International Journal of Communication Networks and Information Security (IJCNIS)

An Ensemble Based Astrological Prediction Model for Profession and Marriage Using Machine Learning Strategies

2024-08-28T02:41:39+00:00

The fascination with astrology, an ancient and conventional form of prediction, continues to grow despite the absence of universal astrological prediction rules or principles globally. While accuracy is not guaranteed, astrologers prioritize offering high-quality services over establishing universal standards. In contrast, machine learning yields superior outcomes across diverse applications through its capacity to handle large, noisy, complex datasets via classification and prediction. This paper aims to present a scientific method that addresses the shortcomings of traditional astrology, identifies universal prediction rules, and employs classification techniques—Neural Network (NN), Import Vector Machine (IVM), Random Forest (RF), and Iterative Boosting—to validate the reliability of astrology in predicting profession and marriage outcomes. We computed Correctly Classified Instances (CCI), Incorrectly Classified Instances (ICI), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Relative Absolute Error (RAE) using cross-validation with 10, 12, and 14 folds. Additionally, we evaluated F-Measure, Precision, True Positive Rate, False Positive Rate, and area values for MCC, ROC, and PRC. For three-class labeling of professor, businessman, and doctor, we determined the true positive rates, false positive rates, accuracy, F-measure, PRC, and ROC area. We gathered birthdate, birthplace, and time of birth data from one hundred individuals across these professions, creating horoscopes using software. Data analysis involved building a datasheet in .csv format and employing the Weka tool to assess various parameters, including classifier accuracy, to identify the most effective classification method.

Creativity and Innovation on the Adoptions of Creative Arts Activities: Attitudes and Perceptions of Kindergarten Teachers in Yunnan

2024-09-02T09:13:59+00:00

The study aimed to explore the influences of attitudes and perceptions of creativity and innovation of kindergarten art teachers on creative art activities in the kindergarten art classes of the first-class public demonstration kindergarten schools in Qujing City, Yunnan Province. Questionnaires were employed to collect data from 261 kindergarten art teachers. Descriptive analysis and multiple regression were used to analyze the data. The findings revealed the statistical differences among six variables of the adoption of creative art activities, which included: 1) Creativity in Creative Art Activities; 2) Evaluation of Perception of Teaching Activities; 3) Specified Art activities in Art Education; 4) Planned Classroom Goals; 5) Planned Classroom Activities; and 6) Adopting Creative Art Activities into Creative Art Teaching. For future research, longitudinal and cross-regional comparisons to track long-term changes with combined research methods can be applied to ensure the efficiencies of kindergarten creative art teaching activities to promote Chinese kindergartens’ artistic senses and skills.

An analysis using a structural equation model to assessthe various factors influencing the Iraqi construction industry, with a specific focus on the moderating of organizational culture

2024-09-02T13:15:18+00:00

The construction industry is of great importance as it is able to achieve cost savings and promote economic development worldwide. Regardless of a country's level of development, be it an underdeveloped country. Nevertheless, there are a number of constraints and hazards that hinder the start or progress of a construction project, and which usually have a significant negative impact on the overall project. In a previous study, the influence of a company on construction performance was investigated, leaving out certain factors. This study aims to fill this research gap by using the methodology of organizational culture and the various factors including stakeholders, communication, cost, technology, top management support and local authority support to investigate the impact on the Iraqi construction industry. The data pertaining to the research was gathered through a survey questionnaire administered to multiple construction project practitioners in Iraq. The research objective was achieved through structural equation modeling (SEM). The study operator a quantitative approach to gather data, which includes a survey questionnaire administered to construction project practitioners and interviews conducted with academicians who specialize in the construction industry. The results obtained from the SEM analysis indicate the model is appropriate for the characteristics of variables and data under investigation. The further analysis of research outcomes demonstrated that the hypotheses (H1, H2, H3, H4, H5, H6, H7, and H8) all the results were found to be statistically significantand had positive findings. A survey instrument was utilized to obtain information for the research from many construction companies in Iraq. The data have been analyzed, and an SPSS AMOS 26 software-based structural model has been constructed to test the results of the hypotheses.A moderate relationship can be inferred between organizational culture and the construction industry in Iraq, as indicated by a positive correlation coefficient of 0.036. A positive association is denoted by the positive sign that is an increase in one variable is typically accompanied by an increase in the other. A correlation coefficient of 0.08 indicates a positive relationship between organizational culture and stakeholder factors. Although the correlation demonstrates statistical significance, its magnitude suggests the strength of the relationship. A relationship exists between stakeholder factors and their influence on the Iraqi construction industry, as indicated by a positive correlation of 0.080. Alterations in construction industry developments might be correlated with stakeholder factor changes, as indicated by the positive correlation; however, the relationship is not definitive, noting that correlation does not imply causation is essential. Although the statistical relationships presented offer valuable insights, further investigation and analysis are required to comprehend the fundamental mechanisms and factors that underlie these correlations within the organizational culture and construction industry of Iraq.

Artificial Intelligence Driven Customer Relationship Management: Harnessing the power of technology to improve business efficiency

2024-09-03T13:12:16+00:00

This paper investigates how the Artificial Intelligence (AI) has significantly affected Customer Relationship Management (CRM), with focus on the transformative potential of AI tools like chatbots and predictive analytics in transforming customer-business interactions. Companies that integrate chatbots can personalize their assistance 24/7, thus improving client involvement and satisfaction. Additionally, another benefit from predictive analytics, is, the successful interpretation of customers’ behaviour patterns and their future requirements to enable early or precise tailoring of the experience. It also strengthens the existing business relationship with the customers and makes business efficient and effective in terms of increased sales turnover and revenues. This study by applying the mixed method approach underlines the crucial role of management and clients’ centric approach in conditions of the intensified competition of the nowadays high-stake market environment. The research results show that the use of AI in CRM systems can be critically beneficial for a business since such a system can enhance customer experience and provide decision-makers with tools to enhance their understanding of consumer conduct and behaviours. Such competencies help companies increase their long-term performance on the market since they uncover the potential of AI in CRM. Altogether, the findings are highly beneficial as it reveals the opportunities of leveraging AI in CRM systems to deliver the clear perspectives for improving interactions with clients and organisation’s performance.The research findings demonstrate that AI-powered CRM systems offer a significant competitive advantage by enriching customer interactions, uncovering deep insights into consumer behaviours and supporting better strategic decisions making. These competencies enable companies to strengthen their market position for long-term performance by revealing the true potential of artificial intelligence in CRM. The findings are highly beneficial as it brings forth insights into AI-powered CRM systems provide a clear roadmap for enhancing customer engagement and operational efficiency.

Integration of Artificial Intelligence in Activity-Based Project Costing: Enhancing Accuracy and Efficiency in Project Cost Management

2024-09-04T06:49:30+00:00

Activity-Based Project Costing (ABPC) has long been recognized as an effective method for managing project costs. However, the increasing complexity of modern projects demands more sophisticated approaches. This study explores the integration of Artificial Intelligence (AI) into ABPC to enhance cost estimation accuracy and project management efficiency. By utilizing machine learning algorithms and big data analysis, it has been developed an AI-ABPC model capable of predicting project activity costs with higher precision, identifying hidden patterns in historical data, and providing real-time cost optimization recommendations. A case study of 50 large-scale construction projects showed that the AI-ABPC model improved cost estimation accuracy by 30% and reduced cost analysis time by 40% compared to traditional ABPC methods. These findings pave the way for a revolution in project cost management, enabling faster and more accurate decision-making in dynamic project environments. The implementation of AI in ABPC not only enhances project financial performance but also fosters innovation in overall project management practices.

Recommendations for Changes in Education Practice in Sociology for Students

2024-08-31T12:32:52+00:00

The fundamental subject behind various professions is education beliefs. Both learning and teaching are complex tasks. An effective communication between the student and the teacher is essential to implement these tasks. A proper language, symbolism and technical vocabulary is essential for realizing the basics of instructions in Sociology. There are many difficulties faced by the students in learning Sociology such as; complexities with abstract direction and time concepts, mistakes like recalling, reading and writing numbers, reversals, omissions, transpositions, substitutions and additions. This paper of research has been designed to identify the existing difficulties faced by the students in learning Sociology and recommend some solutions and changes in the education practice, which could be made in Sociology teaching for the students. The research study encapsulates a survey, which comprise of 14 teachers of Sociology and 200 students. Both open-ended and closed ended questions were present in the questionnaire, which was designed for the proposed research. Some of the common difficulties encountered by the students in learning Sociology is identified in the present scenario and also the perceptions of the teachers about the mathematical difficulties faced by students were identified and recommendations were made finally, which were contemplated on the learning strategies and the beliefs of the students.

Enhanced SDD Algorithm Optimization Technique For Finding Hyper Parameter Of SVM

2024-09-05T05:44:28+00:00

Background: It is crucial to pay attention to the classify data. The classification of data via Support Vector Machine (SVM) approach has severe restrictions. Corresponding to this, the intriguing improvements could not be accomplished without a suitable Support Vector Machine (SVM) classifier improvement and it is of high significance to build a machine learning model which can accurately classify the data. In this paper, an enhanced framework is proposed mainly used for classifying the data by introducing a hyperplane.

Objective: The most important aspect of this whole framework is to create an enhanced version of recently developed evolutionary algorithm known as Social Ski Driver (SSD) optimization. As far as we know, enhanced version of SDD optimization algorithm have not yet used in SVM hyperparameter optimization to classify data.

Methods: We, improvise Social Ski Driver (SSD) exploitation ability, with Levy flight. To verify this, the proposed method is then applied to balanced, imbalance and multiclass datasets with higher dimensionality from the UCI repository then empiercally compared with Grid Search, PSO and SSD-SVM.

Results: The result achieved shows that ESSD-SVM is capable of finding, optimal solution and better performance classification as compared with other approaches

Conclusion: The proposed ESSD-SVM model's effectiveness is demonstrated by its accuracy that indicates that it optimizes classification performance for hybrid models, which takes less time.

Higher Order FIR Filter Architecture Design Using Optimized Booth Multipliers and SRCSLA Adders with Retiming for Denoising of ECG Signals

2024-09-05T07:34:29+00:00

In this paper, a power and delay efficient higher order filter architecture is designed and implemented for the ECG signal processing applications. To reduce the critical path delay, the retiming is introduced in the direct form FIR architecture. The optimized multipliers and adders are key blocks in the filter architecture to increase the delay and power consumption. In this regard, an optimized Radix-4 Booth multiplier is designed using a modified booth encoder and selector blocks along with the proposed improved version of Square Root Carry Select Adders (SRCSLA). The design metrics of the multiplier, which is used to multiply the filter coefficients and input samples also improved by using the proposed SRCSLA. For the proposed SRCSLA, The Carry Look Ahead and Carry Skip Adder concepts are combined and modified according to the proposed SRCSLA. Different bit size-based SRCSLA adders are implemented for the proposed multiplier architecture and to sum the final filter output. The adder structure speed is improved by modified carry-producing and propagating blocks. The retiming-based direct-form FIR filter architecture for the order N = 32 is coded by HDL and synthesized using Genus tools from Cadence in 45nm CMOS technology. Area complexity, time complexity, and power consumption are estimated by the reports generated by the Genus synthesis tool. The trade-off design metrics Area-Delay-Product (ADP) and Power-Delay-Product (PDP) are also estimated and compared with the conventional filter architectures, and existing filter distributed system architectures.

A Literature Review on Software Defect Prediction: Trends, Methods, and Frameworks

2024-09-05T08:41:57+00:00

Identifying possible problems at an early point in the development lifecycle is one of the most important things that software defect prediction can do to enhance software quality and minimize development costs. This is one of the most crucial roles that software defect prediction can play. Of all the functions that software can perform, this is one of the most crucial ones. This literature review aims to offer a thorough examination of the research trends, methodologies, and frameworks utilized in the field of software defect prediction. This study analyzes a broad range of scholarly publications. These publications cover a wide variety of topics related to defect prediction, including dataset features, prediction models, assessment measures, and prediction approaches. Within the context of minimizing the negative consequences of defects on software quality and project schedules, the review emphasizes the significance of software defect prediction. This investigation identifies significant research themes such as the use of machine learning algorithms, feature selection approaches, and ensemble methods in defect prediction. The paper also scrutinizes the challenges and limitations associated with the diverse defect prediction methodologies currently in use. These include the imbalance of the dataset, the bias in feature selection, and the overfitting of the model. Additionally, it highlights the development of research fields and the opportunities for future study, such as the incorporation of domain knowledge, the incorporation of varied data sources, and the development of advanced approaches to predictive modeling. Furthermore, it acknowledges the existence of these opportunities. In its entirety, this literature review provides researchers and practitioners working in the field of software engineering with critical insights into the present state of the art in software defect prediction.

Deep ConvBi-LSTM: A Robust 3D Room Layout Estimation Model for Indoor Environment

2024-09-02T11:03:33+00:00

Room layout estimation is importance in recent times due to its extended application area. This process is highly challenging due to several factors affecting the room image such as clutter, occlusions, illuminations, etc. It is important to accurately identify the 3D layout of the room from a single 2D room image. The available techniques focused on determining the 3D layout but with limited number of features. It is important for a model to be fed with large number of features to result in successful predictions. To this extent, the proposed model introduced a robust 3D layout estimation framework for indoor environment. Initially, the input image is pre-processed and then subjected to layout estimation where our proposed model predicted both the edge maps and semantic labels for the image. For prediction, the proposed framework utilized the Deep ConvBi-LSTM model and a score function is defined and maximized by remora optimization algorithm (ROA) to obtain the optimal 2D layout from the candidate set. Finally, the 3D output is reconstructed from the 2D layout based on the layout coordinates and camera orientations. The experimental results of the proposed model proved the efficiency of the model in providing the desired performance.

Deep Transfer Learning for Masked Face Reconstruction and Hybrid DCNN-ELM Framework for Recognition

2024-09-05T05:22:52+00:00

Facial reconstruction has always been a pivotal aspect of medical and forensic science. The increasing use of face masks in recent years has posed new challenges, making traditional facial recognition techniques less effective. To address this, our research explored innovative methods for reconstructing faces from images obscured by masks. We focused on post mask face reconstruction and facial recognition using cutting-edge techniques. We assess the effectiveness of three key unmasking algorithms: edgeconnect (EC), gated convolution (GC), and hierarchical variational vector quantized autoencoders (HVQVAE). Using two synthetic face datasets, MaskedFace-CelebA and MaskedFace-CelebAHQ, we rigorously evaluate the quality of the reconstructed faces based on metrics such as the PSNR, SSIM, UIQI, and NCORR. Among these, the Gated Convolution algorithm stands out as the superior choice in terms of image quality. For facial recognition, we employ a novel hybrid framework that combines a deep convolutional neural network and an extreme learning machine (DCNN-ELM). We tested five classifiers (Vgg16, Vgg19, ResNet50, ResNet101, and ResNet152) in combination with ELM and a support vector machine (SVM). Our comprehensive ablation study revealed that ResNet152 combined with ELM achieved the best performance, with a facial recognition accuracy of 60.9%, suggesting that the reconstructed faces were of high quality. Our paper presents a novel approach to image classification utilizing five classifiers within the DCNN+ELM hybrid framework and provides a complete ablation study of these classifiers. This research underscores the importance of face reconstruction in the current field and its potential to enhance facial recognition techniques.

Advancements in Natural Language Processing: Enhancing Machine Understanding of Human Language in Conversational AI Systems

2024-09-07T06:02:24+00:00

This paper aims to review the recent developments in Natural Language Processing and their implications to the improvement of current comprehension in conversational AI interfaces. The comparison of four leading NLP models, namely BERT, GPT, T5 and XLNet is carried out systematically with respect to the major tasks of conversational interfaces including text generation, sentiment analysis and question answering. Based on the performance predicate that I used, it is clear that BERT has a 91% accuracy level. 5%, GPT 88. 2%, T5 89. 6%, and XLNet 90. 3%. Precision scores were precise to the second decimal place for BERT at 92. of reserves as 1%, while keeping GPT at 87%. 9%, T5 at 88. 5%, while last comes the averagely performing XLNet at 89. 7%. On the aspect of recall rates, BERT had a slightly better performance at 90.8%, GPT at 86. 5%, T5 at 87. ; 87% BERT, 88% RoBERTa, 89% XLNet. 2%. The recognized F1-scores were as well highest in BERT where it obtained 91. 4%, and XLNet at 89%. 5%, T5 at 88. Metcash Group Ltd at 17, Coles Group at 28, Woolworths at 10, Metcash Ltd at 6% and GPT at 87. 7%. This paper shows that BERT surpasses GPT-T5 in terms of accuracy and precision; however, GPT and T5 are better suited for text generation applications. These models contain theoretical and practical value in analyzing their advantages and drawbacks, which makes the base for choosing the suitable tools of NLP for definite uses and developed future conversational AI appliances.

Next-Generation Wireless Communication: Exploring the Potential of 5G and Beyond in Enabling Ultra-Reliable Low Latency Communications for IOT and Autonomous Systems

2024-09-07T06:16:56+00:00

The current study aims at exploring the development of wireless communication technologies especially 5G and the future 6G to deliver ULLC for IoT and auto-mobiles. By the use of simulation models and real-life examples the research assesses gains resulting from these next generation networks. The outcome indicates that 5G network means a set of numerous improvements as compared with the previous technologies and it possesses the latency of 1. 2 milliseconds and the throughput of 10 Gbps. In the future, 6G technologies have been expected to increase performance even more as the forecasted latency of 0. 8 milliseconds, packet loss rates getting down to around 0. 01%, and throughput which could go to up to 15 Gbps. The study also presents artificial intelligence, the edge computing system, and other high-advanced beam-forming technologies that assist in enhancing network performance and dependability. Another actual example showed how 5G can be used in the control of traffic, which reached a latency of 1. 1 millisecond with the reliability rate of more than 99 %. 98%. Essentially, these research discoveries indicate how next generation wireless networks may revolutionize key applications as well as progress the way toward more reliable connections.

Tax Transparency Moderates the Effect of Green Supply Chain and Green Accounting on Corporate Reputation and its Impact on Financial Performance Risk

2024-09-07T06:43:55+00:00

A company's reputation is influenced by its adherence to good or bad business ethics. Tax avoidance is a decision that reflects poor business ethics. Management's efforts to enhance tax transparency signal to investors that the company upholds strong ethical standards by being transparent about its taxes, which helps reduce tax avoidance. This study examines the impact of green supply chains and green accounting on corporate reputation and how these factors influence financial performance risk, with tax transparency serving as a moderating factor. The study uses a sample of 658 companies over a two-year period, selected through purposive sampling, and applies moderation regression analysis. The findings support three hypotheses and reject two, indicating that the green supply chain positively affects corporate reputation, and tax transparency strengthens the relationship between the green supply chain and green accounting with corporate reputation. The practical implication is that green supply chains can serve as a strategy to enhance corporate reputation, and tax transparency can reinforce corporate environmental policies, further improving corporate reputation.

Machine Learning Based Framework for Unmasking Bogus Reviews in Online Shopping

2024-09-07T06:59:50+00:00

This research introduces a robust machine learning framework that utilizes the K-Nearest Neighbors (KNN) algorithm to detect fake reviews in Amazon product feedback. The model capitalizes on KNN's ability to assess the proximity of data points, integrating a diverse range of features derived from the textual content, temporal patterns, and contextual elements of reviews. By thoroughly analyzing these features, the model is able to identify subtle discrepancies that distinguish genuine feedback from deceptive ones. Rigorous validation on real-world datasets demonstrates the model's high accuracy in detecting fake reviews, while also maintaining a balance between effectiveness and computational efficiency. The model's design ensures it is adaptable across various product categories and scales well within Amazon's vast ecosystem, addressing the complexities of diverse product offerings. Furthermore, the approach is engineered to be resilient against evolving deceptive tactics and variations across different regions and time periods, showcasing its robustness and long-term applicability. The study highlights the importance of adopting KNN-based methodologies as a critical tool in the ongoing battle to preserve the integrity of online feedback systems. By enhancing the reliability of reviews, this framework empowers consumers with trustworthy information, enabling them to make informed purchasing decisions. The findings of this research advocate for the broader implementation of KNN-driven approaches to fortify consumer trust and ensure the credibility of e-commerce platforms.

Wireless Energy Harvesting (Weh) and Spectrum Sharing In Cognitive Radio Networks

2024-09-07T10:27:44+00:00

It is detailed how one possibility exists for making use of wireless energy in the context of a decode-and-forward relay-assisted secondary user (SU) network that functions according to the guidelines of a cognitive spectrum sharing paradigm. The maximum power that the source and relay in the SU network can transmit from the harvested energy, the peak interference power from the source and relay in the SU network at the primary user (PU) network, and the interference power of the PU network at the relay-assisted SU network are the power constraints that were used to derive an expression for the outage probability of the relay-assisted cognitive network. According to the findings of the research, a relay-assisted network that makes use of the recommended wireless energy harvesting protocol has the potential to operate with an outage probability that is less than 20% for particular applications that take place in the real world. The performance limitation that was placed on the primary system is what is utilised to establish the optimisation challenge that has to be met in order to maximise the area throughput of the secondary system. After analysing the performance of the system with the help of the stochastic geometry theory, we developed a method that evenly distributes the available bandwidth and time resources in such a way as to make it possible for electromagnetic transmission as well as data transfer. Data on performance are provided to highlight how the various system parameters interact with one another and to assist with our theoretical research.