London Journal of Research In Computer Science and Technology
London Journal of Research in Computer Science and Technology London Journal of Research in Computer Science and Technology: A: Software Engineering & Computing Algorithms, is focused on publishing groundbreaking ideas in domains of computing and advances in technology. It provides practical, theoretical, and managerial implications useful for further research development. As a flagship scholarly journal, It is a peer-reviewed, international, indexed journal. The journal publishes high-quality research papers that are double-blind refereed. Authors and readers include academic researchers and educators, as well as engineering professionals. SUBMIT MANUSCRIPT
Self-Service Analytics 2.0: AI-Powered Dashboard Generation with Human-in-the Loop Feedback Architecture
This paper presents the architectural foundation and implementation results of Self-Service Analytics 2.0, an AI-powered system that automatically generates business dashboards from raw data while incorporating continuous human feedback loops. Our architecture integrates automated schema detection, intelligent KPI discovery, and adaptive visualization generation through a multi-layered feedback mechanism that learns from user interactions. The system demonstrates a 47% reduction in dashboard creation time and achieves 78% user satisfaction scores through iterative refinement. We detail the comprehensive architecture including feedback collection pipelines, model adaptation mechanisms, and human-in-the-loop quality assurance workflows that ensure generated insights remain aligned with business objectives.
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The Weakest Link in Internet Privacy: Security and Compliance Risks in Third-Party Vendor Data Handling
The new internet economy relies on third-party sellers, such as cloud computing service providers, SaaS and services, payment processing services, and marketing services. On the one hand, such sellers make scaling and innovativeness possible, and, on the other hand, such sellers endanger the safety of personal data and the sanctity of the law. This paper discusses the vulnerabilities inherent to vendor ecosystems using case studies of the Target and SolarWinds breaches to provide examples of the weaknesses present in systems. It also talks about the regulatory frameworks such as GDPR, CCPA, HIPAA, and PCI DSS, and outlines the impediments to implementation and lapses in responsibility. This empirical study proposal of the best internet company practices on vendor risk is provided to contribute to benchmarking in this under-researched field. Lastly, there are technical safeguards, organizational measures and policy recommendations, and finally a call to a global Vendor Privacy Assurance Standard. The results show that vendors are the least strong link in privacy protection, and that there is a need for concerted efforts across the industry, regulators, and academia.
The Relationship between Consciousness and Linguistic Data to Formalize
This study seeks to formalize the relationship between consciousness and linguistic data through a symmetry-based approach. It proceeds from the assumption that consciousness as a biological fact and linguistic structures-especially their formally describable patterns�are mirror images of one another. The structures of conscious linguistic composition are reflected in the organization of linguistic data, while formal linguistic patterns can be traced back to the inner, rhythmic, and symmetrical architecture of consciousness. To map this relationship, the study integrates the methodological toolkits of three coequal fields: computational linguistic encoding, rhythm-based phonology, and the historical investigation of linguistic melody. The computational linguistic encoding relies on a custom-developed system that classifies syllables within words by typology and position. At the core of the model stands a center�periphery principle: vowels constitute the center of the structure, encircled by concentrically arranged consonants. This symmetry-based approach enables precise formal annotation of the internal structure of syllables and sheds light on recurring patterns in phonological organization. The classification introduces 55 syllable types grounded in five basic structures (e.g., open, closed, reduced, etc.), augmented by positional labelling that records whether a syllable is word-initial, word-medial, or word-final.
Design of a Real-Time, Multilingual, Emotion-Aware Cyberbullying Detection System using Multi-Teacher Knowledge Distillation and Explainable AI
Social media cyberbullying has propagated rapidly and is being experienced by individuals worldwide. It tends to be expressed using sarcasm, emotional language, and multiple languages, making it difficult to determine the identity of the perpetrator. Although automated detection systems are becoming increasingly prevalent, the majority of existing systems suffer from language issues, function only in offline batch mode, and are black-box models that cannot be interpreted. These constraints make it more difficult to intervene with speed and transparency. This paper offers a real-time, multilingual system for detecting cyberbullying, using explainable AI, emotion and sarcasm detection, and Multi-Teacher Knowledge Distillation (MTKD) to address shortcomings. The system leverages an ensemble of transformer-based teacher models, like mBERT, XLM-R, and IndicBERT, to capture language-specific features. Then, the models collaborate to produce a lightweight XG Boost classifier. To assist with the interpretation of context, additional layers are incorporated to identify sarcasm and emotion. SHAP (SHapley Additive Explanations) is employed to provide each prediction token-level interpretability. Algorithmic and architectural design of a system that would form a transparent, efficient, and deployable solution to detect cyberbullying in different emotional and linguistic contexts is the focus of this study.
AI-based Sustainable Vehicle Monitoring System for Existing Internal Combustion Vehicles
The transportation industry is a major contributor to carbon� emissions, with internal combustion engines responsible for over� 25% of the total. Despite advances and regulations encouraging the� shift to electric vehicles, the transition from diesel engines remains� slow, as expected. Many countries have heavily relied on diesel� engines, which makes the switch to electric vehicles more difficult� due to the higher costs of buying and replacing internal combustion� engines with electric ones. Therefore, this report suggests a� solution: retrofitting existing ICE vehicles with AI-powered� sustainable vehicle monitoring systems. This upgrade involves� installing sensors that work with OBD-II diagnostics to monitor� emissions, fuel use, and driving habits in real time. Gathering this� data aims to develop personalized, eco-friendly driving� recommendations that help reduce overall emissions. This method� provides a cost-effective, sustainable, and environmentally friendly� alternative to high carbon emissions. It is also scalable, even in� regions with limited financial resources.
Federated Data Governance for Cross?Institution Anti?Money Laundering (AML) using Data Warehousing and AI
Federated data governance enables banking institutions to leverage collaborative capabilities, effectively combating money laundering activities while upholding compliance requirements and safeguarding information autonomy. The structural design merges secure information warehousing with cognitive systems, creating identification frameworks that maintain confidentiality across institutional boundaries. Utilizing federated learning principles, institutions uncover intricate laundering schemes typically concealed within segregated systems. The governance structure employs cryptographic safeguards, detailed permission hierarchies, and permanent verification records to protect information throughout collaborative engagements. Successful deployment addresses system interoperability, allocates processing capacity, and harmonizes data structures among participating organizations. Regulatory aspects include navigating jurisdictional requirements, transnational information exchange protocols, and coherence with global financial security standards. The article yields improvements in identification precision, surveillance capabilities, and notification accuracy when compared with conventional isolated approaches. Financial organizations implementing federated governance enhance compliance positions while sustaining operational autonomy. Through a balance between security imperatives and functional requirements, this architecture provides a comprehensive solution to coordinated anti-money laundering challenges within interconnected financial markets, laying the groundwork for productive collaboration against increasingly sophisticated financial offenses.
Sentiment Analysis of Computer Mediated Communication in Social using Natural Language Processing
The massive interactions on social mediaplat forms had created a luxury of computer-mediated communication (CMC) languages in recent times, especially on the X (formerly Twitter) Platform. Resources required in extracting and analyzing these enormous expressions whether for public perception, market trends, or social dynamics are incredibly huge and can also be complex to handle. The comparative investigation of the CMC based on the accuracy of the interpreted sentiments is expressed within the Google Natural Language Processing(NLP) API model. The results of experts� analysis with that of the Google NLP model using sizable data of CMC from X were compared. The X comments on the declaration of the state of emergency by the Nigeria President-Bola Ahmed Tinubu- in Rivers State on the 18th of March 2025 as posted by its handlers were the subjects of analysis. Identification and categorization of sentiment polarity whether positive, negative, or neutral were carried out by the model. Indices such as linguistic variations, context-dependent sentiment, sarcasm, and irony were used in order to understand the influence on the accuracy and reliability of sentiment analysis results of the tool. The outcome of this paper reveals the remarkable strengths and weaknesses of an Google NLP model in analyzing sentiment present in the CMC in social media platforms.
Self-Serving Data Marts Orchestrated by Auto ML-Governed Pipelines
Self-serving data marts orchestrated through Auto ML-governed pipelines mark a significant advancement in enterprise analytics democratization. This architectural approach creates domain-specific information repositories with automated data preparation, feature engineering, and model development functions accessible to business users without deep technical knowledge. The governance framework applies automated quality controls, lineage tracking, and access management, ensuring data integrity throughout analytical processes. Integration with existing data warehouse systems maintains centralized governance while enabling distributed analytical capabilities, addressing specific business needs. Implementation factors include metadata standardization, processing resource allocation, and organizational change management supporting effective usage. Technical elements comprise automated data profiling, dynamic transformation creation, and continuous quality monitoring throughout pipeline operation. The orchestration layer manages complex workflows while implementing appropriate error handling and recovery mechanisms. Enterprises implementing these frameworks report significant enhancements in analytical responsiveness, resource utilization effectiveness, and business coordination compared to conventional centralized models. This balanced approach resolves conflicting requirements between governance standardization and analytical adaptability, establishing durable foundations for growing self-service functions while preserving appropriate supervision across increasingly intricate data landscapes.
Advanced Robotic Multimodal Imaging with Real-Time Motion Compensation for Dynamic Structural Health Monitoring
Modern composite materials promise superior performance and load-bearing capabilities, yet evaluating their structural integrity remains challenging. Current testing methods, such as visual, thermographic, ultrasonic, optical, electromagnetic, terahertz, shearography, X-ray, and neutron imaging, are hampered by long scan durations, limited field of view, suboptimal accuracy, and high costs, particularly when applied to large structures. This paper addresses these issues by introducing a novel robotic multimodal imaging system that overcomes the limitations of traditional methods. This system dynamically captures both static and dynamic properties of materials using advanced motion compensation techniques. By integrating multiple radiographic modalities into a coordinated robotic platform, it provides rapid, high-resolution imaging of composite materials of large structures without the need for disassembly. The system was validated through simulations of a four-robot radiograph setup, treated as two single-plane systems. The 3D position and orientation of a cube phantom were determined by generating computer-based digitally reconstructed radiographs from a computed tomography model and applying a 3D line intersection method based on known imaging geometries. Comparisons between marker-based and markerless kinematics tracking methods yielded differences of only 0.03 mm in translation and 0.06� in rotation. These findings demonstrate that the proposed system significantly reduces scan times and enhances accuracy, offering a robust, scalable solution for dynamic inspection in diverse fields such as aerospace and medical device manufacturing.
Internet of Things and Global System for Mobile Communications
Robotics-driven Imaging; Motion Correction; Multimodal; Porosity Analysis; Materials testing
Computer-based metrology; Non-Destructive Testing
Smart Safety Solutions: Real-Time SMS Alerts for Gas Leakage and Fire Detection using IoT
Security issues are becoming more and more important in today's environment, necessitating increased attention to detail because of the rising dangers of infiltration and dangerous situations. Gas leaks and fires are among the most urgent hazards to residential and commercial safety as they may cause a devastating loss of life and property. Conventional alarm systems that just use sound signals frequently fail to guarantee prompt human reaction, particularly in situations where inhabitants are not present. Using Internet of Things (IoT) devices that are coupled with GSM (Global System for Mobile Communications) technology, this study suggests a real-time gas leak and fire detection system that sends timely SMS notifications. To identify anomalies, the system uses an Atmega644P microprocessor that is interfaced with gas and fire sensors, relays, and buzzers. Through the SIM548C GSM module, which notifies pre-designated emergency contacts via SMS in real time, the system instantly sounds an alarm in the event of a gas leak or fire. This rapid communication mechanism ensures faster response and mitigates potential damage. The increasing importance of intelligent, real-time safety systems in tackling contemporary security issues is highlighted by this review.
Digitizing Compliance: A Case Study in AMS-EDI Integration for Secure Global Shipping
The implementation of the Automated Manifest System (AMS) rule by the U.S. Customs and Border Protection agency following the September 11 attacks marked a pivotal transformation in global maritime security protocols. This transformative case documents the creation of an Electronic Data Interchange (EDI) compliance system that bridged regulatory requirements with operational efficiency. The initiative successfully deployed a middleware abstraction layer that processed thousands of daily cargo manifest submissions while validating critical data elements required by federal regulations. Developed by a geographically distributed team operating across multiple time zones, the system achieved exceptional first attempt acceptance rates while significantly reducing processing times and costs. The impementation pioneered human-centric design principles in regulatory technology, dramatically decreasing cognitive load and training requirements while increasing user satisfaction and adoption rates. Beyond meeting immediate compliance needs, the solution generated substantial recurring revenue, expanded market share, and catalyzed broader digital transformation initiatives across the maritime shipping industry. The case exemplifies how regulatory challenges, when approached through innovative architectural design and cross-border collaboration, can yield competitive advantages while establishing new standards for balancing security imperatives with commercial efficiency.
Enhancing Learning Through Game Flow: A Case Study for Epistemic Game “Working with Water"
Epistemic video games, interpreted as video games pursuing new knowledge through the mechanism that concatenates� practices and cognition, grab numerous attention on account of their extensive implementation since COVID-19. The� corresponding academic explorations into the players, especially indicating the students, have largely integrated in game� enjoyment and the principals exerted. Understanding the essential factors that promote game enjoyment that aligns with� educational purposes is crucially important for clarifying the mechanism of epistemic games. Consequently, the present� study adopts the Game Flow model presented by Sweetser and Wyeth (2005), whose accuracy has been verified and yields to evaluate enjoyment of video games by examining specific criteria under eight elements: concentration, challenge, player� skills, control, clear goals, feedback, immersion, and social interaction of the model. The highly acclaimed epistemic game� �Working with Water� is elected to conduct a case study by implementing the Game Flow model. The result indicates that� the overall values for Game Flow cohere with the performance, usability, and potentiality of the epistemic games. The� model performs well in the evaluation of epistemic games to a large extent. but simultaneously limited to the intrinsic harshness of the specific genre that differs from others in the degree of enjoyment. As a result, the evaluating model based� on Game Flow is worthy of inquiry in an educational context for further investigation.�
Advancement in Autonomous Navigation in Space through Artificial Intelligence: A Systematic Review
Since the first trip to Mars, rovers have been used to conduct scientific experiments. The use of rovers continues to date due to Mars being uninhabitable for humans at this state and time. Despite the invention of Mars rovers, many limitations have been challenging space exploration, even with improvements in the Curiosity and Perseverance rovers. This study aimed to investigate the advancement in autonomous navigation in space through artificial intelligence. The study used qualitative, desktop, and systematic review research designs. The study's objectives were to determine the types of autonomous navigation technologies in space and examine the role of artificial intelligence in autonomous navigation technologies. Data was collected from secondary sources, which were purely open-access online journals. A total of 61 journals were searched as the target population. The study then conducted judgmental sampling using 9 out of 61 journals. The Preferred Reporting Items for Systematic Reviews and meta-analysis guidelines guided the sampling process. The study used an extraction form to collect data. The study found that star tracker navigation reduces positioning errors and environmental disturbances from sand and dust storms. It was also established that LOS navigation and star trackers ensured highly accurate navigation in challenging conditions for path-planning accuracy. Selecting the Next Coordinate, Obtaining Coordinates of Target Points, Generating Additional Coordinates, and optimising planned paths reduce travel time and energy consumption. On-orbit servicing robotics can extend rover missions up to 10 years. It was also found that 3D OctoMaps improves rovers' navigation accuracy. In addition, the study revealed that unsupervised homography networks, state recursive models, and inertial measurement models reduce localisation errors. Simultaneous Localization and Mapping can improve navigation accuracy and reduce mission delays. The use of RL navigation systems reduces mission delays. It was also found that Convolutional Neural network algorithms advance autonomous navigation accuracy in terms of terrain classification. The study also established that the Nvidia Jetson Nano AI controller and Rapidly-exploring Random Tree (RRT) in MATLAB reduce travel time and energy consumption. Finally, it was found that artificial neural networks advance the accuracy of terrain classification and lower navigation errors.
Classification of Acoustic Data with Transformer Model
Bees are essential to global ecosystems, particularly for pollinating crops, yet in recent years their populations have faced significant decline. One critical aspect of bee colony health is the ability to detect negative in-hive events such as a queen leaving the hive. Traditionally, beekeepers rely on manual inspections to assess hive conditions, a labor-intensive and time-consuming process. However, recent advances in machine learning offer new approaches to automating this task. Since 2016, there have been attempts to classify bee sounds using machine learning, employing the power of different machine learning methods, including deep learning architectures. In this research, we explore the use of acoustic labeled data for in-hive event classification, focusing specifically on detecting when a queen leaves the hive. We utilize 12-hour recordings from different locations, with the data preprocessed and transformed to be suitable for input into a transformer-based neural network. Our goal is to demonstrate that transformer models yield superior results in this task compared to previous approaches. The study is organized into several key sections: we first highlight the ecological importance of bees, followed by a literature review on the state of bee sound classification research. We then delve into the data preparation process, model design, and present our findings. Our results underscore the potential of transformer models in automating hive monitoring, offering a scalable solution for beekeepers to protect and preserve bee populations.
An Approach to Setting up Dataset for Winner-Domain Algorithm Selecting Teamers Participating in Vietnam National Competitions
The dataset of competitors� performance in some recent years is the input of winner-domain algorithm selecting good student as member of a subject team participating in Vietnam national prize. The data of learning performance features may be retrieved from the competitors� learning outcomes. Meanwhile, the collection of non-learning performance feature data of competitors meets challenges relating to the individual right and interviewees� cooperation for answering questionnaire. This research analyzed competitors� skills and lifestyle into very simple questions with very easy answer mode, without the deep relation to their individual lifestyle. The reply-matrix and reply-cube are formed to map simple answers of questionnaire reply into quantitative values. This approach may be flexibly applied to easily form the datasets of competitors� performance for various subjects in different years and at different levels of competitions.
Security through Elliptic Curves for Wireless Network in Mobile Devices
The basic principle is �A function is easy to evaluate but its invert is infeasible unless a secret key is known�. It is mathematically proved that security of cryptographic does not imply its implementation security of system against Side-channel Attacks. The security of system lies in the difficulty of extracting k from P and Q. It is essential to secure the implementation of cryptosystems in embedded devices against side-channel attacks. These attacks monitor the power consumption or the Electromagnetic emanations of a device Ex smart cards or mobile devices. The attacker�s goal is to retrieve partial or full information about a long-term key that is employed in several ECSM executions. We are implementing a secret key avoid to retrieving the valuable information by the attacker through Simple Power Analysis Attacks.
Four Universal Data Structures of 169 arrays
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Artificial Intelligence and the Challenges of Copyright Protection Systems in Search of Common Norms
The integration of artificial intelligence (AI) into the production of creative works presents significant challenges to the existing frameworks of copyright law. With AI's increasing autonomy in creative processes, traditional notions of authorship and originality are under scrutiny, necessitating a reevaluation of copyright protections. The paper proposes an evolved copyright framework that distinguishes between AI-assisted and AI-autonomous creations, suggesting clear guidelines for AI authorship and recommending the establishment of a specific copyright category for AI-generated works. This category would adjust protection levels and terms to reflect the unique nature of these works while ensuring that human creativity remains incentivized. The proposed framework aims to recalibrate copyright law to ensure the protection of human creators' rights while acknowledging AI's innovative contributions. This focused approach underscores the need for copyright law to adapt to technological advances, ensuring that it continues to fulfill its role in promoting creativity and innovation.
Practical Screening Method for Cancer Gene Diagnosis, How to Choose Cancer and Normal Patients by Four Principles
We developed a new theory of discriminant analysis (Theory1). Physicians can use it for practical medical� diagnoses. Only Revised IP Optimal-LDF (RIP) obtains the minimum number of misclassification (MNM). RIP can� discriminate linearly separable data (LSD) theoretically. It discriminated against 169 microarrays with two classes and found� that 169 MNMs are zero and LSD. It can split high-dimensional arrays into many small LSD with less than n (patient�s� number) genes that are the candidates of multivariate oncogenes. We completed a new theory of high-dimensional gene data� analysis (Theory2). A 100-fold Cross-Validation (Method1) can rank all candidates for the importance of diagnosis. Thus, if� physicians firstly use Theory2 as the screening method, they can start their medical studies with the correct small sizes of� candidates. This paper analyzes four arrays in detail and proposes correctly choosing cancer and normal patients using four� principles.�
Smooth Attention: Improving Image Semantic Segmentation
This document shows the required format and appearance of a manuscript prepared for SPIE journals. It is prepared using LaTeX2e with the class file spieman. cls. Please note that the following journals require the use of structured abstracts in manuscript submissions: Neurophotonics, the Journal of Biomedical Optics, and the Journal of Medical Imaging. Structured abstracts are encouraged for the Journal of Micro/Nanolithography, MEMS, and MOEMS. Guidelines are available on the journal website. Whether structured or single-paragraph, the abstract should be a summary of the paper and not an introduction. Because the abstract may be used in abstracting and indexing databases, it should be self-contained (i.e., no numerical references) and substantive in nature, presenting concisely the objectives, methodology used, results obtained, and their significance. A list of up to six keywords should immediately follow.
Achieving Path Reversal in OTN By Control Plane Programmability
SDN �paradigm has been successfully used in IP networks to bring agility and programmability. Applying SDN to OTN (Optical Transport Networks) has some challenges due to circuit switched nature and physical characteristics of OTN. If SDN and OTN can be modified to adapt to each other, there are immense benefits that can be derived from this combination. This paper presents an innovative design by modifying components in OTN and incorporating SDN concepts along-with it. This design encompasses key SDN concepts such as control plane and data-plane separation, central view of network domain for decision making and software-based programmability. This design uniquely implements the flow-reversal in Optical Ring by programmability that its control plane offers. In traditional OTN, it is not only time consuming but also complex process to obtain this type of flow reversal as it requires hardware configurations. The design presented here manages to rapidly configure the flow using its centralized control-plane programmatically. The design was implemented and thoroughly tested using simulator tool OptiSystem. The design is generic and does not depend on any specific vendor component. The design is flexible and can co-exist in end-to-end network setup. This design aims to help fellow researchers in their research work related to SDON (Software Defined Optical Network) and can be used as-is or with suitable modifications. This paper is organized into logical sections, starting with Introduction section that gives background and problem description. The next section is Literature Review which covers review of relevant studies and gaps. Then Methodology section briefly covers research methodology used, how results are collected and stored etc. The Design section depicts the solution by explaining high-level design approach, reasons behind design choices and the detailed design. The Result section denotes the observed outcome and confirms that it matches with expected results. Finally, conclusion section summarizes key points, findings, contribution and future scope.
The Extent to which Graphic Designers in Oman Rely on AI Software
The current study examined the extent to which graphic designers in Oman rely on artificial intelligence software and it highlighted full-time and freelance designers specifically. Artificial intelligence has advanced tremendously in the last several years, and this has had an impact on human life in general. In other words, artificial intelligence is a tool that recognizes patterns and assists humans in problem-solving. From this perspective, AI can serve as a designer&#39;s aide. This study aims to measure the extent to which graphic designers use AI software to accomplish their design tasks and to identify the most frequently used AI programs among graphic designers in Oman. This research utilized a quantitative method, collecting data through an online survey questionnaire. By exploring the familiarity of designers with AI programs in graphic design within Oman, this study provides a foundational resource for future research. It is among the few studies that focus on this specific target group. Future research could investigate the long-term effects and evolving practices of users as AI tools become increasingly integrated into design workflows.
Neuro-Driven Cybersecurity: Strengthening Digital Defense
The swift progress in the fields of neuroscience and cybersecurity has presented a transformative opportunity for multidisciplinary collaboration. The use of neuroscience-informed approaches can offer fresh perspectives and tactics for bolstering cybersecurity safeguards as the digital ecosystem grows more intricate and linked. This editorial examines the connections between these two disciplines, emphasizing how neuroscience can advance our knowledge of vulnerabilities that are human-centric, enhance threat detection and response, and encourage the creation of cybersecurity frameworks that are more flexible and resilient. The editorial highlights the vital significance of promoting interdisciplinary research and collaboration to protect the digital domain against developing cyber threats by looking at the intersection of neuroscience and cybersecurity.
Digital Identity in the Age of Cybersecurity: Challenges and Solutions
Digital identity encompasses a mix of personal traits, data, and activities online. It's not just basic info like name and age but also includes all the stuff you do and leave behind online. Think of it like your online fingerprint. Just like how we have layers to our real-world identity, our digital one is just as layered(Clooney et al., 1995). It's not just about who we are but also about how we're seen and understood by others online. Films like Jia's explore this idea, showing how our online identities can be shaped, portrayed, and even disrupted through storytelling(MENKUS et al., 2018). So, basically, digital identity is this complex thing that both mirrors and shapes how we see ourselves and others in the online world.
Internet Architecture: Current limitations leading Towards Future Internet Architecture
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Sentiment Analysis in Higher Education: A Systematic Mapping Reviewbased Deep Neural Network
In the last years, sentiment analysis (SA) has attracted increasing interest in the text mining area. It increasingly becomes a popular research area for opinion mining in education that analyses and understands students' opinions toward their institutions for improving the quality of decision-making. In this study, a systematic review was conducted to explore the recent application of sentiment analysis in higher education; to classify SA techniques and methods commonly and successfully used in the higher education domains. A systematic mapping review was applied to 840 articles, and 22 related studies are selected based on the study's criteria. The findings revealed that the prior studies mainly focus on six domains for applying SA in the higher education context and the teaching quality evaluation was the most addressed domain. The study also found that applying specific SA techniques could be the best tool for institutions to solve particular learning problems and a useful tool for improving higher education institutions' quality and evaluating the teaching process as well as teachers' performance. This study's main contribution is the new categorizations of the SA applications techniques in higher education to provide a closely full image of SA-related tools and areas.
Big Data Analytics : Use of Hadoop Mapreduce
Effect of Google Meet and Instagram App on Speaking Accuracy in Intermediate Level of EFL Learners in L2
Foreign language acquisition is a complex process that requires extensive practice, particularly in developing speaking skills. The aims of this study aims to analyze the effect of utilizing digital platforms such as Google Meet and Instagram on improving foreign language speaking abilities. Four key areas will be discussed: greater access to native speakers, interactive and immersive speaking opportunities, increased cultural exposure, and personalized digital language learning tools. The findings suggest that incorporating Google Meet and Instagram into foreign language learning significantly enhances students' speaking proficiency, motivation, and overall language acquisition. In this study, for completion of this article used from qualitative methods and for data collection is used from interview from the learners the integration of Google Meet and Instagram into foreign language learning offers a multitude of benefits in terms of native speaker interaction, immersive speaking opportunities, cultural exposure, and personalized language learning tools. By utilizing these platforms, language learners can significantly enhance their speaking skills, build confidence, broaden cultural awareness, and establish connections with individuals from various linguistic backgrounds.
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Visualization Tool for Data Structures in Real Time
Data structures are crucial aspects of Computer Science, but grasping their abstract nature poses challenges for students and software developers. A visualization tool for data structures such as arrays, stacks, and trees would effectively simplify the complex nature of data structures and algorithms. This research utilized an object-oriented approach to create a real-time Visualization Tool for Data Structures. This tool offers visual representations of fundamental data structures such as arrays and trees. The visualization tool is web-based and developed with HTML, CSS, and JavaScript technologies. The tool's efficacy underwent evaluation using complexity metrics. Results notably demonstrate that as the volume of data increases, the complexity of data structures follows suit. Consequently, this paper serves as an informative resource concerning the selection of data types and their respective implementation styles within data structures. Such insights furnish developers with valuable knowledge regarding the efficiency of diverse data types in software development, empowering informed decisions when choosing between data types based on their impact on space complexities within data structures.
Cascading Failure under the Digital Economy Industrial Risk Transmission
With the background of emergencies and the digital economy industry as the core, this paper collects the data of China's 2020 input-output table, uses network analysis technology to establish digital economy industrial networks, analyzes the structural characteristics of industrial networks, and analyzes the risk transmission characteristics of industrial networks under the impact of various emergencies by establishing cascade failure simulation models. The results show that the digital trading industry occupies a higher position than other digital economy industries in the industrial network and is an important intermediary industry in the economy. Sudden systemic financial risks cause the largest damage at the highest risk transmission speed.
Leveraging AI in Clinical Diagnosis: A Paradigm Shift in Medical Decision-Making
The integration of Artificial Intelligence (AI) in clinical diagnosis heralds a significant shift in medical decision-making, enhancing diagnostic accuracy and efficiency while introducing new ethical and operational challenges. This review delves into the latest advancements, applications, and implications of AI in clinical diagnostics, discussing its potential to transform healthcare practices.