International Journal of Innovations in Science & Technology
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Interfering Factors of Use of E-Commerce Toward Innovative Performance of SMEs by Moderating The Effects of E-commerce Marketing Capabilities
No full textThe performance of Small and Medium-sized Enterprises (SMEs), crucial to technological innovation within business and management, is a key obstacle to industrialization and creating a reaction to the changes. In the current study, the relationship between Technological, Organizational, and Environmental (TOE) aspects and the innovative performance of SMEs is mediated and moderated by the use of e-commerce and the efficiency of e-commerce marketing. In the present study, data were gathered through both face-to-face and online methods from proprietors and managers of SMEs operating in six prominent cities in Pakistan. Nearly 274 participants were randomly chosen to participate in the data collection. While 250 completed surveys were used for the analyses due to the unfinished survey report. The current study employed SPSS 25 to calculate the descriptive statistics, Smart Partial Least Square (PLS) 3.3.2 to analyze the data, and SEM to calculate the inferential statistics. The study\u27s findings indicate that the environmental component, the use of e-commerce, and the technology factor (technology readiness) are all positively correlated. Similarly, there is a negative association between organizational factors (adoption cost) and the use of e-commerce. In contrast, there is a positive relationship between the use of e-commerce and the innovative performance of SMEs (IP). The usage of e-commerce does not mediate adoption cost and innovative performance, but Technology Readiness (TR), Government Support (GS), and Innovative Performance (IP) do. The utilization of e-commerce and innovative performance and e-commerce marketing capabilities do not moderately correlate
Contemporary Study of Machine Learning Algorithms for Traffic Density Estimation in Intelligent Transportation Systems
Get PDFIntelligent Transportation Systems (ITS) provides the state-of-the-art real time integration of vehicles and intelligent systems. Collectively, the prospective of the technologies have capability to communicate between system users, roads, and infrastructure. This study presents a comprehensive examination of many applications and implications of AI and ML in the development of an ITS. The primary objective of this is to effectively mitigate the traffic congestion and enhance road safety measures to prevent accidents. Subsequently, we examined different machine learning methodologies employed in the identification of road traffic based on vehicles and their junctions with the purpose of evading impediments, as well as forecasting real-time traffic patterns to attain intelligent and effective transportation systems. The exponential growth of the population inside the country has resulted in a corresponding rise in the utilization of vehicles and various modes of transportation, thereby it needs a contributing to the exacerbation of traffic congestion and the occurrence of road accidents. Therefore, there exists a need for intelligent transportation systems that possess the capability to offer the dependable transportation services while simultaneously upholding environmental standards to overcome the traffic congestions. Designing accurate models for predicting traffic density is a crucial task in the field of transportation systems. This study compares the ML models which are derived using a variety of machine-learning approaches. Supervised machine learning algorithms, including Naive Bayes, Markov models, KNN, linear regression, and SVM, and KNN are employed. The conclusion result suggests that the Markov model achieves the highest level of accuracy, of 98%. Implementation of ITS with Markov Model provides the best performance in resilient environment
Investigating Deep Learning Methods for Detecting Lung Adenocarcinoma on the TCIA Dataset
Get PDFLung cancer, one of the deadliest diseases worldwide, can be treated, where the survival rates increase with early detection and treatment. CT scans are the most advanced imaging modality in clinical practices. Interpreting and identifying cancer from CT scan images can be difficult for doctors. Thus, automated detection helps doctors to identify malignant cells. A variety of techniques including deep learning and image processing have been extensively examined and evaluated. The objective of this study is to evaluate different transfer learning models through the optimization of certain variables including learning rate (LR), batch size (BS), and epochs. Finally, this study presents an enhanced model that achieves improved accuracy and faster processing times. Three models, namely VGG16, ResNet-50, and CNN Sequential Model, have undergone evaluation by changing parameters like learning rate, batch size, and epochs and after extensive experiments, it has been found that among these three models, the CNN Sequential model is working best with an accuracy of 94.1% and processing time of 1620 seconds. However, VGG16 and ResNet50 have 95.0% and 93% accuracies along with processing times of 5865 seconds and 9460 seconds, respectively
Action Recognition of Human Skeletal Data Using CNN and LSTM
Get PDFHuman action recognition recognizes an action performed by human beings in order to witness the type of action being performed. A lot of technologies have been developed in order to perform this task like GRN, KNN, SVM, depth maps, and two-stream maps. We have used 3 different methods in our research first method is a 2D CNN model, the second method uses an LSTM model and the third method is a combination of CNN+LSTM. With the help of ReLu as an activation function for hidden and input layers. Softmax is an activation function for output training of a neural network. After performing some epochs the results of the recognition of activity are declared. Our dataset is WISDM which recognizes 6 activities e.g., Running, Walking, Sitting, Standing, Downstairs, and Upstairs. After the model is done training the accuracy and loss of recognition of action are described. We achieved to increase in the accuracy of our LSTM model by tuning the hyperparameter by 1.5%. The accuracy of recognition of action is now 98.5% with a decrease in a loss that is 0.09% on the LSTM model, the accuracy of 0.92% and loss of 0.24% is achieved on our 2D CNN model while the CNN+LSTM model gave us an accuracy of 0.90% with the loss of 0.46% that is a stupendous achievement in the path of recognizing actions of a human. Then we introduced autocorrelation for our models. After that, the features of our models and their correlations with each other are also introduced in our research
Rock Fall Hazard and Risk Assessment using GIS Along Jaglot-Skardu Road, Pakistan
Get PDFRockfall is one of the major hazards all around the world. In Northern Pakistan, Jaglot-Skardu. The road is the main highway that connects KKH and Skardu, located in Gilgit Baltistan. The Area is very unique to study rock falls because of the variety of changes in the geological, seismological, and atmospheric conditions. The hazard and risk mapping of rock falls includes preparation of rock fall inventory map, susceptibility map and spatial analysis of Rock fall with its conditioning factors using GIS and Remote Sensing. The inventory map includes record of past rock falls along the road and prepared on hill shade map of the area using ArcGIS 10.4. Susceptibility maps of the area is generated using Weighted overlay technique. In Weighted overlay technique we use multi influencing factors of the rock falls as such as aspect, geology, slope, elevation, faults, curvature, Topographic wetness index, streams and road. Each map unit is than reclassified and assigned weight in weighted overlay to generate susceptibility map of area. In inventory map, almost 200 rock falls are marked and delineated on hill shade map of the area. The results of susceptibility show four zones i.e., low, moderate, high and very high hazard zones.The spatial analysis of rock falls showed that fault and geology is the main factor that are triggering the rock falls in the area. The areas which are present low and moderate susceptible zones are somehow safe and suitable for future planning and development and areas present in high to very high susceptible zones, large scale geotechnical investigations are required before any development and construction
Detection of Bronchitis Virus through Web-Based Interface and Management Strategies for Effective Control
Get PDFRNA viruses are distinguished by their quick adaptation to novel surroundings made possible by their high mutation and recombination rates. These viruses are responsible for the majority of newly identified illnesses and host transitions. Even well-known infections can be difficult to control due to their propensity for rapid evolution, which can impede our understanding of molecular epidemiology, reduce the sensitivity of diagnostic assays, reduce the efficiency of vaccines, and promote instances of immune escape. This scenario is consistent with the infectious bronchitis virus\u27s (IBV) past. The chicken industry has been aware of it since the 1930s, but it continues to be a major source of sickness and economic losses. Over the years, several different approaches have been tried and mostly unsuccessfully implemented to lessen its effects. However, they are rarely subjected to a fair and impartial assessment. Therefore, the pros and cons of IBV detection and control measures, and the efficacy of their execution, still mainly depend on the perspective of the observer. The purpose of this publication is to summaries the key aspects of IBV biology and evolution with an eye toward their diagnostic and preventative utility. Python based script has been developed for detection of Bronchitis virus
Geospatial Analysis of Land Fragmentation and Its Impact on Land Use of District Peshawar, Pakistan
Get PDFThe study analyzes how land fragmentation affects the use of the land in sample villages of Peshawar district. Globally, land is a primary source of productivity, yet the population is expanding at an alarming rate. This population growth has an effect on how land is acquired and used, which frequently results in the problem of land fragmentation. To meet the study\u27s goals, data were gathered from both primary and secondary sources including an intensive field survey using a questionnaire as well as land revenue department and population census organization. Out of a total of 279 villages two sample villages, namely village Ghalji Kander Khel and village Mathra were selected by random means for detailed and intensive study. During 1990-91 to 2020-21, fragmented land in sample villages increased. In village Ghalji Kander Khel fragmented land increased from 5.6% in 1990-91 to 23.9% in 2020-21 while in village Mathra fragmented land increased from 6.9% in 1990-91 to 27.1% in 2020-21 indicating an overall four-time increase during past two decades. The main cause of land fragmentation in sample villages is the Law of Inheritance, followed by population growth, market prices, financial difficulties, social issues, and government infrastructure. In sample villages, both area under cultivation and cultivable waste decreased out of which in village Ghalji Kander Khel cultivated land shrunk from 3478 kanal (1 kanal =506 m2) to 2194.1kanals and cultivable waste reduced from 31.1 to 25.4 kanal from 1990-91 to 2020-21. In village Mathra, cultivated land contracted from 5473.2 kanal in 1990-91 to 3443.94 kanal in 2020-21, and cultivable waste diminished from 117.81 kanal to 32.4 kanal. The built-up area enlarged from 802.4 kanal to 1298.1 kanal in Ghalji Kander Khel and from 1392.3 kanal to 1991.6 kanal in Mathra. Finally, it was revealed that most of the area under cultivation is transformed into other land uses. The conversion of cultivable waste to cultivable land took place on a very small scale
LectureBuddy: Towards Anonymous, Continuous, Real-time, and Automated Course Evaluation System
Get PDFStudent’s course evaluations are a primary tool for measuring teaching effectiveness. The traditional practice in course evaluation at most institutes is carried out once, at the end of each semester. The effectiveness of this system requires candid participation from the students, followed up by the administration, and the faculty. While corrective action took place behind the scenes over a long period, students never observed any immediate change(s) based on the feedback they were provided through the existing course evaluation systems. This discourages students from considering the evaluation seriously. In this paper, we investigate the need for an innovative system to replace the existing course evaluation systems. We conducted two separate surveys from 210 students and 67 teachers to gain insight into the existing course evaluation systems. The survey participants answered questions based on the tendency of feedback provided by students, method of teacher’s evaluations, frequency of evaluations conducted by institutes, and steps to make classrooms more interactive. We also conducted a comprehensive statistical analysis of the data collected from the surveys, both qualitative and quantitative. Our study showed a need for an innovative course evaluation system to continuously gather student feedback throughout the semester anonymously. These findings led us to develop the prototype of an innovative course evaluation system, “Lecture Buddy”, which is anonymous, continuous, real-time, and automated and which alleviates the shortcomings of the traditional course evaluation systems
Efficient Optimization of Adaptive Transmission Range in MANET - Maximizing Packet Delivery Ratio
No full textMobile Ad-hoc Network (MANET) is a self-systematized network, hasn’t fixed infrastructure and centralized administration system. Due to the frequent changes in network topology, MANET nodes are free to change locations anywhere they like.
Novelty statement: Typically, mobile devices in MANET are configured identically to have same transmission ranges, homogeneously. Previous research proves the optimum homogeneous transmission range that maximizes Packet Delivery Ratio (PDR). Mostly, it has been shown inversely proportional to the node density, and transmission range itself along with its PDR is not being studied. This study aims to show that instead of using an optimum homogenous transmission range for all mobile nodes, a non-homogenous scheme, where optimum transmission range for each node is computed separately.
Material and Method: In order to validate the study, simulations were performed on the network simulator NS3 with node ranges of 25, 50, and 100 over an area of 500 m2. Destination Sequences Distance Vector (DSDV) Protocol was selected to perform simulations in which each scenario was executed for 300 seconds (5 minutes).
Result and Discussion: The evaluation of results show that the maximum PDR can be achieved by computing a separate transmission range for each node as compared to the homogenous transmission ranges.
Concluding Remarks: In the end, it can be concluded that adaptive transmission ranges are optimally effective as compared to homogenous transmission range
Evolving Security Landscape of the Internet of Things: Assessing Advantages and Challenges
Get PDFIn light of the widespread integration of the Internet of Things (IoT), it is crucial for organizations to prioritize their attention towards establishing resilient system security. The presence of any vulnerability within a system has the potential to result in system failure or a cyberattack, hence causing significant repercussions on a wide scale. This encompasses a set of measures and protocols designed to safeguard against cyber threats that especially exploit vulnerabilities in physically interconnected IoT devices. The security teams responsible for managing IoT security are currently facing a range of challenges, including but not limited to inventory management, operational complexities, variety in IoT devices, ownership concerns, increasing data volumes, and emerging threats. This review provides a critical analysis of the existing body of research pertaining to the subject of security in the context of the IoT. The focus is mostly on the present state of affairs, practical implementations, and the issues that are associated with this domain. Moreover, it delves into the prospective prospects and opportunities that are anticipated in this particular domain. Lately, there has been a noticeable surge in interest among scholars hailing from diverse academic disciplines and geographical locations, all focusing on the improvement of internet network security. The assurance of data integrity, confidentiality, authentication, and authorization is imperative in light of the substantial volume of data that traverses network devices. Nevertheless, the field of IoT security exhibits significant potential for further development. The IoT has become a popular technology paradigm that facilitates the integration of diverse objects and systems. Yet, the extensive use of the IoT has generated apprehensions regarding security, specifically pertaining to the safeguarding of data and the integrity of networks