable services users and organizations must have access to the cloud. Before using any access control mechanism, the organizations should know about the limitations of the access control mechanism. At present, many access control mechanisms are available in cloud computing. In this paper, our main goal is to identify the access control mechanisms in cloud computing and their limitations in cloud computing.
Evaluation of Safe Landing Site Detection Methods for Unmanned Aerial Vehicles
Authors: Ghous Hamid , Malik Mubasher H. , Majeed Dania , Mohamed Fathima Nuzha , Nasir Ayesha
Published: January 2024

Nowadays, aerial vehicles (drones) are becoming more popular. Over the past few years, Unmanned Aerial Vehicles (UAVs) have been used in various remote sensing applications. Every aerial vehicle is now either partially or completely automated. The tiniest type of aerial vehicle is the UAV. The widespread use of aerial drones requires numerous safe landing site detection techniques. The paper aims to review literature on techniques for automatic safe landing of aerial drone vehicles by detecting suitable landing sites, considering factors such as ground surfaces and using image processing methods. A drone must determine whether the landing zones are safe for automatic landing. Onboard visual sensors provide potential information on outdoor and indoor ground surfaces through signals or images. The optimal landing locations are then determined from the input data using various image processing and safe landing area detection (SLAD) methods. UAVs are acquisition systems that are quick, efficient, and adaptable. We discuss existing safe landing detection approaches and their achievements. Furthermore, we focus on possible areas for improvement, strength, and future approaches for safe landing site detection. The research addresses the increasing need for safe landing site detection techniques in the widespread use of aerial drones, allowing for automated and secure landing operations.
The Impact of COVID-19 on E-Learning: Context-Based Sentiment Analysis Discourse Using Text Mining
Authors: Rehman Aqsa , Aslam Naeem , Abid Kamran , Fuzail Muhammad , Rehman Asif-Ur-
Published: January 2024

Finding the most dominant and pertinent user opinions on a certain topic is crucial to the sentiment analysis success factor. During the pandemic lockdowns around the world, the suspension of academic institutions leads to an exceptional increase in distance education. Academic institutions closed their campuses immediately to mitigate the effects of COVID-19 and prevent its pervasive spread, and educational activities were shifted to online platforms. The effectiveness of online education is a significant topic of interest for both students and their parents, especially in terms of how students and teachers perceive it and how technologically viable it is in a range of social circumstances. Before such a wide adoption of e-learning is possible, these issues must be analyzed from multiple perspectives. The present research aims to evaluate the efficacy of e-learning by examining individuals' perceptions of it. Opinions can be found on websites such as Instagram, Facebook, Twitter, etc. As social media has recently emerged as a significant means of communication. This study addresses factors connected to a significant change in the educational system. 200,000 tweets were gathered from Twitter to evaluate the opinions of Twitter users who were taking part in online learning. This study adopts VADER to analyze the subjectivity and polarity score of tweets, a topic model was also created using the LDA algorithm to determine the themes that were talked about on Twitter the most. The models have been constructed and evaluated using Word2Vec to capture the semantic relationships between words and LSTM and RNN sequential model for sentiment analysis. This study measured the efficiency of a sentiment analysis model using the accuracy metric, the conducted experiments reveal that the proposed hybrid model achieves an overall accuracy of 96.3%. The results also indicate a significant negative impact of the Covid-19 pandemic on individuals' emotions, with 64.4% of the analyzed tweets displaying negative sentiments. These findings provide valuable insights into the relationship between global events and individual emotions on social media platforms.