Academic literature on the topic 'Min-max normalization'

Detecting software vulnerabilities is essential for cybersecurity, particularly in Android systems, which are widely used and vulnerable due to their open-source nature. Conventional signature-based malware detection methods are inadequate against sophisticated and evolving threats. This paper introduces a Hyperparameter-Tuned Deep Learning Approach for Robust Software Vulnerability Detection (HPTDLA-RSVD) aimed at enhancing Android security through an optimized deep learning model. The HPTDLA-RSVD methodology encompasses min-max data normalization, feature selection using the Ant Lion Optimizer (ALO), classification via a Deep Belief Network (DBN), and hyperparameter optimization with the Slime Mould Algorithm (SMA). Experimental evaluations on a benchmark dataset reveal that HPTDLA-RSVD surpasses existing techniques across multiple performance metrics, confirming its efficacy in identifying and mitigating software vulnerabilities on Android platforms.

Sustainable housing is a popular topic with regard to the SDG, sustainable communities and Sustainable cities. Although different researches have come up with regard to different CSC of specific contexts there are very limited studies on CSC on Sustainable low-income housing. This research aims to compare the CSC on Sustainable low-income Housing in designing stage in Sri Lankan Context. 18 CSC were derived from comprehensive literature review and re-examined through the 27 professionals and ranked from community on three locations. Relative Importance Index- RII, Min Max Normalization and Gap analyses were employed in the ranking process of Critical Success Criteria. The highest importance has been ranked with Efficiency use of water and energy, Users Satisfaction and Quality of Housing while least importance is ranked with Maintainability, Public Consultation and community participation and cater for Disables and by Literature, Experts and Community respectively. Anyway, Public Consultation and Community Participation, newly derived CSC which is highly ranked among community is to be concentrated among the professionals for the attention and applications in practices. The findings of the research would support to the designers, architectures, planners specialized in this field to ensure the successful delivery of sustainable housing.

Given the limited resources available for installing advanced signal controllers, many researchers and professionals believe that well-designed coordinated fixed-time signal control, combined with well-defined corridor coordination, is a cost-effective option. In this study, two closely spaced intersections were considered for analysis. It was identified that a subsystem consists of spatial elements (the link road that connects two signalized intersections) and temporal elements (the relative offset between the signals of the two intersections). Throughput, travel time, delay, queue length, and flow efficiency were considered basic performance measurement parameters. The traffic flow analysis was conducted using the Vissim model, with calibration parameters adjusted to suit Sri Lankan conditions based on previous research. Twenty demand models were evaluated, with volume-to-capacity (v/c) ratios ranging from 0.2 to 0.9 across the two intersections. The traffic flow was analyzed under three scenarios: Scenario 1 consisted of 60% through traffic, 20% left-turn traffic, and 20% right-turn traffic. In Scenario 2, the traffic distribution was 60% through traffic, 30% left-turn traffic, and 10% right-turn traffic. Scenario 3 featured 60% through traffic, 10% left-turn traffic, and 30% right-turn traffic. The Vissim model's link road geometry was kept at 500 meters, and both junctions were identical. The east-west direction was treated as the coordinated direction with three lanes, while the north-south cross streets had two lanes. Base saturation flows and offset optimization were calculated using Akcelik’s (1981) method, and cycle timing was determined based on the Webster formula. The study extended its analysis to compare unsynchronized (Case 1) and synchronized (Case 2) models. Initially, travel time was plotted against throughput. Based on the Elbow method, three clusters were identified as the optimal cluster number and then K-means clustering was performed. Generally, Cluster 1 had low flow and moderate traffic, Cluster 2 had moderate to high flow but moderate travel time, and Cluster 3 had moderate flow and high travel time. When the mean values for each cluster were compared in the unsynchronized and synchronized models, signal synchronization improved all key metrics in each cluster, except for queue length on the link road in Cluster 3, and throughput and flow efficiency in Cluster 2. Based on the Independent-Samples T-Test p-value, statistically significant improvements were found in travel time, with an 8.74% improvement in Cluster 2 and a 14.34% improvement in Cluster 3. Delay improvements were 29% and 30% in Clusters 2 and 3, respectively. However, there was a 35.17% increase in queue length on the link road, suggesting that while flow efficiency and delays improved, congestion remained an issue. As a further performance check, the Min-Max Normalization and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods were used. Both methods ranked Cluster 3 as the highest performer, despite the increase in queue length. Finally, considering the Level of Service (LOS) criteria from the Highway Capacity Manual, Cluster 3 showed an improvement from LOS E and D to LOS C, whereas Cluster 2 showed an improvement from LOS C and B to LOS B and Cluster 1 remained at LOS B and A. The most notable result from this research is the identification of Cluster 3 as the best-performing cluster across multiple scenarios, while Cluster 2 provided moderate improvement and Cluster 1 showed no significant improvement. Therefore, this study suggests that to gain the benefits of synchronization, the intersections considered should initially fall within the range of Cluster 3 or Cluster 2. Further research is required to explore how intersection geometry and coordination direction affect synchronization.

As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and feature selection/reduction. These pre-processing techniques play an important role in training a neural network to optimize its performance. This research studies the impact of applying normalization techniques as a pre-processing step to learning, as used by the IDSs. The impacts of pre-processing techniques play an important role in training neural networks to optimize its performance. This report proposes a Deep Neural Network (DNN) model with two hidden layers for IDS architecture and compares two commonly used normalization pre-processing techniques. Our findings are evaluated using accuracy, Area Under Curve (AUC), Receiver Operator Characteristic (ROC), F-1 Score, and loss. The experimentations demonstrate that Z-Score outperforms no-normalization and the use of Min-Max normalization.