bluuebunny/crossref_metadata_2025 huggingface dataset:
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
Solving Triangle Routing Problem by Using Route Optimization Technique in Mobile
Authors: Ahmed Manzar , Haque Ibrar-ul- , Razvi Huma Hasan , Khan Shakil Ahmed , Arshad Jawad Ali , Ahmed Mishaal
Published: January 2024

The demand for internet service has increased expeditiously and users demand new services internet with a high-speed data rate to run many internet programs in parallel. Most Internet users want to run many applications such as video, audio, and many other software programs at the same time. After the COVID-19, the demand for new internet services has increased dramatically for online classes, and video lectures in real-time. Therefore, demand for fast internet services via wireless communication has increased rapidly. The user demands a high data rate and more bandwidth for internet services. There are many issues regarding high data rate but one major problem occurs when the user changes his location or moves to a new network because a new IP address must be, assign with no delay. Hence, the need of dynamic IP addresses is required, whenever a mobile host moves to another location. The packets do not come in a single form and an agent is involved in the whole process, the delay occurs because the agent again assigns a new IP address. This problem occurs in the wireless network. The second problem is the inefficient behavior of non-optimized Mobile IP. In mobile IP protocol, packets of the variable lengths are sent through the wire or wirelessly to the hosts to the destination from, the main location and have to be sent via the agent currently in NS-2. The variable-length packets can be routed directly from mobile hosts in a wired system. This type of routing is called “triangle routing”. In this paper, the dynamic route optimization technique has been proposed to address the delay problem. In addition, the implementations of Mobile IP on wired- cum wireless scenario and the “Route Optimization” extension in the NS2 are used. The simulation tool is used to solve the Mobile IP problem by using dynamic IP addresses considering that the Mobile host moves from one network to another network freely. The optimization used in Mobile IP to solve the triangular routing problem.
Theoretical Study of Localized Electric Field Enhancement for Plasmonic Nano-Imaging via Graphene-Based Heterogeneous U-Shaped Multi-Nanogaps Superlens
Authors: Uddin Noor , Khan Sohrab , Ahmed Noor , Raziq Abdul , Ahmed Shakeel , Mirwani Nawaz
Published: January 2024

In the recent times, Graphene 2-D material has risen as a promising platform for opto-electronics and hybrid-based nanophotonic devices due to its optical characteristics large carrier mobility. The plasmonic U-shaped superlens photolithography interference system is often created with more complex multi-layered noble thin film geometries without graphene. However, this research includes a theoretical investigation of localized electric field enhancement for plasmonic nano-imaging via graphene-based heterogeneous U-shaped multi-nanogaps superlens by adjusting the graphene electron mechanism. It is determined that the plasmon system reaction in graphene thicknesses (⁓0.335nm and ⁓0.67nm) can be extraordinarily documented in the photoresist layer by modifying the thickness (layer) of thick graphene covering heterogeneous U-shaped multi-nanogaps superlens geometry. Moreover, it is described by means of the hybridization resulting in the alteration of the localized electric field enhancement within the graphene material-covered gold nanoimaging superlens. Ultimately, this theoretical investigation reveals that appropriate designing of optical superlens-based on graphene material can observe superior electric field enhancement for plasmons in low-priced , quality and simple nanoimaging for forward-looking plasmon-based applications of photolithography. such as drug delivery, Magnetic resonance imaging(MRI).
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.
Current Stance on Requirement Prioritization Techniques: Challenges, Opportunities, and Future Directions
Authors: Gull Saba , Umer Rahila , Uddin Noor , Khan Sohrab
Published: January 2024

Requirement Engineering is an important aspect of Software Engineering, while Requirement prioritization is a crucial phase of Requirement Engineering, specifically in agile development. In today's world, software systems are nothing but the backbone for business organizations. However, for business operations, requirements are changing rapidly, and it seems to be impossible to accomplish the requirements at once. It may be possible with the help of requirement Prioritization as it decreases the risk for failure. It helps implement the most required and prioritized feature given by stakeholders. The entire purpose of this research is to investigate and rate the complete process of Requirement Engineering, specifically the current requirement prioritization techniques, by conducting an extensive literature review. Furthermore, to identify the opportunities, gaps, and challenges for future
Formal Modeling and Analysis of Integrated Healthcare System using Colored Petri Nets
Authors: Jabbar Muhammad Abdul , Hussain Mahmood , Farooqi Saad , Ashraf Mohsin
Published: January 2024

Given today's situation, when a disease like COVID-19 has engulfed the world, the importance of the Integrated Healthcare System (HCS) increases a lot. Pakistan's healthcare system is not linked. Due to which the patients have trouble in the flow required for treatment in the existing healthcare system. This research understands and integrates the healthcare system in Pakistan. For this purpose, a formal modeling technique is used which not only helps us to integrate the system but also validate and verify the system. First of all, this research reviews existing Healthcare Systems. A comprehensive survey leads to the limitations of existing architectures. To address the limitations, this research proposes an improved architecture for the healthcare system that alleviates the issues of existing architectures. To verify the proposed architecture at the design level, a formal approach is adopted. Therefore, this architecture is modeled using a well-known technique, called Colored Petri Net (CPN). State Space Analysis after simulating the model ensures its consistency and authenticity. The properties indicate that any patient who enters the system exits without any hindrance after undergoing treatment.
Enhanced IoT Composition Architecture based on DIY Business Process Modeling: CoAP based Prototype
Authors: Khan Muhammad sohail , DoHyeun Kim , Tila Faiza
Published: January 2024

Internet of Things has been the focus of research and development in the recent years. This increased attention has resulted in various approaches for the realization of the vision that every physical entity should be a part of global network of things. Representation of real world entities as virtualized entities in the cyber world is one such approach where the behavior of virtual objects is exposed as services and it can be accessed and manipulated like real world objects. The problem with the implementation is that most of the users lack the skills to utilize the services of these connected objects to create their own applications. We present a novel Business Process Modeling based approach to provide an intuitive Do-It-Yourself interface for generic users to easily compose their own IoT applications regardless of their programming skills. The proposed architecture has been implemented as a prototype which utilizes Constrained Application Protocol for connecting the things. A primary performance analysis of the major functionalities has been presented in this paper.
The Role of CNN and RNN in the Classification of Audio Music Genres
Authors: Ashraf Mohsin , Abid Fazeel , Atif Muhammad , Bashir Satwat
Published: January 2024

This study aims at determining how various types of neural networks can be used to categorize music files. We used the GTZAN dataset that contains several genres of traditional music. Every genre has some common traditions that can be referred to as features. The task of classifying music genres based on features is challenging. Deep neural architectures such as Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) have been considered for music analysis. However, it has been observed that neural architectures are data-intensive and face the problem of overfitting. To address this issue, we present a framework containing CNN and RNN with Long Short Time Memory (LSTM) having multiple layers to categorize the music genres and handle the problem of overfitting. Our experiments also revealed the strengths and limitations of deep learning. Finally, we found CNN to be best among other state-of-the-art models and achieved the training and test accuracies of 86.53 % and 81.90 % respectively.
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.
Requirement-Based Automated Test Case Generation: Systematic Literature Review
Authors: Farooq M S , Tahreem Tayyaba
Published: January 2024

There exist multiple techniques of software testing like requirement-based testing (RBT) an approach of software testing from which the tester can generate test cases on the base of requirements without considering the internal system’s structure. In the current area, automation testing is used to minimize time, cost, and human effort. As compared to automated testing, manual testing processes consume more human effort and time. Requirements are documented in natural language so there is no extra training required to understand requirements, RBT is the most used testing technique. Test cases generated with customer requirements are mainly focused on functional test cases. Most approaches focus on real-time embedded systems rather than UML diagrams because non-functional needs are not captured in test cases derived from UML diagrams. Metamodels can be used to extract information from requirements in some cases. Active testing approaches, bounded model checking, activity diagrams, Petri nets round strip strategy, and extended use cases are just a few of the typical ways used to generate test cases. In this article, multiple techniques of automated test case generation have been discussed which are not being addressed in state-of-art literature reviews. Studies included in this systematic literature review (SLR) are built on a set of three research objectives and a variety of high-quality evaluation criteria. Taxonomy has been presented based on test case generation with requirement-based techniques and tools. In the end, gaps and challenges have been discussed to assist researchers to pursue future work.
Effect of Blockchain on Education: A Systemic Literature Review
Authors: Talat Muneeba , Riaz Shamyla , Farooq Muhammad Shoaib
Published: January 2024

Education is a significant part of the human life that is changes with time as the world goes through technological advancements. One of these technologies is blockchain that is being integrated into different fields like finance, agriculture and education. The purpose of this paper is to write a review on the work that has been done in the education sector in the last five years. Firstly, after all levels of the selection procedure, 24 articles were chosen to respond to the research questions. Secondly, the exploration of blockchain in the education sector is discussed in detail and how this technology can be of great advantage in the future. Thirdly, taxonomy of blockchain’s use in education is conferred. Lastly, the challenged and gaps of this specific topic were discussed.
Hate Speech Detection Model on Web 3.0 Based Platform using Blockchain and NLP
Authors: Farooq Muhammad Shoaib , Ali Usman
Published: January 2024

With the increase in usage of social media applications like Facebook, Twitter, or Instagram, hate speech is also on the rise. Hate speech can be defined as the ill talk toward any race, caste, religion, and ethnicity. Now with the new development of web 3.0 which is decentralized, it is difficult to control elements like hate speech because there is no central body that can control it. In this research paper, we have proposed an NLP-based hate speech detection model for Web 3.0 that is able to detect hate speech with the accuracy of 89% before it gets to Blockchain. The NLP-based algorithms, IPFS, and smart contracts are used.
Comparative Analysis of Facial Recognition Models to Record Classroom attendance with one click
Authors: Hussain Shahzaib Shoukat , Qayyum Kashifa , Khalil Dr. Mudassir , Aziz Omer
Published: January 2024

Human face recognition is the hottest topic in this era due to its importance in identity authentication and security checking. It has grown a momentous spot among all biometric-based systems. It can be used for verification and scrutiny to prove the identity of a person and sense individuals, singly. Great success has been achieved recently on face recognition comparison by using different models. But in this research, the HOG (Histogram Oriented Gradient) and Vgg (Visual Geometry Group) algorithms are selected as these algorithms are common and still widely accessible on the internet, and these are usually used out of the box for various applications. In this paper, we examine the accuracy and time efficiency between the HOG (Histogram Oriented Gradient) and machine learning model. Also, this paper conferred these pre-trained models and outlines which results in high-tech performance without the need for any prevailing hardware. Their features are observed and the result of each model is estimated. Their comparative analysis of the output of both of these models with the execution time and the number of repetitions of the practical algorithms is offered. It also constructed two simple applications for face recognition-based algorithms. By using these application testing data is taken from the live stream/camera in the form of image and compared through training images stored in the faces database and their time and accuracy are noted during this process and their performance is evaluated based on time and accuracy.
Role of Logistic Regression in Malware Detection: A Systematic Literature Review
Authors: Farooq Muhammad Shoaib , Akram Zeeshan , Alvi Atif , Omer Uzma
Published: January 2024

When brain, the first virus known introduced in computer systems, requirement of security was raised. Malware Detection turn out to be more vital when network is used for transferring Secret Information. Nowadays our central attributes i.e., Banking, Agriculture, Robotics, Virtual Social Life, Online Multiplayer Gaming, Private Conversations etc. is practicing internet and Malware will abolish everything if we discount it. Lots of new malwares are located by the passage of time, so we need a reliable, fast and trustworthy machine learning technique to handle them. Logistic Regression Classifier is useable for handling such a huge data, majorly counted in this paper. This is a complete SLR that delivers progressive approach in the field of malware detection. It legally reduces time and the cost of researchers. Limitations and future directions of machine learning classifiers to detect malwares are discussed in this paper.
Harvesting Sea Wave Energy By Using Point Absorber and Blockchain Technology
Authors: Ahmed Afshaar , Liaquat Syed Faraz , Rizvi Huma Hasan , Ahmed Manzar , Rizvi Jeffery Ali , Ahmed Mishaal
Published: January 2024

In the current era of modern innovations, the applications of energy trading using secure software systems are increasing dramatically. The concept of energy generation using sea waves is slowly emerging. Moreover, to the best of our knowledge, the use of blockchain in this domain is in its infancy. In this research work, the Point absorber energy converter will be analyzed for power generation, which works with the power of the incoming and the outgoing waves. This system is more efficient and economical compared to old converters. The power in waves has potential force, which can be converted into kinetic force. This kinetic force is used to rotate the alternator, which produces useful power and this power can be stored in batteries or used directly by using various methods. The main benefit of this WEC is that it can be cone in very shallow waters and does not require deep waters to generate power. There is also another positive factor of this converter is that when the wave enters the converter’s incoming as well as the outgoing waves in the front and back direction and the height of the waves come onward, the shore the height of the water increases and decreases as the waves recede. This level changes in height and is used to move another alternator, which, is used on the same structure. The alternators used in the research are based on the axial flux alternator, which works on low rpm and has a power density much higher than the radial flux motor. For energy, trading, and control blockchain method has been employed considering power shortage and theft in our country and globally. The results of the paper reveal that the proposed system can be very effective if applied on a large scale.
Design of a Substitution Box using a Novel Chaotic Map and Permutation
Authors: Farooq Muhammad Shoaib , Munir Kinza , Alvi Atif , Omer Uzma
Published: January 2024

Information security plays a vital role in the security of data, because data is the most important asset for any organization. So, the first and foremost importance in the functioning of any organization, is the security of its data.To secure data, cryptographic techniques play a vital role in securing data. For that thing, chaos plays a vital role in cryptography. For producing chaos, we have implemented a chaotic map, in which we have implemented linear equation along with exponential equation based on conditions, which integrates chaos in the plain text which makes it less open to crypt-analysis. To make a chaotic map, more robust, it must have some properties, which include High non-linearity, SAC values must be nearer to 0.5 and and it should also satisfy the property of Linear probability, Differential uniformity and BIC value that is Bit Independence Criterion. Moreover the values, we achieved in our S-Box were proved to be competitively better from many S-Boxes which have been designed by now.
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.
Designing Fuzzy Logic Controller for UNIX find Command
Authors: Arif Butt Muhammad , Idrees Muhammad , Saleem Raza Muhammad , Ahmad Ashfaq
Published: January 2024

This research article propose a novel fuzzy logic controller (FLC) for fuzzification of the standard UNIX find(1) command. The interface of the proposed ffind(1) command is more user friendly and efficiently handles imprecise conditions. The FLC of the novel fuzzy-find (ffind(1)) shell command is written in C language, and it can locate files and directories using three associated timestamps (mtime, ctime, and atime). The user specifies the search criteria which can be any of the three times in days, the ffind(1) command recursively reads the timestamps from inode blocks of all the files under the given directory hierarchy. The ffind(1) command converts the file timestamps (number of seconds passed since UNIX epoch) into days, and then the difference between user given value and each of the file’s timestamp is calculated. These crisp differences of each file are input to our proposed fuzzy logic controller, which finally generates a crisp selection index (S.I.) value for each file. The file having the maximum S.I. value is the target file. Moreover, the user can also give a threshold value to see the sorted list of files that are a close match to the given selection criteria. The proposed study designed an algorithm that demonstrates the complete working of ffind(1) command and also compare the standard UNIX find(1) command with the novel ffind(1) command using different data sets. The results show that the proposed ffind(1) command performs significantly better than the vanilla flavour find(1) command. The proposed ffind(1) command once added in the /bin directory of any UNIX based system will allow the users of the system to make use of the more powerful and flexible features of ffind(1) in order to search for files and directories in a more user friendly or fuzzy way, which is the way humans thinks.
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.
COVID-19 Vaccine Supply chain management by Blockchain
Authors: Shahzad Talha , Alvi Atif
Published: January 2024

The pandemic of COVID-19 is a big challenge to human health. Covid-19 pandemics is the worldwide disease starting from Wuhan in December 2019. It effect a huge amount of people. Now the whole world enter in the process of Immunization and vaccinationabst: The COVID-19 pandemic has as of now featured the absence of flexibility in supply chains, as worldwide organizations fall flat from disturbances at single hubs and associations. With regards to COVID-19 pandemic, the fast carry out of its vaccination and the execution of an overall overall immunization process is very difficult, yet its prosperity will rely upon the accessibility of a functional and straightforward conveyance chain that can be inspected by all important beneficiaries. In this research, we examine how blockchain innovation can help in a few parts of vaccine distribution of COVID-19 . We describe a framework in which blockchain innovation is utilized to surety information respectability and changelessness of recipient enlistment for inoculation, keeping away from character burglaries and pantomimes. Brilliant agreements are characterized to screen and track the legitimate immunization conveyance conditions against the protected dealing with rules characterized by antibody makers empowering the attention to all arrange peers. For antibody organization, a straightforward and sealed answer for incidental effects self-revealing is given thinking about recipient and administrated immunization affiliation. A model was carried out utilizing the Ethereum network test, which determine the conditions of COVID-19 Vaccine.. The outcomes got for each chain activity can be checked and approved on the Etherscan. As far as throughput and versatility, the described blockchain framework shows promising outcomes while the assessed cost as far as gas for vaccination situation in light of genuine information stays inside sensible cutoff points.
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
Detection of Heart Disease Using Supervised Machine Learning
Authors: Kanwal Amna , Ahmad Khawaja Tehseen , Abid Kamran , Aslam Naeem
Published: January 2024

One of the most prevailing and serious disease affecting human’s health is Heart Disease (HD). Early diagnosis may allow for heart disease prevention or reduction, which could lower the rate of death.Machine Learning techniques have produced a variety of solutions for heart disease prediction and is capable of predicting illness at early stage . This study propose a model that includes many machine learning (ML) techniques to obtain accurate heart disease (HD) predictions. Data collection and pre-processing are used to create accurate data for the training model. Supervised Machine learning classifiers like support vector machine (SVM), decision tree (DT), logistic regression (LR), K Nearest Neighbor (KNN) and Naïve Bayes (NB) are used for predicting heart disease. Most relevant features are selected by using Relief and LASSO feature selection techniques. Various evaluating methods like, sensitivity, accuracy, specificity, MCC,confusion matrix and precision are used for the performance evaluation of model. This study did comparative analysis using supervised machine learning and feature selection techniques. Decision tree gives highest accuracy of 85.21% with all features. On the other hand, with feature selection techniques SVM has an excellent performance. Future strategy is to use Deep learning algorithms and other feature selection techniques.
Wheat Disease Detection for Yield Management Using IoT and Deep Learning Techniques
Authors: Akbar Sana , Ahmad Khawaja Tehseen , Abid Mhammad Kamran , Aslam Naeem
Published: January 2024

Our economy is mostly based on agriculture. One of the difficult problems in the agriculture sector is crop yield predictions. Crop yield prediction using a machine learning algorithm with the help of IoT increases the production of wheat yield and improves the quality of yield. Today's low agricultural production is a problem for farmers. Low crop output is mainly caused by a lack of information regarding soil fertility and crop selection, and proper crop selection is the key to maximizing crop yield. One of the interesting agricultural research areas where deep learning (DL) algorithm concepts can be used is the identification of wheat disease from images. We consider two leaf diseases septoria and stripe rust and also take a healthy leaf and then do a comparison between the leaves using CNN. As a contribution, we developed a system ML with a neural network mobilenet and efficient net-b3 that detects wheat leaf disease and improves accuracy gradually. Moreover, we do a complete review of yield management in which IoT sensors are used with machine learning algorithms. This study aims to create a system that can correctly choose a crop for maximum yield utilizing IoT devices and machine learning (ML) algorithms. We achieve 97% accuracy using mobilenet which is better than the efficient net. The presented work also applied different image augmentation techniques to remove the problem of overfitting. The presented work is compared with the state-of-the-art method in terms of accuracy and precision score.
Industrial Control and Building Automation System Penetrating Testing using Modbus TCP Testbed
Authors: Ali Muhammad Usman , Akhtar Muhammad , Durad Hanif
Published: January 2024

Industrial Control System (ICS) plays a vital role in industries as it controls industrial processes such as power plants, food production, transportation, water and gas distribution etc. Similarly Building Automation System (BAS) is utilized for control, energy efficiency and conservation of modern buildings. As both BAS and ICS systems are becoming increasingly interconnected with networking technologies and becoming a lucrative target for attacks thus pose a serious threat to the infrastructure they control. ICS and BAS networks have been using legacy protocols with implementation of ICT protocols and technologies to be connected with modern networks. Thus, they have lack of security implementation. This paper presented a test-bed for testing vulnerabilities in Modbus protocol on HVAC control system. Two MITM attack scenarios were discussed and performed to demonstrate the weakness in the Modbus TCP protocol. The proposed system was tested using EasyIO-FS-32 server class controller having Modbus RTU, TCP and BACnet MSTP, TCP.
Identification of Malicious PDFs Using Convolutional Neural Networks
Authors: Tayyab Umm-e-Hani , Zain Muhammad , Khan Faiza Babar , Durad Dr. Muhammad Hanif
Published: January 2024

With multiple possible carriers of malware, one of the most targeted file formats by malware writers is PDF format due to its inherent shortcomings as well as the limitations of PDF readers. The PDF-based attack is one of the most common attacks among document-based attacks. Some of the most luring features of PDF files include their widespread use which has replaced Word documents quite dominantly, secondly the ease of crafting a malicious PDF, and above all its capability of containing javascript. Initially, researchers focused on identifying malicious PDFs by comparing the structural changes between benign and malicious PDFs. Malware writers started using the evasion techniques to hide the malware so that structural changes can be hidden. Researchers started exploiting the benefits of Artificial Intelligence to combat these evasion techniques. We developed a convolutional neural network, trained to classify PDFs as benign and malicious using the byte level information, on a dataset of approximately 21,000 files. Our model can detect malicious PDFs without the human intervention and manual feature extraction. Moreover, our model detects malicious PDFs with 97% accuracy.
AI Empowered Machine Learning and Deep Learning for Combating with Covid-19; State of the Art contributions, Available Datasets, Challenges and Future Directions
Authors: Hameed Sana , Arslan Farrukh , Iqbal Munaza , Ali Mubashir , Haroon Muhammad Umar , Imran Naveed
Published: January 2024

The brand-new coronavirus COVID-19 spread rapidly over the globe and killed plenty of people. The World Health Organization (WHO) has classified COVID-19 as a global pandemic and all possible efforts are made to save human lives. Various governments implement complete and smart lockdowns to reduce COVID-19. For the detection and diagnosis of COVID-19, the conventional Polymerase Chain Reaction (PCR) test is regarded as essential. But an examination of a PCR test is time-consuming, requires resources as well as reports a large number of false negative results. Artificial Intelligence is fast growing technology with unlimited successful implementation with remarkable results in all problematic sectors. There are already several examples of AI-enabled Machine Learning (ML) and Deep Learning (DL) being used successfully in the healthcare industry. We have evaluated the most recent and cutting-edge research in this paper. ML and DL-based work for combating COVID-19. We discussed the COVID-19 background with all essentials and presented ML and DL-based systems highlighted by a new hierarchy. Identification and diagnosis of COVID-19 by considering medical images, sound analysis, and textual and non-invasive technologies. Further, we have also tabularized the COVID-19 datasets with their possible implementation. In last, we have listed the current challenges which are faced by ML and DL while combating COVID-19 and future directions for research.
An Improved Semantic Query Expansion Approach Using Incremental User Tag Profile for Efficient Information Retrieval
Authors: Raza Muhammad Ahsan , Ali Muhammad , Pasha Maruf , Ali Mubashir
Published: January 2024

The World Wide Web (WWW) comprises a wide range of information, and it is mainly operated on the principles of keyword matching which often reduces accurate information retrieval. The Keyword matching mechanism faces word mismatch problems while retrieving relevant information. Furthermore, the inherent ambiguity of short keyword queries demands enhanced methods for Web retrieval. Ontological-based query expansion is one of the primary methods for Web retrieval, and it handles the vocabulary mismatch problem by computing semantics from the ontology knowledgebase. However, the retrieval of information relevant to user interests is a major challenge. In this paper, we seek to improve retrieval performance by leveraging user preferences and ontology semantics in the process of query expansion. The expansion words are added to the user query using WordNet lexicon and domain ontology. Additionally, the search intent of the user is also added as expansion words by exploiting a tag-based user profile. When it comes to obtaining relevant documents, the proposed framework outperforms the keyword-based approach by achieving a 76% F1-score. This noticeable improvement accurately reflects the importance of including user intents in the process of semantic query expansion.
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.
A Treatise to Metrics and Frameworks for Semantic Social Networks: A Systematic Literature Review
Authors: Akram Hina , Pervez Mir Rizwan , Farooq Muhammad Shoaib
Published: January 2024

In the semantic web, each piece of data, referred to as metadata, is accompanied by meaningful data. This information is then used by machines to make calculations-based decisions. This implementation of semantic web methods in social networks shapes the premise of Semantic Social Network (SSN). SSN creates a virtual group of friends and family where people can share resources, thoughts, by staying associated with their loved ones. This semantic social data can be used to gauge different conduct ascribes of SSN individuals utilizing measurements. The extricated results from these measurements can help experts of SSN to settle on better choices in giving offers, rewards, badges to individuals. There are frameworks available for semantic social network analysis as well. A systematic literature review (SLR) is presented in this paper by leading an overview of available metrics to calculate semantic social network member traits and frameworks used for social network analysis. The basic SLR has been aggregated by looking into research articles distributed in all around presumed scenes somewhere in the range of 2000 and 2020. A sum of 22 papers was painstakingly filtered through an orderly interaction and grouped appropriately. The essential target of this precise investigation is the assortment of all-important examinations on currently available metrics to calculate members ascribes of the semantic social network and the frameworks used for semantic network analysis. As a result, six metrics and nine frameworks are extracted and presented. Moreover, this paper also discusses possible ways to improve metrics for a better semantic social network (SSN) analysis. In recent years, countless efforts have been made in the Semantic Social Network Analysis area to measure ascribes and define frameworks. Consequently, it is imperative, to sum up, gather, break down, and group the research about this area. The reason for this research is to introduce an extensive deliberate literature review for the aggregation of metrics and frameworks used in semantic social network analysis SSNA.
Prediction of Product Rating based on Polarized Reviews using Supervised Machine Learning
Authors: Khan Raheel Ahmad , Mannan Abdul , Aslam Naeem
Published: January 2024

E-commerce websites facilitate customers to leave their experiences in the form of textual reviews for a variety of products. Recently, online reviews have played significant influencing role in customers’ decision for purchasing. The reviews have information and first hand experience about products’ quality for customers. Free-text sections are frequently found on online review web pages in addition to star-level reviewing options. But on many web pages, we find only the former option. Therefore, there is a need to convert the text-written reviews to star-level on the basis of the information they contain. Automatic conversion of online text-based reviews has recently been emerged as an active field of research in machine learning and deep learning. This paper presents a supervised machine and deep learning based solution to transform text-based reviews to star-level numerical representation by exploiting polarization detected on the basis of lexical analysis. Experiments were conducted on famous Amazon dataset under different choices of regression and classification techniques. Experimental results have indicated that the use of polarized reviews can significantly improve the rating prediction.