International Journal of Innovations in Science & Technology
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Smart Power Management with Small Cells: A Path to Sustainable Data Connectivity
Get PDFThe rising demand for energy-efficient networks capable of supporting high-speed data traffic poses a critical challenge for network operators. This study addresses this issue by proposing a power control strategy that dynamically adjusts small cell transmit power based on traffic patterns. Power usage is reduced to 40% of the total capacity during normal traffic, while 60% is utilized during high traffic intensity. This traffic-driven power allocation approach achieves a 13-15% improvement in energy efficiency compared to conventional small cell-controlled sleep modes. By optimizing energy consumption without compromising network performance, this research provides a practical solution for balancing efficiency and user satisfaction in modern mobile networks
Analysis of Machine Learning Models to Automate the Early Detection of Alzheimer Disease
Get PDFAlzheimer\u27s disease is an advanced neurological illness that primarily affects those over 65. It is characterized by memory loss and cognitive deterioration. Although there isn\u27t a known cure, early intervention can greatly delay the disease\u27s progression, which emphasizes how crucial a prompt and precise diagnosis is. Early-stage identification is still a difficult and time-consuming procedure, though. This study uses machine learning (ML) to improve and speed up Alzheimer\u27s disease detection. The National Alzheimer\u27s Coordinating Center (NACC) dataset, which consists of clinical and genomic data, was subjected to three ML algorithms: Elastic Net Classifier (ENC), Random Forest (RF), and Artificial Neural Network (ANN). Unlike established methodologies that largely rely on Magnetic Resonance Imaging (MRI) paired with other modalities, this research highlights the utilization of limited datasets and comparatively underexplored clinical-genomic data. The models were trained and assessed using the Scikit-learn and Tensor Flow frameworks. With an accuracy, F1 score, and recall of 92%, ANN outperformed the other models, indicating its potential for early Alzheimer\u27s identification. This study demonstrates the feasibility of addressing difficulties in early-stage Alzheimer\u27s diagnosis by combining clinical and genomic data with machine learning algorithms
The Implementation of Rent Hub System: An Intelligent Online Rental Marketplace with ML-Powered Personalized Product Discovery and Recommendations
No full textThe rapid expansion of peer-to-peer rental services has significantly influenced the share economy by connecting consumers with short-term access to diverse rental products. However, existing platforms primarily focus on specific categories, limiting consumer choices and creating a gap in the market. This study introduces RentAll, a comprehensive multi-category rental platform offering access to houses, automobiles, furniture, gadgets, and jewelry, while prioritizing data privacy through anonymized transactions. To enhance user experience, we developed a recommendation system utilizing content-based filtering, cosine similarity, and collaborative filtering through FP-Growth Frequent Itemset Mining to suggest products based on customer behavior. Additionally, a chatbot powered by a Sequence-to-Sequence model using RNN and LSTM units was integrated for real-time customer support. The results demonstrate RentAll\u27s effectiveness in providing a unified rental solution with personalized recommendations. The platform streamlines the rental process, reduces financial strain, and expands product offerings to serve diverse demographics. High user satisfaction is reported due to its user-friendly interface and engaging features, including secure payment processing via Easypaisa. Moreover, the implementation of robust security measures protects user information and builds trust. In conclusion, RentAll effectively addresses key issues in online rentals by offering a user-friendly platform with diverse rental categories, enhancing consumer convenience and satisfaction while maintaining stringent data protection standards
Deep Learning-Based Image Captioning for Visual Impairment Using a VGG16 and LSTM Approach
Get PDFVisually impaired persons frequently have trouble understanding their environment. which affects regular tasks like reading signs, navigating their surroundings and recognizing things. Providing precise and timely image descriptions is crucial to improving their comprehension of their surroundings. Even if they work, traditional image captioning techniques frequently fail to provide clear, understandable explanations. Recent developments in deep learning present fresh chance to enhances to enhance picture captioning. In this sector, long short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) have become indispensable instruments. This research focuses on applications like VGG16 and Resnet models, data augmentation and transfer learning with a custom dataset to create such kind of captioning system providing original setup for precise context retrieval. Additionally, the system utilizes a text-to-speech functionality so users can listen to their responses if they are visually impaired. The highest accuracy obtained by the model is 0.9106 and validation loss was 0.1766. On randomly chosen set data experiments are conducted, there were significant differences between the BLEU scores we observed, ranging from 0.7788, to a perfect score of 0.1, indicating a diverse range of captions accuracy. This research shows how the adopted more sophisticated CNN models along with text-to-speech can improve image captioning systems by offering visually impaired detailed and meaningful descriptions
Mininet-IDS: A Step Towards Reproducible Research for Machine Learning Based Intrusion Detection Systems
Get PDFSoftware-Defined Networking (SDN) has revolutionized network management by enabling more flexible, programmable, and controlled networks. However, the SDN controller can be a target for attacks that could bring down the entire network. In this context, intrusion detection systems (IDS) are essential for maintaining network security. Modern IDS are often enhanced with machine learning models to detect a range of network attacks. This process typically includes dataset preprocessing, model training, and integration of these models into network emulators like Mininet. However, this workflow can be complex and error-prone. To address these challenges, we present Mininet-IDS, a comprehensive command-line interface (CLI) tool that streamlines the process by offering integrated functionalities for dataset preprocessing, feature selection, model training, and deployment within the Mininet environment. Our tool simplifies the workflow by eliminating compatibility issues and ensuring reproducibility. We evaluate Mininet-IDS using the NSL-KDD dataset, training various machine learning models to detect DDoS attacks. Our results demonstrate the tool\u27s efficiency and accuracy, making it a valuable resource for network security researchers to conduct experiments with minimal machine learning expertise
The Emergence of the Internet of Things in Military Defense: A Comprehensive Review
Get PDFThe Internet of Things (IoT) has emerged as a significant research field. The main concept of IoT technology is to connect millions of devices and facilitate interaction between these devices and the cloud. Recently this concept has been considered and applied in the design of systems intended for distributing data and information between heterogeneous devices. The goal is to enhance the performance of the business and decision-making process. IoT enables energy and supply chain monitoring, production coordination, equipment performance optimization, transportation, and public health, to improve, and enhance worker safety. It is revolutionizing military operations enhancing battlespace awareness operational efficiency, and command structures while enabling more intelligent and responsive security systems across diverse defense sectors and mission domains. This paper discusses how IoT technology shapes the future of military information and defense systems. The goal of this article is to present a comprehensive literature review on the application of IoT in military defense. This review also puts future recommendations for the further development of IoT technology in the defense sector
Revolutionary Hologram Systems: Pioneering a New Frontier in Visual Technology
Get PDFNew technologies are enabling the development of consolidated, portable holographic displays that can be utilized in a variety of settings, making holographic content more accessible and shareable. Holographic displays represent objects in three dimensions, providing a more pragmatic and immersive viewing experience. This is particularly important in sectors such as architecture, design, and medical imaging, where the representation of depth can aid in comprehension and decision-making. This study presents a revolutionary and efficient method for converting 2D images into immersive 3D holograms using Blender software and a holographic FP. The process begins with Blender, an open-source 3D creative software that transforms 2D photographs into dynamic 3D models to add depth and realism. These transformed images are then uploaded to a user-friendly mobile app, which acts as an intermediary for seamless transfer to a holographic display. The smartphone app offers an intuitive interface for image customization and management. This study contributes to the understanding and practical application of 3D holographic displays by addressing key challenges and outlining the development process, resulting in a versatile tool. These recent breakthroughs in holography are expanding its potential applications and enhancing user experiences, making holographic technology increasingly significant in industries such as healthcare, education, entertainment, and data visualization
An Identification of Fake Contents Using Text-mining Techniques
No full textIn recent years, social media users have become increasingly concerned about sharing content that may be unpleasant or harmful. The widespread use of platforms like Facebook and Twitter has contributed significantly to this growing awareness. The primary objective of our approach is to accelerate and automate the detection of offensive content posted on these platforms, simplifying the process of taking necessary actions and filtering harmful communications. A benchmark dataset, OLID 2019 (Offensive Language Identification Dataset), is available online to aid in this task. Our study focuses on identifying whether a tweet is offensive. Our team, which included several members, rigorously compared various feature extraction methods and model-building algorithms. Ultimately, our comparative analysis revealed that decision trees were the most effective model. The decision trees applied to the normalized dataset resulted in an 84% improvement in the Macro F1 score, which aligns with previous research. In conclusion, a real-time system could be developed across multiple social media platforms to detect and evaluate objectionable posts, enabling timely interventions to promote healthier online behavior and foster a positive societal impact
A Comprehensive Review and Analysis on Voltage Stability Enhancement Using Distributed Generation
Get PDFThe present-day scenario of electrical power system engineering mainly comprises issues like power paucity, blackout, load shedding, and ineptness in meeting the necessary demand for power. Therefore, new power plants are built and old ones are expanded and upgraded. Although these developments play a key role in today’s scenario, there remains a field where the scope of development persists. This review article focuses on incorporating load models in traditional (OPF) studies and comparing the results of the above with those obtained from OPF analysis with the incorporation of (DG). The study of stability analysis and its behavior is a very important aspect of power system planning and its design for reliable operation. The determination of the transient stability of an electric power system is a crucial step in power system analysis. It has become imperative for power engineers to look out for improvement in the voltage stability of a power system. For this purpose, the IEEE 9 bus system is considered. The main objective is the analysis of the transient stability of an IEEE 9 bus system consisting of three generators and nine buses. Further, transient analysis of the power system network will facilitate the design of a better DG network. Here, MATLAB software is used to analyze the stability of the power system
Python Based Estimation of Groundwater Quality Along Hudaira Drain
Get PDFDuring periods of restricted access to fresh surface water, enterprises depend on underground water reserves to meet their growing demands. Groundwater is crucial for fulfilling the growing demands of families, agriculture, and industry. The degradation of groundwater quality has resulted from a combination of natural phenomena and human intervention, leading to the introduction of novel contaminants into the ecosystem. The current study utilized geospatial technology to investigate the geochemical properties and Water Quality Index (WQI) of groundwater along the Hudaira drain in the Lahore area of Pakistan. A total of thirty-six groundwater samples were taken at regular intervals of half and one kilometer along the Hudaira drain. The samples underwent analysis for twenty physio-chemical and metal parameters. The groundwater at the sites under investigation was classified into three categories: adequate (5.55%), acceptable (63.9%), and poor (30.6%), according to the WQI. The trilinear piper diagram was used to assess the salinity of water samples. Samples were segregated into two groups: the first group mainly consisted of calcium bicarbonate, whereas the second group contained calcium sodium bicarbonate salts in groundwater. The Gibbs diagram is employed to illustrate the prevailing influence of rock-water interactions in all groundwater samples. Elevated levels of salt lead to salinity issues and diminished agricultural output. This study demonstrated the harmful effect of drained water on groundwater in the Hudaira region, primarily through the processes of percolation and infiltration. Moreover, it can be inferred that the groundwater near the Hudaira drain is not fit for human consumption. Nevertheless, prolonged irrigation may give rise to issues associated with the accumulation of salt