bluuebunny/crossref_metadata_2025 huggingface dataset:
Advancing cryptographic security: a novel hybrid AES-RSA model with byte-level tokenization
Authors: Durge Renuka Shone , Deshmukh Vaishali M.
Published: June 2024

As cyberattacks are getting more complex and sophisticated, stringent, multi-layered security measures are required. Existing approaches often rely on tokenization or encryption algorithms, both of which have drawbacks. Previous attempts to ensure data security have primarily focused on tokenization techniques or complex encryption algorithms. While these methods work well on their own, they have proven vulnerable to sophisticated cyberattacks. This research presents new ways to improve data security in digital storage and communication systems. We solve data security issues by proposing a multi-level encryption strategy that combines double encryption technology along with tokenization. The first step in the procedure is a byte-level byte-pair encoding (BPE) tokenizer, which tokenizes the input data and adds a layer of protection to make it unreadable. After tokenization, data is encrypted using Rivest–Shamir–Adleman (RSA) to create a strong initial level of security. To further enhance security, data encrypted with RSA has an additional layer of encryption applied using the advanced encryption standard (AES) method. This article describes how this approach is implemented in practice and shows how it is effective in protecting data at a higher level than single-layer encryption or tokenization systems.
A hybrid approach using convolutional neural networks and genetic algorithm to improve of sensing brain tumor prediction
Authors: Ettakifi Hamza , Tkatek Said
Published: June 2024

Brain tumor is the most constantly diagnosed cancer, and the opinion of the brain is veritably sensitive and complex, which is the subject of numerous studies and inquiries. In computer vision, deep literacy ways, similar as the convolutional neural network (CNN), are employed due to their bracket capabilities using learned point styles and their capability to work with complex images. still, their performance is largely dependent on the network structure and the named optimization system for tuning the network parameters. In this paper, we present new yet effective styles for training convolutional neural networks. The maturity of current state-of-the-art literacy styles for convolutional neural networks are grounded on grade descent. In discrepancy to traditional convolutional neural network training styles, we propose an enhancement by incorporating the inheritable algorithm for brain tumor prediction. Our work involves designing a convolutional neural network model to grease the bracket process, training the model using different optimizers (Adam and the inheritable algorithm), and assessing the model through colorful trials on the brain magnetic resonance imaging (MRI) dataset. We demonstrate that the convolutional neural network model trained using the inheritable algorithm performs as well as the Adam optimizer, achieving a bracket delicacy of 99.5.
Entities recommendations using contextual information
Authors: Saidi Imène , Mahammed Nadir , Klouche Badia , Khayra Bencherif
Published: June 2024

Generating entities recommendations has attracted considerable interest in recent years. Most recently published works mainly focus on providing a user with the most relevant and/or personalized entity recommendations that score highly against the query and/or the user’s preference. Some works consider user side information, such as the user network, user relations, and user’s demographic information, and propose to integrate them into the framework of recommender systems. These approaches have been shown to increase the users’ satisfaction and engagement with the system. In this paper, we investigate entities recommender systems and summarize the recent efforts in this domain by categorizing approaches. The first category presents different approaches that utilize knowledge graph as side information. The second category gathers work that consider both the current query, and the users’ previous interactions with the system. These latter works have considered the full user history to personalize the ranking of recommended entities related to the query. In this review paper, we emphasize contextual information-based approaches that utilize user’s context and feedback to improve the recommendations. We accomplished a summary of the literature and synthesized the papers according to different perceptions. Finally, a comparison between approaches is provided and some drawbacks are identified.
Internet behavioral models for improving internet quality of service or user profiling: a systematic literature review
Authors: Lei Zhang , Kamal Bashah Nor Shahniza
Published: June 2024

Internet behavior models have found applications across diverse domains, notably in internet addiction, customer satisfaction analysis, user purchasing behavior prediction, and optimizing internet of things (IoT) sensor performance. However, a notable gap exists in exploring these models in enhancing internet quality of service (QoS), specifically in campus settings, intricately linked to the nuances of students' online behavior. This study elucidates the strategic utilization of internet behavioral models for augmenting internet QoS and facilitating user behavior analysis. Creating datasets grounded in internet users' access behavior represents a pivotal phase, with explicit, implicit, and mixed methods emerging as the prevailing approaches. In this comprehensive literature review, we systematically scrutinized the methods, techniques, and inherent characteristics of constructing internet behavior models according to a systematic literature review process. The qualitative findings extracted from the systematic review encapsulated 1,046 articles, meticulously classified according to predefined inclusion and exclusion criteria. Subsequently, 35 articles were judiciously selected for in-depth analysis. This study culminated in identifying the most pertinent methodologies and salient features pivotal to construct robust internet behavior model for improving internet QoS and user experience.
Hospital quality classification based on quality indicator data during the COVID-19 pandemic
Authors: Nurhaida Ida , Dhamanti Inge , Ayumi Vina , Yakub Fitri , Tjahjono Benny
Published: June 2024

This research aim is to propose a machine learning approach to automatically evaluate or categories hospital quality status using quality indicator data. This research was divided into six stages: data collection, pre-processing, feature engineering, data training, data testing, and evaluation. In 2020, we collected 5,542 data values for quality indicators from 658 Indonesian hospitals. However, we analyzed data from only 275 hospitals due to inadequate submission. We employed methods of machine learning such as decision tree (DT), gaussian naïve Bayes (GNB), logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), linear discriminant analysis (LDA) and neural network (NN) for research archive purposes. Logistic regression achieved a 70% accuracy rate, SVM a 68% accuracy rate, and neural network a 59.34% of accuracy. Moreover, K-nearest neighbors achieved a 54% of accuracy and decision tree a 41% accuracy. Gaussian-NB achieved a 32% accuracy rate. The linear discriminant analysis achieved the highest accuracy with 71%. It can be concluded that linear discriminant analysis is the algorithm suitable for hospital quality data in this research.
Improving the efficiency of food supplies for a trading company based on an artificial neural network
Authors: Bisenovna Kassekeyeva Aislu , Arman Ashatuly Sadvakassov , Zhanar Beibutovna Lamasheva , Yesilbayuly Kerimkhulle Seyit , Zagievna Abdrakhmanova Alfiya , Galymbekovna Makpal Zhartybayeva , Oralkhanuly Oralkhanov Berdibek
Published: June 2024

This article presents the proper organization of the supply chain to meet consumer demand, which is crucial for modern commercial enterprises involved in the sale of various products. Studies indicate that a company's success is linked to the satisfaction of its customers. To optimize the supply chain, this study will consider the use of artificial neural network models. The results of this model will seek a balance between demand and supply, helping determine the necessary quantity of goods to satisfy demand and prevent overproduction. By using this model, the company can fully meet the needs of its customers. Additionally, the company saves its resources and labor costs and reallocates them to other tasks. The model demonstrates the optimization of production and supply business processes, as well as an increase in efficiency.
Privacy-preserving reservation model for public facilities based on public Blockchain
Authors: Basuki Akbari Indra , Rosiyadi Didi , Susanto Hadi , Setiawan Iwan , Salim Taufik Ibnu
Published: June 2024

Ensuring fairness in the utilization of government-funded public facilities, such as co-working spaces, sports fields, and meeting rooms, is imperative to accommodate all citizens. However, meeting these requirements poses a significant challenge due to the high costs associated with maintaining digital infrastructure, employee wages, and cybersecurity expenses. Fortunately, Blockchain smart contracts present an economical and secure solution for managing digital infrastructure. They offer a pay-per-transaction schema, immutable transaction records, and role-based data updates. Despite these advantages, public blockchains raise concerns about data privacy since records are publicly readable. To address this issue, this study proposes a privacy-preserving mechanism for public facilities' reservation systems. The approach involves encrypting the reservation table with fully-homomorphic encryption (FHE). By employing FHE with binary masking and polynomial evaluation, the reservation table can be updated without decrypting the data. Consequently, citizens can discreetly book facilities without revealing their identities and eliminating the risk of overlapping schedules. The proposed system allows anyone to verify reservations without disclosing requested data and table contents. Moreover, the system operates autonomously without the need for human administration, ensuring enhanced user privacy.
Efficient offloading and task scheduling in internet of thingth-cloud-fog environment
Authors: Gamal Marwa , Awad Samar , Abdel-Kader Rehab F. , Abd Elsalam  Khaled
Published: June 2024

Efficient offloading and scientific task scheduling are crucial for managing computational tasks in research environments. This involves determining the optimal location for executing a workflow task and allocating the task to computing resources to optimize performance. The challenge is to minimize completion time, energy consumption, and cost. This study proposes three methods: latency-centric offloading (LCO) for delay-sensitive applications; energy-based offloading (EBO) for energy-saving; and efficient offloading (EO) for balanced task distribution across tiers. Scheduling in this paper uses a genetic algorithm (GA) with a weighted sum objective function considering makespan, cost, and energy for IoT-fog-cloud. Comparative studies involving Montage, Cybershake, and epigenomics workflows indicate that LCO excels in terms of makespan and cost but ranks the lowest in energy. EBO excels in energy efficiency, aligning closely with the base method. EO competes effectively with the base method in terms of makespan and cost but consumes more energy. This research enables the selection of the most suitable method based on the type of application and its prioritization of makespan, energy, or cost.
Digital technologies evolution in swiftlet farming: a systematic literature review
Authors: Markom Arni Munira , Yusof Yusrina , Markom Marni Azira , Haris Hazlihan , Muhammad Ahmad Razif
Published: June 2024

The integration of cutting-edge technologies into swiftlet farming has greatly enhanced efficiency, productivity, and sustainability. The internet of things (IoT) provides farmers with up-to-date environmental data, enabling them to create and sustain ideal circumstances for swiftlets. Artificial intelligence (AI) enhances this process by analysing vast databases and providing farmers with well-informed choices to optimize yield. Biotechnology, by combining genetic selection and breeding programs, effectively connects with the IoT, enabling constant monitoring and control of the health and genetic traits of swiftlets. The integration of renewable energy technology seeks to diminish dependence on conventional energy sources, promoting sustainability. In this paper, a systematic review of the literature is examined the utilization of digital technology in the swiftlet farmhouse. The findings were classified into three main themes: smart monitoring and control systems, advanced bird detection techniques, and sustainable practices and innovative approaches, specifically in the manufacture of edible bird nest. This systematic literature review emphasizes the multidisciplinary nature of swiftlet farming's technological evolution, technology developers, challenges and recommendations that farmers and the industry face in their pursuit of sustainable growth.
Research of semantic aspects of the Kazakh language when translating into the Kazakh sign language
Authors: Nurgazina Dana , Kudubayeva Saule
Published: June 2024

The article discusses the semantic aspects of Kazakh sign language and its characteristics. Semantics, a field within linguistics, focuses on examining the meanings conveyed by expressions and combinations of signs. The author delves into the inquiry of the degree of similarity between verbal and sign languages, highlighting their fundamental distinctions. The primary objective of the research is to scrutinize the characteristics of parts of speech in the Kazakh language when expressed gesturally, along with the principles governing the translation of verbs and adverbial tenses. The article explains in detail the formulas for translating the text into sign language, based on the subject-object-predicate. Examples are given that illustrate the subject-object relationship and determine who acts as the speaker, "object" or "subject" of the utterance. It is necessary to note that for successful translation it is necessary first to understand the meaning of the sentence. The article concludes by emphasizing the importance of understanding both structural elements and contextual nuances in the fascinating world of the semantics of the Kazakh sign language. It inspires further research aimed at uncovering the complexities and exceptions that contribute to a deep understanding of linguistic nuances in this unique form of communication.
MSAPersonality: a modern standard Arabic dataset for personality recognition
Authors: Chraibi Khaoula , Chaker Ilham , Dhassi Younes , Zahi Azeddine
Published: June 2024

Automatic personality recognition is a task that attempts to automatically infer personality traits from a variety of data sources, including Text. Our words, whether spoken or written, reveal a lot about who we are. As people speak different languages, each with its own set of characteristics and level of complexity, identifying their personalities automatically might be language-dependent. This task requires an annotated text corpus with personality traits. However, the lack of corpora for languages other than English makes the task extremely challenging. We concentrated our efforts in this paper on the Arabic language in particular because it is understudied and lacks a corpus, despite being one of the most widely spoken languages in the world. Our primary goal was constructing our “MSAPersonality” dataset, which consists of 267 texts in modern standard Arabic that have been annotated with the Big Five personality traits. To evaluate the dataset and its potential for classification and regression, we used text preprocessing techniques, feature extraction, and machine learning algorithms. We obtained promising experimental results. Therefore, further research into predicting personality from Arabic text can be conducted.
Prediction of vulnerability severity using vulnerability description with natural language processing and deep learning
Authors: Ahmed Abdirahman Abdullahi , Osman Hashi Abdirahman , Romo Rodriguez Octavio Ernesto , Abdirahman Elmi Mohamed
Published: June 2024

One of the most critical aspects of a software piece is its vulnerabilities. Regardless of the years of experience, type of project, or the size of the team, it is impossible to avoid introducing vulnerabilities while developing or maintaining software. This aspect becomes crucial when the software is deployed in production or released to the final users. At that point finding vulnerabilities becomes a race between the developers and malicious intruders, whoever finds it first can either exploit it or fix it. Acknowledging this situation and using the tools and standards that we have available in the field, such as common vulnerability exposures and common vulnerability scoring systems, and based on modern researches, in this study, we propose to have an approach different from the common practices of manual classification, using a 2-layer convolutional neuronal network (CNN) to automatize the classification of vulnerabilities, speeding up this process and enabling developers to have a faster response towards vulnerabilities, producing safer software. The experimental results obtained in this study suggest that pre-trained word embeddings contributed to an increase in accuracy of approximately 2% and the overall accuracy become 0.816%.
The use of genetic algorithm and particle swarm optimization on tiered feature selection method in machine learning-based coronary heart disease diagnosis system
Authors: Wiharto Wiharto , Mufidah Yasmin , Salamah Umi , Suryani Esti , Setyawan Sigit
Published: June 2024

Coronary heart disease (CHD) is a leading global cause of death. Early detection is the right step to reduce mortality rates and treatment costs. Early detection can be developed using machine learning by utilizing patient medical record datasets. Unfortunately, this dataset has excessive features which can reduce machine learning performance. For this reason, it is necessary to reduce the number of redundant features and irrelevant data to improve machine learning performance. Therefore, this research proposes a tiered of feature selection model with genetic algorithm (GA) and particle swarm optimization (PSO) to improve the performance of the diagnosis model. The feature selection model is evaluated using parameters derived from the confusion matrix and using the CatBoost machine learning algorithm. Model testing uses z-Alizadeh Sani, Cleveland, Statlog, and Hungarian datasets. The best results for this model were obtained on the z-Alizadeh Sani dataset with 6 selected features from 54 features and the resulting performance for accuracy parameters was 99.32%, specificity 98.57%, sensitivity 100.00%, area under the curve (AUC) 99.28%, and F1-Score 99.37%. The proposed feature selection model is able to provide machine learning performance in the very good category. The diagnostic model proposed is of excellent standard.
Hybridization of the Q-learning and honey bee foraging algorithms for load balancing in cloud environments
Authors: Adewale Adeyinka Ajao , Obiazi Oghorchukwuyem , Okokpujie Kennedy , Koto Omiloli
Published: June 2024

Load balancing (LB) is very critical in cloud computing because it keeps nodes from being overloading while others are idle or underutilized. Maintaining the quality of service (QoS) characteristics like response time, throughput, cost, makespan, resource utilization, and runtime is difficult in cloud computing due to load balancing. A robust resource allocation strategy contributes to the end user receiving high-quality cloud computing services. An effective LB strategy should improve and deliver required user satisfaction by efficiently using the resources of virtual machines (VM). The Q-learning method and the honey bee foraging load balancing algorithm were combined in this study. This hybrid combination of a load balancing algorithm and a machine learning method has reduced the runtime of load balancing activities and makespan, and increased task throughput in a cloud computing environment thereby enhancing routing activities. It achieved this by continuously tracking the usage histories of the VMs and altering the usage matrix to send jobs to the VMs with the best usage histories.
A two-stage approach for aircraft detection with convolutional neural network
Authors: Toghuj Wael , Alraba'nah Yousef
Published: June 2024

Over the past few years, object detection has experienced remarkable advancements, primarily attributable to significant progress in deep learning architectures. Nonetheless, the task of identifying aircraft targets within remote sensing images remains a challenging and actively explored area. Presently, there are two main approaches employed for this task: one utilizing convolutional neural network (CNN) techniques and the other relying on conventional methods. In this work, a CNN based architecture is proposed to recognize aircraft types using remote sensing images. The experiments performed on multi-type aircraft remote sensing images (MTARSI) dataset show that the proposed architecture achieves 97.07%, 94.81%, and 94.44% accuracy rates for training, validation and testing sets. The results approve that, the architecture outperforms state of the art models.
Deep learning based multi disease classification of plant leaves using light weight residual architecture
Authors: Sadhasivam Muniyandi , Geetha Manoharan Kalaiselvi , Maria Britto James Gladson
Published: June 2024

Plant diseases can severely impact crop yields, posing a major risk to worldwide food stability. Prompt and precise identification of these diseases is crucial for early intervention and efficient crop administration. This paper introduces an innovative method for detecting plant leaf diseases using residual networks (ResNets) and the PlantVillage dataset. To develop light weight residual (LWR) architecture, five convolutional layers are interleaved with five max-pooling layers, making up the architecture of ten layers. The number of filters in the convolutional layers is gradually increased from 32 to 64 and up to 512 with a 3×3 kernel. A fully connected layer is the last layer of the network which provides the classification of leaf diseases The LWR architecture is trained and evaluated using the PlantVillage dataset, a broad collection of annotated images. This dataset serves as the basis for the system. The findings of the experiments provide evidence that the suggested system has higher accuracy, sensitivity, and specificity measures. The use of residual networks in LWR architecture improves the capability of the model to acquire complicated representations, which in turn enables a more precise differentiation between healthy and unhealthy plant leaves.
Email subjects generation with large language models: GPT-3.5, PaLM 2, and BERT
Authors: Loukili Soumaya , Fennan Abdelhadi , Elaachak Lotfi
Published: June 2024

In order to enhance marketing efforts and improve the performance of marketing campaigns, the effectiveness of language generation models needs to be evaluated. This study examines the performance of large language models (LLMs), namely GPT-3.5, PaLM 2, and bidirectional encoder representations from transformers (BERT), in generating email subjects for advertising campaigns. By comparing their results, the authors evaluate the efficacy of these models in enhancing marketing efforts. The objective is to explore how LLMs contribute to creating compelling email subject lines and improving opening rates and campaign performance, which gives us an insight into the impact of these models in digital marketing. In this paper, the authors first go over the different types of language models and the differences between them, before giving an overview of the most popular ones that will be used in the study, such as GPT-3.5, PaLM 2, and BERT. This study assesses the relevance, engagement, and uniqueness of GPT-3.5, PaLM 2, and BERT by training and fine-tuning them on marketing texts. The findings provide insights into the major positive impact of artificial intelligence (AI) on digital marketing, enabling informed decision-making for AI-driven email marketing strategies.
Mean makespan task scheduling approach for the edge computing environment
Authors: Saini Nisha , Kumar Jitender
Published: June 2024

Task scheduling in the edge computing environment poses significant challenges due to its inherent NP-hard nature. Several researchers concentrated on minimizing simple makespan, disregarding the reduction of the mean time to complete all tasks, resulting in uneven distributions of mean completion times. To address this issue, this study proposes a novel mean makespan task scheduling strategy (MMTSS) to minimize simple and mean makespan. MMTSS optimizes the utilization of virtual machine capacity and uses the mean makespan optimization to minimize the processing time of tasks. In addition, it reduces imbalance by evenly distributing tasks among virtual machines, which makes it easier to schedule batches subsequently. Using genetic algorithm optimization, MMTSS effectively lowers processing time and mean makespan, offering a viable approach for effective task scheduling in the edge computing environment. The simulation results, obtained using cloudlets ranging from 500 to 2000, explicitly demonstrate the improved performance of our approach in terms of both simple and mean makespan metrics.
Socio-technical factors influencing big data analytics adoption in healthcare
Authors: Wolseley Nik Nurdini , Salahuddin Lizawati , Mohd Aboobaider Burhanuddin , Raja Ikram Raja Rina , Hashim Ummi Rabaah , Abdul Rahim Fiza
Published: June 2024

The purpose of this study is to determine the key socio-technical factors influencing big data analytics adoption in healthcare services. A systematic literature review was conducted using peer-reviewed scholarly publications spanning from 2013 to 2023 to illuminate the influencing factors. Twelve papers focused on the factors influencing big data analytics (BDA) adoption in healthcare services were included for review. The factors were divided into four major groups namely i) person, ii) technology, iii) organization, and iv) environment. Analytical skills define a person, whereas technology is characterized by system quality and information quality. Organization support, organization resources, training, data governance, and evidence-based decision-making are all associated with the organization. Finally, government regulations are allocated to the environment. This review presents evidence of the socio-technical factors that influence big data analytics adoption in healthcare services. The findings from this review recommend future big data analytics adoption in healthcare services to carefully evaluate the factors identified in this study.
Personalized diabetes diagnosis using machine learning and electronic health records
Authors: S. Gowthami , Siva Reddy R. Venkata , Ahmed Mohammed Riyaz
Published: June 2024

Diabetes mellitus (DM) poses a significant health challenge globally, necessitating accurate and timely diagnosis for effective management. Conventional diagnostic methods often struggle to address the multifaceted nature of diabetes and the requisite lifestyle adjustments. In this study, we propose a data-driven approach utilizing machine learning techniques to enhance diabetes diagnosis. By leveraging extensive patient attributes and medical records, machine learning algorithms can uncover intricate patterns and correlations. Our methodology, validated on the PIMA India dataset, demonstrates promising results. The random forest model achieved the highest accuracy of 87%, followed closely by gradient boost at 90%. Notably, XGBoost and CATBoost models attained a peak accuracy of 90.9%. These findings underscore the potential of machine learning in transforming diabetes diagnosis. Beyond improving diagnostic accuracy, our approach aims to guide individuals towards healthier lifestyles. Intelligent systems driven by machine learning hold promise for revolutionizing diabetes management, ultimately leading to better patient outcomes and more effective health care delivery.
AMSVT: audio Mel-spectrogram vision transformer for spoken Arabic digit recognition
Authors: Mahmoudi Omayma , El Allali Naoufal , Bouami Mouncef Filali
Published: June 2024

This work presents a novel model to recognize spoken digits in the Arabic language. Due to the transformer-based models' tremendous success in natural language processing (NLP), several attempts have been made to extend transformer-based designs to other domains, such as vision and audio. However, our approach consists of extracting and inputting Mel-spectrogram features into our model of the proposed audio Mel-spectrogram vision transformer (AMSVT) for training. The signal processing community has been interested in these models due to the successful use of vision transformers (ViT) in several computer vision applications. This is because signals are frequently recorded as spectrograms (using the Mel-spectrogram, for example), which may be given directly as input to vision transformers. Our model outperformed a group of models in terms of accuracy and time, such as convolutional neural network (CNN)-based and recurrent neural network (RNN)-based.
Improved moth search algorithm with mutation operator for numerical optimization problems
Authors: Ghaleb Sanaa A. A. , Mohamad Mumtazimah , Mohammed Ghanem Waheed Ali Hussein , Alhadi Arifah Che , Nasser Abdullah B. , Aldowah Hanan
Published: June 2024

The moth search algorithm (MSA) is a meta-heuristic optimization technique inspired by moth behavior, has shown remarkable efficacy in solving optimization challenges. However, its poor exploration capability results in an imbalance between exploitation and exploration. To address this issue, this research introduces a new mutation operator to enhance exploration by increasing population diversity. The proposed enhanced moth search algorithm (EMSA) aims to expedite convergence and improve overall robustness by exploring new solutions more effectively. Evaluation on ten benchmark functions demonstrates EMSA's superior exploration capabilities, efficiently tackling optimization problems and yielding more optimal solutions within the search space. Compared to conventional MSA and other established algorithms, EMSA delivers well-balanced results, showcasing its effectiveness in optimizing the search space. In the future, the EMSA could potentially find applications in addressing real-world engineering optimization challenges.
The impact of feature extraction techniques on the performance of text data classification models
Authors: Maiti Abdallah , Abarda Abdallah , Hanini Mohamed
Published: June 2024

Sentiment analysis is a crucial discipline that focuses on the interpretation of feelings and points of view in textual data. Our study aims to assess the impact of different feature extraction methods on the accuracy of opinion research models. Techniques such as bag-of-words (BoW), term frequency-inverse document frequency (TF-IDF), Word2Vec, global vectors (GloVe) and bidirectional encoder representations from transformers (BERT) were used with three machine learning algorithms and three deep learning networks as classifiers. The IMDB movie review dataset was used for evaluation. The results showed that combining BERT with LSTM, CNN and RNN improved performance, achieving an accuracy rate of 94%, precision of 94.14%, recall of 93.27% and an F1 score of 89.33%. These results highlight the significant contribution of ERTB to model performance, outperforming other feature extraction techniques in text classification. The study concludes that the fusion of BERT and LSTM significantly improves model accuracy for opinion retrieval, recommending BERT as the main feature extraction method for optimizing performance in NLP tasks.
A perspective on smart universities as being downsized smart cities: a technological view of internet of thing and big data
Authors: Abdul Jawwad Abdul Kareem , Turab Nidal , Al-Mahadin Ghayth , Owida Hamza Abu , Al-Nabulsi Jamal
Published: June 2024

The integration of internet of things (IoT) and big data technologies is transforming the overall perspective of managing various sectors of modern life; with higher educational sectors being no exception of this transformation. This paper explores the idea of a “smart university” as an extension of the overarching “smart city” framework, emphasizing the blending of IoT and big data technologies within higher education institutions. The study investigates the incorporation of IoT technologies throughout university campuses, including intelligent classrooms, smart infrastructure, and device networking. Moreover, the paper delves into the substantial role played by big data analytics in processing and extracting meaningful insights from extensive data generated by IoT devices in a Smart University. The use of predictive analytics, machine learning algorithms, and data-driven decision-making contributes to personalized learning experiences, adaptive campus management, and proactive maintenance of university facilities. Furthermore, this paper not only emphasizes the potential benefits of deploying IoT and big data in a university setting but also addresses challenges related to security, privacy, and ethical considerations. By embracing a comprehensive approach to technology integration, universities can leverage the capabilities of IoT and big data to establish intelligent, interconnected, and flexible educational environments that align with the broader vision of a smart city.
Effectiveness of VGG19 in deep learning for brain tumor detection
Authors: Arlis Syafri , Putra Muhammad Reza , Yanto Musli
Published: June 2024

Image processing in the diagnosis of disease is one of the jobs that is currently developing in the world of health. Diagnosis is carried out by utilizing the role of image processing to provide a level of accuracy in diagnosis results and provide efficiency to medical personnel. This research aims to develop a brain tumor object detection process using a deep learning (DL) approach to magnetic resonance images (MRI) images. This development was carried out to optimize the brain tumor diagnosis process by playing the role of the image extraction process. This research dataset was sourced from the M. Djamil Padang Provincial General Hospital with a total of 3370 MRI images. The results of this work report show that DL performance is capable of carrying out the detection process automatically with an accuracy level of 97,83%. The results of the development of the extraction process can work effectively in ensuring brain tumor objects are precise and accurate. Overall, this research can make a major contribution to maximizing the diagnosis process and assisting medical personnel in the early treatment of brain tumor patients.
Fog computing in classrooms: boosting efficiency, responsiveness, user experience
Authors: Hasanuddin Tasrif , Hadi Mokh Sholihul , Sujito Sujito , Rosnani Rosnani
Published: June 2024

In the context of rapidly advancing smart education systems, the effective management and optimization of modern classroom remain critical challenges. This research presents a novel methodology leveraging cloud and fog computing-based simulations, with a specific focus on the implementation of iFogSim. Empirical findings validate the efficacy of fog computing in monitoring classrooms, demonstrating significant improvements in performance metrics compared to traditional cloud computing architectures. Specifically, fog computing ensures remarkably low latency, with a mere 7 milliseconds, even with scalable integration across multiple classrooms. In contrast, cloud computing infrastructures exhibit considerably higher initial latencies, starting at 210 milliseconds, which further escalate with the increasing number of monitored classrooms. Furthermore, our analysis reveals substantially lower network overhead associated with fog computing, measuring at 5,231.8 kilobytes, in sharp contrast to the significantly higher network usage of 80,808 kilobytes observed with cloud computing solutions. These findings underscore the potential of fog computing as a promising solution for efficient and real-time management classroom in smart education environments.
Automated Alzheimer’s disease detection and classification based on optimized deep learning models using MRI
Authors: Saini Rashmi , Singh Suraj , Semwal Prabhakar
Published: June 2024

Alzheimer’s disease (AD) is a devastating neurologic condition characterized by brain atrophy and neuronal loss, posing a significant global health challenge. Early detection is paramount to impede its progression. This study aims to construct an optimized deep learning (DL) framework for early AD detection and classification using magnetic resonance images (MRI) scans. The classification task involves distinguishing between four AD stages: mild demented (MD), very mild demented (VmD), moderate demented (MoD), and non-demented (ND). To achieve effective classification, three DL models (VGG16, InceptionV3, and ResNet50) are implemented and fine-tuned. A systematic evaluation is conducted to optimize hyper-parameters, with extensive experimentation. The results demonstrate superior classification performance of the customized DL models compared to state-of-the-art methods. Specifically, visual geometry group 16 (VGG16) achieves the highest accuracy of 95.85%, followed by ResNet50 with 89.38%, while InceptionV3 yields the lowest accuracy of 87.23%. This study highlights the critical role of selecting appropriate DL models and customizing them for accurate AD detection and classification across various stages, offering significant insights for advancing clinical diagnosis and treatment strategies.
Islanded microgrid: hybrid energy resilience optimization
Authors: Gopu Veeranjaneyulu , Nagaraj Mudakapla Shadaksharappa
Published: June 2024

To maintain a dependable and sustainable power supply in a microgrid system, it is crucial to combine renewable energy sources with hybrid energy backup. To achieve maximum output power from solar source, a high gain DC-DC boost converter is managed using the dual Kalman filter based perturb and observe approach. A sliding mode current controller at a three phase inverter with an LC filter is intended to follow an undisturbed reference voltage. To effectively manage the power flow and optimize the utilization of available resources, a robust power management algorithm is required. The novel power management algorithm for a solo operated renewable distribution generation unit with hybrid energy backup in a microgrid is introduced. The algorithm aims to dynamically allocate power among various sources, storage systems, and loads, considering their characteristics and the overall system constraints. The algorithm utilizes sliding mode control techniques to regulate the current flow from the renewable generator and effectively manage the power allocation among different energy sources, storage systems, and loads.
PV system applied on the new PUC-NPC inverter
Authors: Amari Abdelhay Sadki , El Gadari Ayoub , Arbaoui Abdezzahid
Published: June 2024

In this article, the authors present a new competitive Packed-U-cell and neutral point clamped (PUC-NPC) 9-level converter that utilizes a small number of power switches and passive components. Only seven switches are added to a single DC source. However, the proposed converter takes benefits of the neutral point clamped (NPC) converter’s capability to connect capacitors in series, all with a simple control that enables the algorithm associated to the proposed inverter operate correctly in an open loop operation. A technique of pulse width modulation (PWM) is employed to maintain capacitor voltage at desired values. The proposed inverter produces an almost sinusoidal waveform output voltage, as result, a perfectly sinusoidal charge current with minimal impact on the economy and energy consumption are optioned. The converter has been integrated with a photovoltaic system to minimize its impact on the electricity supply and enhance energy efficiency. In order to take a maximum power from the photovoltaic panel, authors employ a maximum power point tracking (MPPT) technique based on disturbance and observe (P and O). When combined with the proposed control technique, it offers a competitive system suitable for standalone use or as an energy source, eliminating the need for PI regulators or additional investments. The proposed 9-level converter has been validated through simulation, utilizing the MATLAB Simulink environment to model the proposed system. Dynamics of this later were verified by changing the load charge.
The De Bruijn graph of non-sequential pattern repetitions in DNA strings
Authors: Fong Wan Heng , Ildrussi Ahmed , Yosman Ahmad Firdaus
Published: June 2024

In molecular biology, constructing a genome based on substantially many reads from multitudes of deoxyribonucleic acid (DNA) strings has become an insurmountable task; one which has been continuously addressed by the introduction of various assembly algorithms based on three steps called the overlap-layout-consensus strategy. In the overlap step, the De Bruijn graph is one of many graphs that illustrate the data of all the assembly algorithms. In this article, by using definitions and methods of mathematical induction, some properties of the De Bruijn graph of one time and two times non-sequential repetition of patterns in a DNA string are presented. Examples of these De Bruijn graphs are also given. From there, a generalisation of said properties for m times non-sequential pattern repetition in a DNA string is acquired by means of mathematical induction, as well. The theoretical work in this research is invaluable to develop algorithms that increase the computational efficiency of assembly algorithms.
On-chip based power estimation for CMOS VLSI circuits using support vector machine
Authors: Nagarajan Sridevi , Mahadeviah Prasanna Kumar
Published: June 2024

Power estimation has a major impact on the reliability of very-large-scale integration (VLSI) circuits. As a results power estimation is highly needed in VLSI circuits at the early stages. One of the evident challenges in integrated circuit (IC) industry is development and investigation of techniques for the reduction of design complexity due to the growing process variations and reduction of chip manufacturing turnaround time. Under these conditions, the higher design levels of average power estimation before the chip manufacturing process is highly essential for the calculation of power budget and to take the necessary steps for the reduction of power consumption. Over the years, most of the approaches were designed to estimate the power usage, however, most of the conventional techniques are time consuming, resource-intensive and largely manual. Machine learning techniques have received much attention in many of the engineering applications and are capable for modelling the complex systems through historical data. Hence, in this work on chip-based power estimation for complementary metal-oxide-semiconductor (CMOS) VLSI using support-vector machine (SVM) is presented to estimate the power. The SVM is employed to estimate the usage of power at runtime. The performance of this model is evaluated in terms of Power usage, delay, data accuracy and error rate.
Semi-decentralized Lyapunov-based formation control of multiple omnidirectional mobile robots
Authors: Agung Hendi Wicaksono , Jordan Fransisco
Published: June 2024

This paper introduces an advanced formation control algorithm based on a Lyapunov approach for coordinating multiple omnidirectional mobile robots in collaborative object transport tasks. The semi-decentralized strategy ensures that the robots maintain a predefined geometric formation, crucial for stability during material transportation, and dynamically adapt to avoid collisions using onboard sensors. Experimental with a physical robot simulator demonstrates successful maintenance of line and triangle formations achieving an average side length maintenance of 1.00 meters with minimal deviation. Quantitative analysis across 30 experimental runs reveals consistent performance, with a maximum side length fluctuation of only 2 centimeters, validating the effectiveness of maintaining formation within a multi-robot system (MRS) framework. The Lyapunov-based approach proves to be an efficient method for cooperative object transport, achieving consistent performance with minimal deviation.
Developing Bluetooth phonocardiogram for detecting heart murmurs using hybrid MFCC and LSTM
Authors: Wahyu Nugroho Dwi Oktavianto , Hikmah Nada Fitrieyatul , A’alimah Fathin Hanum , Oktavia Nabila Shafa , Dwi Winarsih Meitha Auliana , Elparani Sirsta Hayatu , Rifqi Hananto R. M. Tejo
Published: June 2024

Cardiovascular disease is a leading global cause of mortality. Most stethoscopes still necessitate the use of tubing, which entails direct physical contact between the healthcare provider and patient. The stethoscope can serve as a means of transmission if it is utilized on individuals who have been diagnosed with airborne and droplet-borne infectious illnesses. A prototype was created to capture heart sounds using a Phonocardiography (PCG) device over website-based Bluetooth connectivity. This approach offers the benefits of being cost-effective, facilitating computer-aided diagnostics, and being wearable. In addition, the primary significance of this study resides in the identification of heart sound irregularities caused by cardio dynamic abnormalities of the heart valves, known as murmurs. The heart sound categorization process utilizes a machine learning model that involves extracting 25 Mel frequency cepstral coefficients (MFCC) as features. The model employs a hybrid approach combining convolutional neural network and long short-term memory (CNN-LSTM) techniques. The research findings indicate that the suggested model achieves an average accuracy rate of 95.9% over five distinct categories, i.e., normal, atrial stenosis, mitral regurgitation, mitral stenosis, and mitral valves prolapse. Further study can be conducted on hardware development by incorporating an infrared sensor at the fingertip of the stethoscope.
Complexity analysis of the VVenC versus VVC encoder
Authors: Touzani Hajar , Errahimi Fatima , Mansouri Anass , Ahaitouf Ali
Published: June 2024

The joint video experts team (JVET) has recently finalized a next-generation open-source video codec, called versatile video coding (VVC). The new standard presents a higher gain in the run time and rate compressions. Based on the reference software (VTM, VVC Test Model) of VVC, an optimized encoder was developed these last years resulting in a fast and efficient encoder, called Fraunhofer versatile video encoder (VVenC). Based on the Bjontegaard methodology and the GPROF profiling tool, this paper presents a technical complexity analysis and comparison of both VVC and VVenC. The appropriate comparisons cover the percentage taken by each block in terms of processing time and the resulting whole encoding time. The peak signal-to-noise ratio (PSNR) and bit rate between VVC and VVenC encoders based on the common test conditions manual are also analyzed and compared. The profiling results show that the VVenC encoder presents a maximum gain of runtime and bit rate of 90% and 20% respectively in classes A and D test sequences, compared to the VVC encoder.