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In this paper, an intrusion detection system is introduced that uses data mining and machine learning concepts to detect network intrusion patterns. In the proposed method, an artificial neural network (ANN) is used as a learning technique in intrusion detection. The metaheuristic algorithm with the swarm-based approach is used to reduce intrusion detection errors. In the proposed method, the Grasshopper Optimization Algorithm (GOA) is used for better and more accurate learning of ANNs to reduce intrusion detection error rate. The role of the GOAMLP algorithm is to minimize the intrusion detection error in the neural network by selecting useful parameters such as weight and bias. Our implementation in MATLAB software and using the KDD and UNSW datasets show that the proposed method detects abnormal, malicious traffic and attacks with high accuracy. The GOAMLP method outperforms and is more accurate than the existing state-of-the-art techniques such as RF, XGBoost, and embedded learning of ANN with BOA, HHO, and BWO algorithms in network intrusion detection.

This research-to-practice work-in-progress addresses a new approach to cybersecurity education. The cyber security skills shortage is reaching prevalent proportions. The consensus in the STEM community is that the problem begins at k-12 schools with too few students interested in STEM subjects. One way to ensure a larger pipeline in cybersecurity is to train more high school teachers to not only teach cybersecurity in their schools or integrate cybersecurity concepts in their classrooms but also to promote IT security as an attractive career path. The proposed research will result in developing a unique and novel curriculum and scalable program in the area of cybersecurity and a set of powerful tools for a fun learning experience in cybersecurity education. In this project, we are focusing on the potential to advance research agendas in cybersecurity and train the future generation with cybersecurity skills and answer fundamental research questions that still exist in the blended learning methodologies for cybersecurity education and assessment. Leadership and entrepreneurship skills are also added to the mix to prepare students for real-world problems. Delivery methods, timing, format, pacing and outcomes alignment will all be assessed to provide a baseline for future research and additional synergy and integration with existing cybersecurity programs to expand or leverage for new cybersecurity and STEM educational research. This is a new model for cybersecurity education, leadership, and entrepreneurship and there is a possibility of a significant leap towards a more advanced cybersecurity educational methodology using this model. The project will also provide a prototype for innovation coupled with character-building and ethical leadership.

Data mining, the discovery of knowledge from data, bridges several disciplines such as database management, artificial intelligence, statistics, visualization and the domain of the data, e.g., biology or engineering. Knowledge discovered by mining the data can be used for various purposes such as developing decision support systems and intelligent tutors. In this paper we present such a data mining problem in the mechanical engineering domain where knowledge discovery from the data is performed using statistical approaches, to conduct predictive analysis for decision support. More specifically, we focus on the engine health problem which consists of using existing data on the behavior of an engine in order to predict whether the engine is capable of functioning well (i.e., it is healthy) and to offer suggestions on preventive maintenance. The data we use for this predictive analysis consists of graphs that plot process parameters such as the vibration and temperature of the engine with respect to time. In this paper we define the problem in detail, propose a solution based on statistical inference techniques, summarize our experimental evaluation and discuss the applications of this work in various fields from a decision support angle.

In this article an approach to surface image quality assessment for surface pattern and object recognition, classification, and identification has been described. The surface quality assessment finds many industrial applications such as auto-mated, advanced, and autonomous manufacturing processes. Given that in most industrial applications the target surface is an unknown variable, having a tool to measure the quality of the surface in real time has a significant value. To add to the complication, in most industrial applications, the surface (and therefore its image) suffers from several physical phenomena such as noise (of several different kinds), time, phase, and frequency shifts, and other clutter caused by interference and speckles. The proposed tool should also be able to measure the level of deterioration of the surface due to these environmental effects. Therefore, evaluation of quality of a surface is not an easy task. It requires a good understanding of the processing methods used and the types of environmental processes affecting the surface. On the other hand, for a meaningful comparative analysis, some effective parameters have to be chosen and qualitatively and quantitatively measured across different settings and processes affecting the surface. Finally, any algorithm capable of handling these tasks has to be efficient, fast, and simple to qualify for the “real-time” applications.

Many wireless sensor network datasets suffer from the effects of acquisition noise, channel noise, fading, and fusion of different nodes with huge amounts of data. Any of these effects alone or their combination could adversely affect the decision made at the fusion center. We have developed computationally low power, low bandwidth, and low cost filters that will remove the noise and compress the data so that a decision can be made at the node level. This wavelet-based method is guaranteed to converge to a stationary point for both uncorrelated and correlated sensor data. Presented here is the theoretical background with examples showing the performance and merits of this novel approach compared to other alternatives.

This paper presents a successful lab simulation experience to teach signal modulation and demodulation concepts in communication and computer networks to computer science and computer engineering students. Two sections of the same college course with a total of 80 subjects participated in this study. After receiving the same lecture at the same time, the subjects in each course were randomly split into two treatment groups. One group completed two laboratory experiments using the computerized simulation program, while the other completed the same two laboratory experiments using the traditional physical laboratory equipments. Upon the completion of the laboratory assignments, the performance instrument was individually administered to each student. The groups were compared on understanding the concepts, remembering the concepts, and displaying a positive attitude toward the treatment tools. Scores on a validated Concepts Test were collected once after the treatment and another time after three weeks in order to gain some insight on students’ knowledge retention. The validated Attitude Survey and qualitative study was administered at the completion of the treatment. The findings of this research indicate that conceptual simulation programs could be feasible substitute for hands-on exercises.

In computational analysis in scientific domains, images are often compared based on their features, e.g., size, depth and other domain-specific aspects. Certain features may be more significant than others while comparing the images and drawing corresponding inferences for specific applications. Though domain experts may have subjective notions of similarity for comparison, they seldom have a distance function that ranks the image features based on their relative importance. We propose a method called features rank for learning such a distance function in order to capture the semantics of the images. We are given training samples with pairs of images and the extent of similarity identified for each pair. Using a guessed initial distance function. Features rank clusters the given images in levels. It then adjusts the distance junction based on the error between the clusters and training samples using heuristics proposed in this paper. The distance junction that gives the lowest error is the output. This contains the features ranked in the order most appropriate the domain. Features rank is evaluated with real image data from nanotechnology and bioinformatics. The results of our evaluation are presented in the paper.

One of the goals of the National Cancer Institute (NCI) cancer control program to reach more than 80% of the eligible women in mammography screening by the year 2000 yet remains as a challenge. That is primarily due to 1) the fact that examination process is a complex and lengthy one and 2) it is not available to the majority of women who live in remote and urban sites. This problem can be solved using ad hoc networking technologies and signal processing algorithms. Software modules can help detect true negatives (TN), while marking true positives (TP) for further investigation. This reduces the load on network and radiologists by a tremendous amount. Ad hoc networking equipment makes clinic-lab connection more accessible and image enhancement algorithms readily available to the radiologists. In this research automatic robust computer assisted diagnostic (CAD) methods for mass detection in breast cancer have been associated with region of interest (ROI) compression for efficient transmission over ad hoc networks. This telemammography system for remote computer-aided breast cancer detection and diagnosis has the advantage of better reach of women for cancer screening and at the same time provides the diagnosis with higher efficiency, better accuracy, and lower cost.

The World Wide Web (WWW) provides alternative means for delivery of the courses and services, providing learners with an extraordinary range of options. There are few, if any, studies that have attempted to evaluate the learning achieved through the use of simulated laboratories in virtual environments, perhaps because of the difficulties in designing tight experiments for such studies. The purpose of this study would be to investigate the effectiveness of simulated labs in engineering and present the results. Specifically, this study examines whether computer simulations are as effective as physical laboratory activities in teaching college-level electronics engineering students about the concepts of signal transmission, modulation and demodulation. Two sections of the same college course with a total of 80 subjects participated in this study. After receiving the same lecture at the same time, the subjects in each course were randomly split into two treatment groups. One group completed two laboratory experiments using the computerized simulation program, while the other completed the same two laboratory experiments using the traditional physical laboratory equipments. Upon the completion of the laboratory assignments, the performance instrument was individually administered to each student. The groups were compared on understanding the concepts, remembering the concepts, and displaying a positive attitude toward the treatment tools. Scores on a validated Concepts Test were collected once after the treatment and another time after three weeks of the study. The validated Attitude Survey and qualitative study was administered at the completion of the treatment.

Technology is having a dramatic effect on colleges and universities, producing what may be the most challenging period in the history of higher education. One form of the convergence of technology and education is distributed learning. The World Wide Web (WWW) provides alternative means for delivery of the courses and services, providing learners with an extraordinary range of options. Distance learning methods are being developed and implemented to offer educational opportunities to those who are unable or choose not to attend an educational institution on a conventional, scheduled basis. There are few, if any, studies that have attempted to evaluate the learning achieved through interactivity of students with the online contents using interactive multimedia in online environments, perhaps because of the difficulties in designing rich multimedia contents, a tight experiment for such studies and the fact that confounding variables can easily become significant. The purpose of this study would be to investigate the effectiveness of learning through interactive multimedia and present the results. This research report provides information on the design and development of an online Computational Method in Engineering Technology course. The course is interactive multimedia-based including 3D animation, simulation, video, graphics, and audio. The research considers information on the theoretical framework for designing the course, the process of developing the multimedia and developing the course. The results of the study on the effectiveness of the course is reported in the presentation. Major points of emphasis studied in this research include: 1. Discussing the need for multimedia-rich courses for teaching technology-based subjects in online environments; 2. Developing multimedia based online courses; 3. The effects of using multimedia in teaching technology-based concepts; 4. The results of the study on the effects of using multimedia in an online environment and 5. Results of student attitudes toward learning through multimedia.

One of the goals of the National Cancer Institute (NCI) cancer control program to reach more than 80% of the eligible women in mammography screening by the year 2000 yet remains as a challenge. That is primarily due to 1) the fact that examination process is a complex and lengthy one and 2) it is not available to the majority of women who live in remote and urban sites. This problem can be solved using ad hoc-networking technologies and signal processing algorithms. Software modules can help detect true negatives (TN), while marking true positives (TP) for further investigation. This reduces the load on network and radiologists by a tremendous amount. Ad hoc networking equipment make clinic-lab connection more accessible and image enhancement algorithms readily available to the radiologists. In this research automatic robust computer assisted diagnostic (CAD) methods for mass detection in breast cancer have been associated with region of interest (ROI) compression for efficient transmission over ad hoc networks. This telemammography system for remote computer-aided breast cancer detection and diagnosis has the advantage of better reach of women for cancer screening and at the same time provides the diagnosis with higher efficiency, better accuracy, and lower cost.