International Journal of Innovations in Science & Technology
Not a member yet
813 research outputs found
Sort by
Analyzing Government Policies Causing Smog: An Evaluation
Get PDFIntroduction/Importance of Study: This study aims to explore the relationship between government policies and the worsening of smog through a comprehensive analysis. Understanding this relationship is essential for designing policies that effectively reduce air pollution, particularly smog.
Novelty Statement: This research introduces a novel approach by proposing a practical solution to address the worsening smog issue, highlighting the gap between government policies and their on-ground implementation.
Material and Method: We conducted an extensive review of relevant laws, policies, regulations, and jurisprudence related to environmental protection. Environmental protection measures were identified through provincial and national environmental protection department websites. Actions required for environmental protection, as outlined in these legal documents, were assessed to develop a viable solution.
Result and Discussion: This study aims to make a significant contribution toward achieving a \u27Good\u27 Air Quality Index (AQI) and guiding the creation of effective government policies. The goal is to ensure that policies are balanced—not too lenient or too strict—so they can address real-time issues effectively.
Concluding Remarks: Achieving an optimal solution requires collaboration among legislators, researchers, technocrats, academicians, and the executive branch. Furthermore, any deficiencies in policy implementation should be addressed by the High Courts or the Supreme Court to ensure accountability and effectiveness
A Critical Evaluation for the Energy Efficient Routing Protocols in Wireless Body Area Sensor Networks (WBAN)
Get PDF
Wireless Body Area Network (WBAN) is a promising technology for providing intelligent healthcare services in remote locations. A review of the literature shows that researchers have primarily focused on Quality of Service (QoS)-aware energy-efficient routing techniques, network topology, and Medium Access Control (MAC) layers in WBANs. QoS-aware routing techniques are based on a set of protocols that efficiently maintain routes and effectively facilitate data exchange between sensor nodes. This research introduces WBAN and discusses its medical applications in detail. It provides a classification of routing protocols in WBAN and addresses the challenges researchers face in QoS-aware routing. Additionally, a framework for an energy-efficient routing protocol in WBAN is developed, aimed at healthcare authorities for use in emergency rescue operations
Dynamic Malware Detection Using Effective Machine Learning Models with Feature Selection Techniques
Get PDFDynamic Malware is a type of virus that is self-modifying, which makes it difficult to analyze in the course of its operation. It occasionally changes its behavior based on the existing environment and the context of execution. The goal of this study was to identify and detect dynamic malware in Android devices using effective machine-learning models with feature selection techniques. With new malicious software emerging daily, relying solely on manual heuristic analysis has become ineffective. To address this limitation, the study used dynamic detection methods to detect the events of interest using machine learning models. Some of these measures entailed duplication of an environment in which the behavior of malware could be replicated and then come up with reports. The reports were then transformed into sparse vector models so that other machine-learning techniques could then be applied to them. In this research study seven different models, namely, KNN, DT, RF, AdaBoost, SGD, Extra Trees, and Gaussian NB, were used to train an effective malware detection model to predict the dynamic malware in its early stages. The study showed that Random Forest, Stochastic Gradient Descent, Extra Tree, and Gaussian Naive Bayes classifiers achieved the highest accuracy compared to other models. This research study endorses the application of machine learning-based automated behavior analysis for malware detection, about the complexities involved in the dynamic behavioral analysis of malicious software
Exploring the Spectrum Power Fractal Scaling Parameters by Hurst Range Increment - Second Order Moment Generation Techniques
Get PDFSpectral power analysis was employed to assess the Fractal Dimension (FD) and explore fractal scaling using Hurst increment ranges and second-order moment relations in the context of urban population trends. This research aimed to scrutinize population trends in Karachi over both uneven periods (1729 to 1946) and even periods (1951 to 2020) using non-parametric Mann-Kendall tests and Hurst error accuracy testing. The primary focus was on analyzing spectrum power fractal scaling through Hurst exponent ranges and second-order moment generation. The FD results indicated irregular (1.371) and regular (1.058) intervals within the inequality range of 1 < D < 1.5. The log-population trend cumulatively increased from 3.0 in 1729 to 5.72 in 1946, and from 6.05 in 1951 to 7.36 in 2020, suggesting that the fractal dimension is more appropriately fitted for total regular intervals. The second-order and range exponents were H2ndM (0.60 ± 0.09) and H-Range (0.83 ± 0.05) for the uneven period (1729 to 1946), and H2ndM (0.85 ± 0.06) and H-Range (0.93 ± 0.02) for the even period (1951 to 2020). The study\u27s results demonstrate that the range increment method is suitable and consistent across both long and short intervals. For regular intervals, the Hurst exponents show a linear relationship, indicating stability in the population trend analysis
A Sustainable Growth Meta-Mask Consulting Application for Agriculture Sector Using Ethereum and Blockchain Technology
Get PDFPakistan\u27s economy depends heavily on the agricultural sector, yet a large number of farmers encounter financial constrains, including debt, loan repayment, a lack of loan security, and crowdfunding scams, which are the primary reasons for converting their lands into real estate. Crowdfunding for agriculture on the blockchain will cut out the middlemen and connect customers and producers directly. Blockchain technology provides a way to share a database or ledger that will guarantee an unalterable and consistent version of the truth even amongst untrustworthy players. Therefore, this study establishes a peer-to-peer network and a marketplace where community members can fund agricultural endeavors in exchange for food items. The novelty of this research is that the blockchain-based crowdfunding system for agriculture that enable investors to connect with farmers consistently and directly. The methodology includes the integration of AI-powered consultation tool, like ChatGPT into a web application to increase its efficacy. With the use of this instrument, enables farmers to solve issues pertaining to saline lands and obtain information regarding land productivity. This tool provides farmers quick, accurate, and easily available information to assist them in making better decisions. Therefore, this research aims to provide a comprehensive approach to aid impoverished farmers, encourage agricultural expansion, and ensure equitable profit sharing among all parties involved. Through the integration of blockchain technology, cooperative investment, and AI-powered consulting, this study aims to promote the agriculture sector\u27s sustainable growth
Classification of Medical Images Through Convolutional Neural Network Modification Method
Get PDFThe COVID-19 positive, tuberculosis and pneumonia, share the trait of being able to be identified using radiological investigations, such as Chest X-ray (CXR) images. This paper aims to distinguish between four classes, including tuberculosis (TB), COVID-19 positive, healthy, and pneumonia using CXR images. Many deep-learning models such as a Convolutional Neural Network (CNN) have been developed for the Classification of CXR images. Deep learning-based models such as CNN offer significant advantages over traditional methods in the classification of diseases like TB, COVID-19, pneumonia, and healthy states. They provide higher accuracy, automation, early detection, reduced subjectivity, and resource efficiency, ultimately leading to improved patient care and outcomes. However, well-liked CNNs are massive models that require a lot of data to achieve optimal accuracy. In this paper, we propose a new CNN model that can be used to distinguish between different classes of CXR images. This model proves to be effective in classifying different diseases such as pneumonia, COVID-19, and tuberculosis. This study has used 6326 CXR images dataset containing COVID-19 positive, tuberculosis, and pneumonia and has normal images. In this dataset, 80% of the CXR images are taken for the training purpose and 20% are taken for the validation purpose, of the proposed CNN model. The proposed CNN modified model with parameter adjustment as well as using categorical cross-entropy as a loss function obtains the highest classification accuracy of 98.51% with a precision, recall, and F1 score of 0.98, 0.985, and 0.98 respectively
Harnessing Language Intelligence: Innovative Approaches to Sustainable Mental Health Interventions in the Digital Age
Get PDFThis study explores the advanced abilities of Natural Language Processing (NLP) methods to revolutionize mental health treatment by understanding how such interventions improve therapeutic outcomes. In doing so, the work of this study is demonstrated as an innovative approach to translating conversational data into actionable insights that bridge a large gap in the detection of subtle emotional cues in mental health assessments. The research used DistilBERT, an optimized version of the BERT framework, which has been fine-tuned on specially selected datasets to accurately identify emotional states such as sadness, joy, anger, and fear. Emotional and linguistic patterns were analyzed to identify often unarticulated signals to identify disorders such as depression, anxiety, and Post-Traumatic Stress Disorder (PTSD) much earlier. In this regard, the model has been found to significantly enhance the understanding of patients\u27 emotional states more accurately and subtly than through traditional means. The findings of this study highlight the potential of offering individualized therapeutic interventions within digital health applications, which enables immediate emotional well-being assessments. The study showcases the flexibility of AI-based systems, making them applicable to almost any environment, including a workplace setting, to promote both wellness and productivity. This study sets the ground for developing scalable, customized, and proactive mental health care strategies that are beyond conventional therapeutic frameworks
Go Drive Net: A Unified Platform for Cloud Storage with Social Networking
Get PDFIn today’s digital age, cloud storage services have revolutionized the way data is stored, accessed, and shared across multiple devices and locations. The primary role of these platforms revolves around storage and access, and they are now vital in many areas. However, the rise of cloud computing has brought new challenges to researchers and professionals. Go Drive Net is being used in this study as a research tool to examine user data that persists after various methods of cloud storage, uploading, and accessing data are explored. Cloud Storage also provides a model as a storage service that provides storage facilities to the users via the Internet. By analyzing user software data, network connection captures, memory captures, and other available data. This study aims to provide experts and analysts with a deeper understanding of the types of data that remain on various devices. By connecting users, Go Drive Net not only improves productivity and collaboration but also provides data security through encryption technology. It also enables data renaming, deletion, sharing, migration, user search, and communication. As cloud computing continues to shape the future of IT, it enables organizations to respond to technological change more quickly, efficiently, and innovatively
Predictive Analysis and Email Categorization Using Large Language Models
Get PDFWith the global rise in internet users, email communication has become an integral part of daily life. Categorizing emails based on their intent can significantly save time and boost productivity. While previous research has explored machine learning models, including neural networks, for intent classification, Large Language Models (LLMs) have yet to be applied to intent-based email categorization. In this study, a subset of 11,000 emails from the publicly available Enron dataset was used to train various LLMs, including Bidirectional Encoder Representations from Transformers (BERT), Distil BERT, XLNet, and Generative Pre-training Transformer (GPT-2) for intent classification. Among these models, Distil BERT achieved the highest accuracy at 82%, followed closely by BERT with 81%. This research demonstrates the potential of LLMs to accurately identify the intent of emails, providing a valuable tool for email classification and management
Lightweight Cryptography Algorithms for Internet of Things enabled Networks: A Comparative Study
Get PDFThe rapid advancement of technology has facilitated the interconnection of numerous devices, enabling the collection of vast amounts of data. Consequently, ensuring security within Internet of Things networks has become a top priority. Cryptography is crucial in safeguarding network authentication, confidentiality, data integrity, and access control. In Internet of Things settings, conventional cryptographic protocols frequently prove impractical owing to the limitations confronting Internet of Things devices. Consequently, scholars have suggested multiple lightweight cryptographic algorithms and protocols customized for safeguarding data in Internet of Things networks, aiming to overcome this hurdle. This review article delves into the most recent lightweight cryptographic protocols designed for Internet of Things networks and furnishes a comparative evaluation of prevalent modern block ciphers. The comparative study discusses the most recent lightweight cryptographic algorithms in different evaluation parameters in terms of their performance metrics, cryptographic features and offering in-depth analysis of their efficiency. In the concluding section, the paper discusses necessary adaptations and suggests future research directions