bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of Deep Learning Approaches for Sentiment Analysis
Authors: Saqib Sheikh Muhammad , Naeem Tariq , Ahmad Shakeel , Sulaiman Alorﬁ Almuhannad 
Published: January 2024

Due to the increasing popularity of posting evaluations, sentiment analysis has grown to be a crucial area of study. Machine learning techniques that are supervised, unsupervised, and semi-supervised have worked very hard to harvest this data. The complicated and technological area of feature engineering falls within machine learning. Using deep learning, this tedious process may be completed automatically. Numerous studies have been conducted on deep learning models like LSTM (Long Short-Term Memory), CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), and GRU (Gated Recurrent Unit). Each model has employed a certain type of data, such as CNN for pictures and LSTM for language translation, etc. To discover the optimal deep learning methodology for the given data, authors here proposed many deep learning methodologies for text data on sentiment analysis. A publicly available dataset including both positive and negative reviews on LSTM, CNN, RNN, and GRU was used in the experiments, and the findings showed that CNN had the highest accuracy compared to the other models. Based on the experimental results of CNN, it was found that prediction from the proposed work exhibited a significant improvement over existing work.
Diabetic Retinopathy Identification from Eye Fundus images using Deep Features
Authors: Mubashra Ayesha , Naeem Ahmad , Aslam Dr. Naeem , Abid Muhammad Kamran , Haider Jabran 
Published: January 2024

Diabetes mellitus can cause diabetic retinopathy (DR), which affects the blood vessel networks in the retina of the eye. The symptoms of diabetic retinopathy may be missing or minimal. For the majority of diabetes patients, a serious problem can damage their vision. It takes a lot of effort for competent doctors to identify lesions in the color-fundus pictures that can be used to accurately diagnose the illness required for diabetic retinopathy diagnosis. The existing Diabetic retinopathy therapies can only slow or delay vision degeneration, highlighting the significance of routine scanning with very effective automatic detection techniques to identify patients early. Therefore, early symptom detection may help prevent blindness. The proposed work aimed to create an automated model for recognizing the initial stages of DR detection using retinal pictures. This research paper presents a novel approach to the multi-classification of Diabetic Retinopathy (DR) based on a combination of deep learning and machine learning techniques. Our proposed model incorporates a Convolutional Neural Network (CNN) with an attention mechanism, allowing for the assignment of weights to extracted features depending on their relevance to the classification task. We employ Non-Negative Matrix Factorization (NMF) to further optimize feature selection to identify the most informative features from weighted representations. Then, the input features are classified using a machine learning classifier based on severity levels. The proposed model is assessed using four distinct ML classifiers (Support Vector Machine (SVM), Decision Tree, Naive Bayes, and KNN) and two publicly accessible databases (DDR and APTOS-Kaggle). For model evaluation, FPR, Specificity, Sensitivity, Precision, false positive rate, and Accuracy are selected. The proposed model's accuracy on DDR is 89.29%, whereas Aptos Kaggle's accuracy on DR grading is 84.1%. KNN classifier performed better than all other classifiers on DDR and Aptos Kaggle datasets with an average accuracy of 89.55%, and 85.78 %respectively for multiclass classification. The findings of the evaluation and experimentation demonstrate that the suggested approach is effective for DR grading accuracy and disease diagnosis at an early stage.
RLTD: A Reliable, Link Quality, Temperature and Delay Aware Routing Protocol for Wireless Body Sensor Networks
Authors: Iqbal Shahid , Raza Bhangwar Ali , Ahmed Adnan , Ahmed Fayaz , Awais Muhammad , Hussain Abrar 
Published: January 2024

The miniaturization of biomedical sensor nodes has paved the way for remote patient monitoring using wearable wireless sensor nodes. The network of such invasive and non-invasive sensor nodes is most commonly known as Wireless Body Sensor Network (WBSN). The healthcare applications of WBSN are delay sensitive and of critical nature, therefore, it require reliable and timely dissemination of patients’ critical data to the remote location to ensure Quality of Service (QoS). However, poor link quality (LQ) may affect QoS and result in higher transmission delay, loss of critical data packets data corruption, packet retransmissions, thereby, degrading network performance and compromising the patient’s privacy and confidentiality. Furthermore, the in-vivo wireless sensor node produces electromagnetic radiations which are absorbed by the human body thereby, causing temperature rise around the implanted sensor node. The prolonged communication of such in-vivo nodes creates a hotspot node that results in sensitive tissue damage. Therefore, ensuring hotspot-free communication is a necessity in WBSN. Considering such issues, we propose a Reliable, Link Quality and Temperature aware routing protocol (RLTD) for delay-sensitive application of WBSNs that route data through reliable nodes and good quality links to the destination node. The efficacy of the proposed (RLTD) routing protocol is confirmed by comparing results with the well-known thermal-aware protocols for WBSN.
Brain Tumor Segmentation using Deep Learning
Authors: Sajid Muhammad , Yaseen Wajeeha , Khan Aman Ullah 
Published: January 2024

In addition to helping doctors discover and measure tumors, it also helps them develop better recovery and treatment plans. Recent MRI brain tumor segmentation algorithms have focused on U-Net design to combine high-level and low-level features for improved accuracy. Fully convolutional networks, which are also used for this purpose, are unable to successfully reconstruct the image through the decoder path because of the insufficient and low-level information from the encoder path. More effort needs to be done to optimise the low-level information flow from the encoder path to the decoder path in order to improve image reconstruction. In this study, we suggested a transfer learning residual U-Net model that combines the U-Net and VGG-16 architectures. To improve image reconstruction, VGG-16 is combined with the encoder. Additionally, a residual path in skipping connection is included to highlight key feature details while muting noisy and unnecessary feature replies. It is trained using The Cancer Imaging Achieve (TCIA) and Brats 2018 datasets, and It makes it easier to segment small brain tumors. When compared to previous brain tumor segmentation techniques, the suggested model performs competitively.
A Novel Method for Ranking a Website Rating via Search Engine Optimization
Authors: Raza Muhammad Ahsan , Raza Binish , Ashraf Mahmood , Raza Sehrish 
Published: January 2024

Everyone uses the internet and a search engine (SE) to find what they need, making SE indispensable. Search engines are used to find any sort of information, which is why most companies want to rank well in SE result pages in order to reach their ideal clients. Without a website, companies nowadays have little chance of competing in the industry. As a consequence, the companies have prioritized and funded efforts to improve their website SE rankings. There are a plethora of methods that may be used to optimize a website for search engines. These methods come under a heading, namely, search engine optimization (SEO) by webmasters. In this research, we propose a methodology that combines on-page and off-page SEO strategies to improve user search and website ranking over SE. The proposed method outperforms in terms of website ranking, whereby a website ranking is improved from fifth to second position on Google SE. This significant shift illustrates that on and off page SEO can work together to boost a website visibility over SE result page.
Mobility and Health Monitoring in People with Different Abilities: A Prototype Enhancing Independence: Innovating an IoT-Integrated Wheelchair for
Authors: Bano Zuhra , Qureshi Farwa , Ansar Moomal , Imdad Nimra , Shams Sarmad , Shamim Fahad 
Published: January 2024

Wheelchair is an essential tool for people with disabilities, enabling them to move around independently and participate fully in society. They come in different types, such as manual wheelchairs, power wheelchairs, sports wheelchairs, and pediatric wheelchairs among others. Certain types of disabilities such as Monoplegia, Hemiplegia, Paraplegia, and Quadriplegia pose difficulties in using conventional power wheelchairs. To overcome these hurdles and provide ease to differently-abled individuals, an Advance Monitoring and Assistive Wheelchair (AMAW) is proposed in this work. The Prototype includes a voice-controlled system for controlling the movement of a wheelchair, an IoT-based real-time health monitoring system to monitor the vitals of the patient remotely, a fall detection system for detecting falls, a tracking system for position and location, and an alarm system to alert caretaker in case of a fall. The real-time embedded monitoring system allows the monitoring of the user’s vital signs like temperature, pulse rate and oxygen saturation and the assistive part allows the wheelchair to move around electronically either through voice or through mobile application. With the assistance of various sensors, the data can easily be monitored remotely by the caretaker at regular intervals of the time. The data display on the LCD fitted onto the wheelchair and in the designed mobile application. Furthermore, the whereabouts of the user are sent via the alert system that notifies the caretaker through GSM in case of changes in parameters and if the user has lost the balance. The vitals through the sensors on the prototype has undergone testing on number of individuals with precise outcomes. In comparison to typical joystick-controlled wheelchairs, this project excels in several aspects, such as its ability to stop or turn using voice commands and avoid collisions with people, furniture, fixed objects, and walls. The user friendly AMAW prototype with real-time monitoring, assistance and alert system may serve as a cost-effective solution in maintaining and providing an independent quality life to differently-abled individuals.
Optimizing the Eﬃciency of Web Mining through Comparative Web Ranking Algorithms
Authors: Khalil Nida , Rehan Saniah , Syed Abeer Javed , Mahboob Khalid , Ali Fayyaz , Waseem Fatima 
Published: January 2024

Millions of web pages carrying massive amounts of data make up the World Wide Web. Real-time data has been generated on a wide scale on the websites. However, not every piece of data is relevant to the user. While scouring the web for information, a user may come upon a web page that contains irrelevant or incomplete information. As a response, search engines can alleviate this issue by displaying the most relevant pages. Two web page ranking algorithms are proposed in this study along with the Dijkstra algorithm; the PageRank algorithm and the Weighted PageRank algorithm. The algorithms are used to evaluate a web page's importance or relevancy within a network, such as the Internet. PageRank evaluates a page's value based on the quantity and quality of links leading to it. It is commonly utilized by nearly all search engines around the world to rank web pages in order of relevance. This algorithm is used by Google, the most widespread Internet search engine. In the process of Web mining, page rank is quite weighty. The most important component of marketing is online use mining, which investigates how people browse and operate a business on a company's website. The study presents two proposed models that try to optimize web links and improve search engine results relevancy for users.
Clustering Approaches for Efficient Radio Resource Management in Heterogeneous Networks
Authors: Farhan Naureen , Rizvi Safdar , Shabbir Amna , Memon Imran 
Published: January 2024

5G telecommunication industry promises to manage and accomplish the massive data traffic and growing network requirement complexities in heterogeneous networks (HetNets). HetNets are K-tier networks and are expected to be seamlessly connected networks with robust services for users anywhere at any time. In near future, the significance of 5G/B5G cellular networks; in both indoor and outdoor environments will be greater than before and it would add up to an exhaustive level. However, as a result of the increased density of networks, a rise in interference within these ultra-dense networks (UDN) will have an alarming impact on throughput, interference and latency. To ensure high throughput with reduced interference in UDNs a clustered architecture is required. A HetNet with clustered approach enables the network to mitigate interference effectively and achieve efficient radio resource management (RRM). In this paper, we analyzed different clustering classifications and existing clustering techniques that are used for proficient radio resource management. The centralized clustering techniques and decentralized clustering techniques are analyzed and as a result, it is assumed that improved performance can be achieved by emphasizing on hybrid clustering approaches. In addition to this, performed a thoughtful review of existing hybrid clustering techniques to achieve improved throughput and mitigate interference in dense heterogeneous networks. Our analysis shows that improved radio resource management and increased throughput in HetNets is achieved by applying hybrid clustering techniques with reduced inter and intra tier interference.
Augmented Reality in Healthcare Education for Human Anatomy
Authors: Zafar Adeel , Farooq Muhammad Shoaib 
Published: January 2024

The study of structures in the human body is known as human anatomy. Medical students face a lot of difficulties while understanding the core concepts of Human Anatomy. To learn anatomy, Augmented Reality (AR) offers an interactive learning experience by visually constructing 3D objects in the user’s world. AR has transformed healthcare education by supporting interactive learning in a way that helps the students and other learners develop new skills by understanding different concepts in a fun and an interactive way rather than the old traditional methods. Various studies show how Augmented Reality has helped different sectors such as Healthcare, Education, Gaming Industry etc. in improving their overall standards by providing a holistic view about different topics. To acknowledge the positive impact of AR in healthcare education in Human Anatomy, a Systematic Literature Review (SLR) is presented in this article. For this article, 34 papers were chosen through a comprehensive procedure. As per our best knowledge, no SLR is available that gives us an extensive review in this field of study. In this paper, we have presented different AR Systems and MAR applications to understand human anatomy in an interactive manner. A taxonomy diagram of the used AR techniques to learn human anatomy has also been presented. Different human organs and body parts taught through AR to help healthcare professionals and students have also been identified in this paper.
A Survey on Fog Computing in IoT
Authors: Rasheed Mansoor Ahmad , Saleem Jabar , Murtaza Hudabia , Tanweer Hafiz Abdullah , Rasheed Mannan Ahmad , Ahmed Mishaal 
Published: January 2024

Technological advancements in computing and wireless handheld devices have increased tremendously the rate of connected devices to the internet. To solve problems of our daily life are being controlled by these devices that created a new approach called as Internet of things. Cloud computing is a promising solution to store data and perform computations for IoT. But it has many issues like latency, traffic congestion, and poor quality of services. Health-related IoT applications are intolerant to delay or poor quality of service. To tackle these issues a new model of computing known as fog computing came into existence. Fog computing helps for effective communication and processing of the massive data that tackles issues caused by cloud computing in a significant way. This study discusses different types of fog computing architectures, some of the issues related to security and privacy along with their possible solutions. In this survey, some practical applications of fog computing in IoT are discussed which can ease our daily problems.
Systematic performance, and Security evaluation of .NET models for accessing database
Authors: Ullah Atta , Usman Muhammad , Abrar Muhammad F. , Ullah Najeeb , Shah Ibrar A. , Nadeem Muhammad F. 
Published: January 2024

In .NET, Object Relational Mapping (ORM) is a programming technique used for accessing the database, which has many frameworks, like Entity Framework, LINQ to SQL, NHibernate, Tele rick Open Access, Light Speed. The LINQ to SQL and Entity Framework usability has increased. This is because of the reason that in these two frameworks full CRUD (Create, Read, Update and Delete) operations can be implemented in short time as compared to Transact Queries, which require more time. In case of multiple projects on various models; Transact Query, LINQ to SQL, and Entity Framework, it becomes difficult to decide which model is the best in terms of performance and security. Therefore, in this article, we provide a comprehensive comparison between Entity Framework, LINQ to SQL and Transact Queries in terms of performance and security. For this purpose, we implemented eleven different types of queries on the selected three frameworks. Subsequently, we quantified and evaluated the execution time and memory usage of all the queries. Furthermore, all types of SQL injection attacks have been applied on three separate applications for security evaluation. Our results show that, the Transact Query is more vulnerable to SQL injection attacks as compared to LINQ to SQL and Entity Framework. Our results show that Transact Query outperforms in terms of memory and CPU usage. Our results also help the practitioner in adopting a framework on the basis of query level performance in terms of memory and CPU usage.
Software Testing Education: A Systematic Literature Review
Authors: Farooq Muhammad Shoaib , Tehseen Rabia , Omer Uzma , Riaz Shamyla , Tahir Saba 
Published: January 2024

Software Testing is the core part of computer science & engineering curriculum. It has been observed that software testing has been taught much in Computer science & engineering disciplines at undergraduate level. Software Testing Education (STE) involves time, cost, risk, quality, integration, communication, human resource, and procurement management skills. The STE at undergraduate and graduate level is a challenging task because it also requires knowledge and experience. This article aims to investigate and synthesize the state-of-the-art research in STE for the improvement of STE curriculum, pedagogical tools and techniques, cognitive, empirical and assessments methods. STE research approaches has been categorized into five categories including empirical type, research approaches, software testing education processes, key areas, and curricula. The ninety-seven articles for the area of STE have been chosen after rigorous systematic screening process published in during 2004 to 2021. Furthermore, tools and techniques, testing processes, pedagogy and student performance are the frequently addressed in STE; whereas assessment methods, gamification, curriculum and exemplary program development appeared as mostly ignored areas. Lastly, research gaps and challenges relate to STE has been presented as future directions for the faculty, software industry, and researchers.
O Programa Mais Vida nos Morros e a Relação das Crianças com o Espaço Público: o Caso da Vila Burity (Recife, PE)
Authors: Nunes de Santana Poliana , Carvalho Benício de Mello Sérgio , Valença Sandro 
Published: January 2024

Objetivo: Avaliar como o programa Mais Vida nos Morros, implementado na Vila Burity, no bairro da Macaxeira, na Zona Norte do Recife (PE), transformou a relação das crianças com o espaço público urbano em que estão inseridas.
Metodologia: Realizou-se um estudo de caso naturalista, com abordagem qualitativa e descritiva. Dados e informações foram coletados através de levantamentos documentais, entrevistas semiestruturadas e observações não participantes. A interpretação e análise se deu pela agregação categorial e análise qualitativa básica, de modo que os achados foram codificados e classificados em três categorias: “processo de intervenção”, “participação infantil” e “consequências da intervenção”.
Resultados: Observou-se que o programa realizou intervenções pautadas nos princípios de urbanismo tático, criando, por exemplo, áreas de lazer e convivência direcionadas às crianças, o que fez com que elas se reapropriassem dos espaços públicos, atribuindo vitalidade às ruas e calçadas e, consequentemente, aumentando a sensação de segurança. Não obstante, os espaços de participação infantil criados foram temporários, de forma que, decorrido algum tempo, as crianças voltaram à invisibilidade, não tendo a oportunidade real de exercer seus direitos, com destaque ao direito à cidade.
Originalidade: Constatou-se que o programa, a despeito de sua proposta ambiciosa, alcançou resultados aquém dos prometidos — e almejados — pelo poder público na Vila Burity, o que não significa que as iniciativas tenham sido um desperdício de esforços, mas implica dizer que elas deveriam ser regularizadas e reforçadas, numa rotina de trabalho contínuo.
Contribuições teóricas e práticas: Contribuiu-se, aqui, com os estudos nas áreas da sociologia da infância, de políticas públicas e de planejamento urbano. Ademais, teceram-se sugestões de melhorias e de ações, visando subsidiar o aperfeiçoamento desta e de outras políticas públicas semelhantes.
Capacidades relacionais em alianças sociais: similaridades e diferenças em multicasos
Authors: Salvador Alves Fernanda , Vieira Cruz Kumasaka Julia Mitsue 
Published: January 2024

Objetivo da pesquisa: O objetivo da presente pesquisa foi analisar distinções e similaridades no desenvolvimento das Capacidades Relacionais em diferentes tipos de alianças sociais brasileiras.
Enquadramento teórico: Para tanto, utilizou-se a base teórica das Capacidades Dinâmicas, que englobam as Capacidades Relacionais.
Metodologia: Para a consecução desta pesquisa, um estudo de multicasos, qualitativo e descritivo foi realizado a partir da perspectiva das duas organizações envolvidas nas quatro alianças sociais estudadas, por meio de entrevistas semiestruturadas e análise documental.
Resultados: Notou-se que o conhecimento é a principal tecnologia transferida entre os parceiros e que essa transferência é uma das recompensas mais relevantes para as organizações. Além disso, apenas as alianças com firmas são formalizadas por meio de contratos, pois estas parceiras percebem sua importância para o gerenciamento da aliança. Os casos também apontam que a confiança e a comunicação eficiente podem ser os aspectos mais importantes para o desempenho da aliança.
Originalidade: Os estudos acerca de Capacidades Relacionais ainda são escassos na literatura, assim como o estudo de Capacidades Relacionais em Alianças Sociais. Ademais, as alianças sociais possuem um papel essencial na resolução de problemas sociais complexos.
Contribuições teóricas e práticas: Os achados desta pesquisa permitem avançar nos estudos relacionados a Capacidades Dinâmicas e Relacionais em Alianças e Cooperações, principalmente as Sociais. Ainda, os parceiros das Alianças Sociais podem identificar componentes e dimensões das Capacidades Relacionais que devem ser incentivados em suas parcerias para garantir seu sucesso e proficuidade.