bluuebunny/crossref_metadata_2025_split huggingface dataset:
Structures of Digraphs Arizing from Lambert Type Maps
Authors: Sabahat Tayyiba , Asif Sufyan , Rehman Asif Abd ur 
Published: January 2024

The Well-known function W eW is called Lambert Map. This map has been viewed by many researchers for finding the approximate solutions of exponential function especially in numerical analysis. Later, it has been incorporated in number theory for finding integral solutions of exponential congruences under a fixed modulus. Instead of WeW, we use the function W2 e W and call this function as Discrete Lambert Type Function (DLTF). In this work, we produce graphs using DLTF, and discuss their structures. We show that the digraphs over DLTF satisfy many structures as these have been followed for using WeW. It would be of great interest, if these results could be generalized for WeWfor all integers n, in the future.
On Fixed Points of Digraphs Over Lambert Type Map
Authors: Sabahat Tayyiba , Asif Sufyan , Rehman Asif Abd ur 
Published: January 2024

Define f(y)=y2hy where h belongs to (Z/mZ), the Discrete Lambert Type Map (DLTM). For a set of vertices and edges over DLTM, diagraphs are obtained in which the vertices are from a whole range of residues modulo a fixed integer s, and edges are obtained when f (y) ≡ v(modsK) is solvable in Y and in terms of Diophantine equation as well. In this paper we proposed new results for for fixed point of digraphs over DLTM.
Energy Trading and Control in Microgrid Network by using Blockchain Technology
Authors: Ahmed Manzar , Khan Abid Mohummad , Ahmed Mishaal , Javeed Muhammad , Farhi Lubna , Huque Ibrar Ul , Yousaf Adnan , Rasheed Mansoor Ahmad , Rasheed Mannan Ahmed 
Published: January 2024

In future all Energy resources will be control through the internet (IoT) both home and commercial systems. For home power system, user can installed solar or wind energy system from 1 KW to 10 MW, depend on the area and location. These Energy resources will be control by Microgrid, the microgrid can work in two mode such as stand-alone system or with grid-connected mode. To control losses and trading the energy in microgrid network. The blockchain technology is suitable for microgrid because it has potential to resolve all issues such as energy theft case or billing fraud. The blockchain is distributed ledger technology and it has the potential to improve efficiencies by updating the status of the system. Microgrid combination with blockchain provide complete solution to all previous issues, which were present in conational energy system. In this research work, the Microgrid model with blockchain technology will be analyze for the energy sector to address the issues of the present problems, such as low efficiency, market competition, improve contract system for selling energy and theft case on basis of trusted and secure transaction platform without third party.
Fagan Inspection: A Defects Finding Mechanism in Software Requirements Specification (SRS) Document
Authors: Yousaf Muhammad Sohaib , Ali Saqib , Nawaz Qamar , Afsar Salman , Mumtaz Imran , Rashid Nida 
Published: January 2024

The preparation of Software Requirement Specification (SRS) document is a critical task as the successful completion of software depends heavely upon the SRS. The requirements gathering phase in Software Development Life Cycle SDLC is equally important as it witnesses the creation of an SRS document. The quality of an SRS document is dubious as there is a scarcity of proficient inspection methods for detecting the defects in the software. There are various reading based inspection techniques such as Checklist Based Reading technique (CBR), Defect Based Reading technique (DBR) and Perspective Based Reading technique (PBR). There are certain problems with these techniques such as the CBR only covers potential types of defects, The DBR focused on various defects but the problem is that it is not structured in nature. Likewise, PBR is not suitable as it mainly focused on use-cases. As owing to the absence of a suitable inspection technique, the main focus of this study is to highlight the Fagan based inspection technique. This technique is not only formal and lightweight but it also overcome the limitations of reading base d techniques. The Fagan is a structured approach and finds defects in specification, programming code, design and also others during the SDLC. During analysis a questionnaire based survey is conducted in almost 300 national and international software industries. As a result, 150 responses are received. The likert scale is used for developing the questionnaire. SPSS tool is also utilized for data entry and analysis. The consequences are drawn by developing hypothesis and using the Chi-square test. More satisfactory results are gained at international level than at the national level. The satisfaction level of defect detection is 59.2%, time saved is 48.3% and cost reduction is 40.0%. Thus, the obtained outcome is positive and satisfies the requirements.
Machine Learning Empowered Efficient Intrusion Detection Framework
Authors: Shafique Hassan , Shah Asghar Ali , Qureshi Muhammad Aasim , Ehsan Muhammad Khurram , Amirzada Muhammad Rizwan 
Published: January 2024

In modern era security is becoming major and basic need of any system. Protecting of a system from unauthorized access is very important for a network system. Network security is turning out to be an influential subject in information technology territory. Hackers and squatters commit uncountable successful attempts to intrude into networks. Intrusion Detection System plays a vital role in a network security to identify and detect the anomalies in a security system of network. The performance of IDS can be measured through its intelligence, efficiency and accurate detection of unknown and known attacks. The greater the gain concept give the best possible detection rate of anomalies. This study proposed a machine learning framework based on MLP classifier with accuracy 99.98%. This work is further validated through 10-fold and JackKnife cross validation. Key metrics to see the impact on accuracy and other performance measured metrics such as Sensitivity, Specificity and Matthew’s Correlation Coefficient. All the metrics gained their highest ratio, which means MLP is the best classification technique. The accuracy, sensitivity, specificity and MCC rate of the suggested model computed 99.99% from whole dataset of UNSW-NB15. These results show the improvement in accuracy while applying different perceptron topologies. K-fold and JackKnife topologies are capable to earn the 99.99% accuracy
Enhanced polarization tuning in hybrid-active-U-shaped nanostructure with dependence of 2D graphene layer using COMSOL Software
Authors: Uddin Noor , Khan Sohrab , Dawood Muhammad , Aziz Mohsin , Bizanjo Mah Gul , Baloch Rozina 
Published: January 2024

In current era, graphene material contributes to the enhancement of polarization active-tuning and offers new procedure to function and restrict light on the nano-level scaled. we designed and systematically studied the surface plasmon resonance (SPR) characteristics of graphene layer on the inside of noble metal nano-U-shaped structure. It is found that the plasmon resonance of noble metal U-shaped nanostructure can be effectively tuned by adjusting the graphene Fermi energy from 0.2 eV to 1.2 eV. By optimizing the structural parameters of the U-shaped and introducing graphene different layers, the local field in the U-shaped model can be greatly enhanced. For realizing the strength of carrier density in graphene, Fermi energies also applied from to two portions from left and right in the nano-U-shaped structure with a specific graphene thickness of ~0.335 nm to ~2.68 nm . This type of hybrid plasmonics in graphene shows very prominent features for enhanced polarization hybrid-active tuning.
Energy Based Segmentation for Lungs Surrounding Pulmonic Diffusion in CT Images.
Authors: Khan Muhammad Junaid , Farhi Lubna , Imam Hassan , Rehman Farhan Ur 
Published: January 2024

Increasing number of COVID-19 Positive cases has lead for an immediate analytical requirement and fast decision making method that uses an adaptive algorithm to formulate an energy based segmentation technique. The proposed algorithm is developed using Level-Set to achieve the required results. This hybrid type method will use local along with global energies that happens to be very efficient for matching the patterns, segmenting the selected area and depends on tracing the anatomic type structures through exploiting the constraints that can be extracted out of the data set containing CT Images. This framework can perform an independent stochastic segmentation of COVID-19 in CT Imaging by smartly combining the level-set, region based, global along with three different types of energies being established as uniform modling energy (UE), mean separation energy (ME) and histogram separation energy (HE) all under same archetecture. The selection of Level-Set technique has solved the topology of the problem by defining the segmentation of boundary for local region that consist of statistics global in nature and energies local in nature given at individual point. Then strategically updating the local region by altering the energies either minimizing or maximizing it as desired. Furthermore, the proposed framework is analysed using different CT Images. The results obtained from this analysis shows that suggested framework achieves 89.5% similarity between segmented and ground truth by dice method, and by Hausdorff algorithm a minimum distance of 0.5(mm). The adaptive stochastic segmentation method in proposed framework can be applied to segment out different levels when there binary thresholds levels are greater than 0.3. The algorithm model is so designed that it can segment out the COVID-19 effected regions automatically from raw CT images with higher accurate boundaries that relatively works. The feature of this stochastic segmentation is associated with COVID-19 severity that arbitrate the causal of the COVID-19 severity.
Pervasive Computing Issues, Vision an Exclusive Relationship Of Pervasive And Cloud Computing
Authors: Farooq Muhammad Shoaib , Rabbani Taha Mubbashar , Alvi Atif , Naem Muhammad Athar 
Published: January 2024

Abstract Pervasive computing also known by the name of ubiquitous computing is a computer paradigm which focuses the integration of self-communicating and intelligent devices into user’s environment and daily life. Its major principle is to infuse computing into real world environment in such way that user cannot even realize it is existing. One of the best available source is mobile devices which can help with everything happening in real life. Mobile devices are not the only sources but they can be use to perform all the operations like downloading, uploading, computing the data, processing any type of data including audio video formats. The major issue could be the storage of such devices and the load taking to perform complex tasks. To tackle such issues, there is a need of cloud computing. Virtual Cloud computing could be the helpful as it provides the environment in which the larger data can be access pervasively. In this paper we will discuss how pervasive computing and cloud computing are associated with each other and how they can be beneficial to each other.
Urdu Sentiment Analysis: Future Extraction, Taxonomy, and Challenges
Authors: Mashooq Mariam , Riaz Shamyla , Farooq M S 
Published: January 2024

By the newly gained attention from several research areas for the field of opinion mining, work in Sentiment Analysis (SA) has also been increased. Sentiment analysis is actually a natural language processing (NLP) method which is implemented to decide whether the data is negative, positive or neutral. This analysis can also utilized to provide most appropriate countermeasures for various issues that are connected with particular fields. It is a contextual extraction and arrangement of text which recognizes and pinpoints subjective information regarding source material and helps to understand the social sentiment of people while monitoring online conversations, comments, tweets, or information on blogs, etc. There is wide utilization of Urdu language in offering perspectives that's why the Urdu language also wants opinion mining as well. In this research, a systematic literature review on sentiment analysis of Urdu language has been performed. This SLR is focusing on explicit research questions and afterward contributions are described appropriately. The findings of the review present a taxonomy that is based on the techniques of sentiment classification. Furthermore, in this SLR, we have extracted all the preprocessing techniques that were used in these 24 papers, the most adopted algorithms by the researchers, the most implemented sentiment analysis approach, and the feature extraction techniques are also extricated. Eventually, a thorough survey is given on all these considerations. After a detailed and deep evaluation, we have computed their accuracy results for better understanding of future researchers.
Improvement for Diagnosis of Gastric Cancer from Endoscopic Images using Machine Learning
Authors: Khan Maryam Kausar , Siddique Muhammad , Aslam Naeem , Khan Muntazir Hussain , Mukhtar Sara , Syed Bushra 
Published: January 2024

Detection of cancer disease in any part of a human body is of utmost importance as it can be cured completely. In this research work, a prognosis of early gastric cancer detection by applying modern machine learning algorithms augmented with fast and efficient classification of white light images. In earlier studies for early gastric cancer detection schemes, nominal endoscopic images demand more computational effort, which slows down process and takes more time. Moreover, in the contemporary methodologies, only basic parameters were used to detect the symptoms of gastric cancer such as accuracy. Whilst in the proposed methodology, protein structure of the cancerous part is also examined with the help of Alpha fold software. A dataset consist of white-light-images is developed from the endoscopic images of the suspected patients. By utilitarian of this dataset in the proposed scheme, results are drawn which shows greater accuracy at a lower cost as compared to contemporary techniques.
A Brief Survey on Natural Language Processing Based Text Generation and Evaluation Techniques
Authors: Philip Philemon , Minhas Sidra 
Published: January 2024

Text Generation is a pressing topic of Natural Language Processing that involves the prediction of upcoming text. Applications like auto-complete, chatbots, auto-correct, and many others use text generation to meet certain communicative requirements. However more accurate text generation methods are needed to encapsulate all possibilities of natural language communication. In this survey, we present cutting-edge methods being adopted for text generation. These methods are divided into three broad categories i.e. 1) Sequence-to-Sequence models (Seq2Seq), 2) Generative Adversarial Networks (GAN), and 3) Miscellaneous. Sequence-to-Sequence involves supervised methods, while GANs are unsupervised, aimed at reducing the dependence of models on training data. After this, we also list a few other text generation methods. We also summarize some evaluation metrics available for text generation and their Performance
Development of Light-Weight Convolutional Neural Network Model to Diagnose Tuberculosis
Authors: Raziq Abdul , Ahmed Noor , Khan Sohrab , Bizanjo Mahgul , Uddin Noor , Baloch Rozina 
Published: January 2024

Tuberculosis (TB) is a contagious chest infection. World Health Organization has introduced different TB control programs in various countries. For the diagnosis of TB, the doctors mostly recommend chest X-ray (CXR) because it is more cost-effective and less time-consuming than existing sputum tests and Tuberculosis Skin Tests (TST). As per the research, deep learning models are best for TB diagnosis, by using CXR rather than normal eye-sight-based traditional method. Since doctor’s eye-sight or his experience is prone to human error, therefore, to solve this problem many Convolutional Neural Network (CNN) based models are introduced. Some of these models have high computational costs, and better accuracy making them heavy model. Whereas, others have less computational costs and lower accuracy making them light-weight models. Such models are further modified by the researchers to be more appropriate for better TB diagnosis, termed as Transfer Learning (TL) technique. However, TL leads to complex CNN structure and high computational cost. The proposed model named as Light TBNET(L-TBNET), attempts to provide less computational costs and higher accuracy simultaneously, as compared to other models such as, ShuffleNet, ResNet-50, MobileNet v2, Inception, and DenseNet. Moreover, the proposed does not include TL technique. This is accomplished by combining standard convolutional layers as well as depth-wise separable convolutional layers resulting in a hybrid model. The accuracy of the proposed model is 96% with lesser computational cost. In this way, the model contributes in providing a light-weight CNN model with higher accuracy.
Early-Stage Heart Disease Prediction using supervised Machine Learning Algorithms
Authors: Arif Hamza , Siddique Muhammad , Aslam Naeem , Pervez Muhammad Tariq , Khan Maryam Kausar 
Published: January 2024

Heart Disease is believed to be the number one killer globally, and its diagnosis has long been considered a very crucial problem. With the revolution of the modern world, it is very important to detect heart disease at its earlier stages so that patient treatment should be done effectively. Many previous researchers used Hybrid and Data Mining techniques to predict heart disease at its earlier stages, but they couldn’t get the required results. The evaluation of the Machine learning and artificial intelligence research community mainly focused on these techniques to get better results. This research paper used six supervised machine learning classifiers like Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, K-Nearest Neighbor and Naïve Bayes. We used two different datasets for the sample data in terms of attributes and values. We also used three different feature selection techniques to improve our accuracy by selecting the most important features. We first applied these machine learning classifiers to our proposed dataset without using the feature selection technique and computing the results. After that, we applied LASSOM, MRMR and MIFS techniques and derived the results with improved accuracies. In the end, we make a comparison table between the results that are computed with and without the feature selection technique. According to our experimental results we can say that the results accuracies computed with the feature selection technique are higher than those computed without feature selection techniques. Evaluation techniques like Confusion matrix, Accuracy, precision, Recall, F1 Score, PR Curve and ROC curve are used to measure the performance of our classifiers. So, we conclude that if we use any of these feature selection techniques, we can conclude better results and predict heart disease at its earlier stages with improved accuracy.
Fuzzy smart framework for diagnosis of liver disorder in Rheumatoid arthritis
Authors: Shakil Marrium , Saleem Darakhshan M , Khan Kashif Mehboob , Shams Sarmad , Farhi Lubna 
Published: January 2024

Despite most of the centralization techniques used in medical diagnosis, a right finding is yet considered a priority. Most of this condition is due to the fact that clinical issue requires both expertise and knowledge in managing with complexity. In this paper, we intend a fuzzy expert framework for studying and examining the risk of anemia and liver dysfunction due to the usage of drugstherapy (DMARDS) among patients in Rhematoid arthritis. The data illustration of this system is given from a significant level, taking into account the recorded data about signs and symptoms in rheumatic patients as well as the clinical appraisal. This framework imitates the professional doctor's behavior. The system predicts the risk of anemia and liver dysfunction in patients treated with DMARDS for rheumatoid arthritis.The framework is designed in such a manner that the patient can access it individually and has correlation with other normal indicative frameworks is quicker, less expensive, and furthermore more liable and exact.
Fake News Detection in Urdu using Deep Learning
Authors: Rauf Farah , Irfan Roha , Mushtaq Lyba , Ashraf Mohsin 
Published: January 2024

An increase in the volume of false information circulating as a direct consequence of the rise in the growth of social media has an effect of misguiding the general population. Therefore, a mechanism for identifying fake news is required to prevent such repercussions. Almost all of these already existing algorithms for detecting fake news work with resource-rich languages such as Spanish and English; however, few techniques can work for resource-constrained languages such as Urdu. The study aims to identify instances of fake news written in Urdu by applying deep learning and machine learning methodologies. We use the MuRIL and T5 models for the implementation process because these models were developed specifically for Urdu and Hindi language recognition. The newspaper articles included in the valid subset derived from credible news sources, and the accuracy of these items have been checked by hand. Inside the misinformation subsection, the problem of how challenging it was to discover fake news was overcome by employing experienced reporters who were native Urdu speakers and instructing them to compose deceptive news items purposefully. It has allowed the researchers to overcome the problem. The dataset covers various subjects, including business, sports, health, showbiz, and technologies. We have carried out baseline classification to use our Urdu database as a standard for other datasets. The experiments with the various systems have indicated that the MuRIL model significantly improves over the other models, such as the T5 Model, and attains an average F1 score of 0.96 and a validation accuracy of 0.83. These results have been determined based on the findings of the experiments.
Identifying Cancer Patients at risk for Heart Failure using deep learning models
Authors: Anis Muhammad , Majeed Adnan , Khalil Mudassir , Aslam Naeem 
Published: January 2024

The cardiotoxicity that may occur as a side effect of cancer treatments has emerged as a significant problem. Cancer patient’s quality of life may be improved if those at risk of cardiotoxicity are identified early and given prophylactic treatments before receiving cardio toxic drugs. The advancement of deep learning will help to support medical practitioners in their ability to make accurate. This study will focus on predicting the enhancement of heart failure in cancer patients. The purpose of this research is to determine whether historical data from electronic health records can accurately predict the occurrence of heart failure in cancer patients. We investigated deep learning algorithms by applying them to 300 cancer patient’s dataset drawn from the Seer database. We determined that there were a total of 300 eligible cases and matched them with controls according to gender age and the primary cancer type etc. Results from the tests suggest that techniques based on deep learning may effectively capture clinical characteristics linked with heart failure in cancer patients.