As cloud computing becomes foundational to sectors like healthcare, finance, and artificial intelligence, accurate resource utilization forecasting has emerged as a critical challenge for ensuring efficiency, cost-effectiveness, and service reliability. This research introduces Runtime-Assembled Context-Specific Cooperative Artificial Neural Networks (RACANN), a novel modular neural framework designed to predict cloud resource utilization with improved interpretability and efficiency. Unlike monolithic deep learning models, RACANN dynamically assembles lightweight context-specific neural agents at runtime, enabling fine-grained temporal adaptability and significantly reducing computational overhead. We further propose eRACANN, an explainable extension that embeds a fuzzy logic-based linguistic layer, offering human-readable justifications for predictions. Experimental results across both structured and noisy real-world datasets demonstrate that while deep neural networks (DNNs) may achieve marginally lower error rates, eRACANN excels in modularity, interpretability, and contextual transparency which are critical properties for operational deployment in dynamic, mission-critical cloud environments. The RACANN framework offers a scalable path toward explainable, adaptive, and resource-efficient AI for cloud infrastructure management.

The triumph of cloud computing hinges upon the adept instantiation of infrastructure and the judicious utilization of available resources. Load balancing, a pivotal facet, substantiates the fulfillment of these imperatives, thereby augmenting the performance of the cloud environment for its users. Our research introduces a load balancing algorithm grounded in threshold principles devised to ensure equitable distribution of workloads among nodes. The main objective of the algorithm is to preclude the overburdening of virtual machines (VMs) within the cloud with tasks or their idleness due to task allocation deficiencies in the presence of active tasks. The threshold values embedded in our algorithm ascertain the judicious deployment of VMs, forestalling both task overload and idle states arising from task allocation inadequacies. Simulation outcomes manifest that our threshold-based algorithm markedly enhances response time for tasks/requests and data processing duration within datacenters, outperforming extant algorithms such as First Come First Serve, Round Robin, and the Equally Spread Current Execution Load Balancing algorithm. Our threshold algorithm attains superior results to alternative load balancing algorithms when coupled with an optimized response time service broker policy.

This innovative practice full paper describes a novel twofold approach to analyze the data collected during a six-week research-based summer professional development workshop for middle and high school STEM teachers in Southwest Missouri. In the fast-evolving field of Computer Science (CS), particularly within the Internet of Things (IoT) domain, the demand for a skilled workforce is increasing. To meet this demand, it is crucial to provide K-12 students with education in computing, computational thinking, and other broader Science, Technology, Engineering, and Mathematics (STEM) disciplines. The existing STEM curriculum could be enhanced to develop skills such as research-based problem-solving more effectively, aiming for a more comprehensive skill set among students. Therefore, empowering STEM teachers with a solid foundation of research-based problem-solving skills can significantly boost their readiness for the classroom, thus enriching their students' educational experiences. The Research Opportunity for Smart Environments (ROSE) program, a three-year initiative funded by the National Science Foundation (NSF), aims to prepare middle and high school STEM teachers to effectively introduce CS concepts through innovative IoT applications in Smart Environments, including Smart Homes and Smart Classrooms. To support STEM education in rural and other underrepresented areas, a cohort of in-service teachers from rural Southwest Missouri was selected for the inaugural ROSE summer workshop. Our data collection approach included open-ended questions and effective formative assessment techniques to gather the teachers' perspectives during the initial summer session. We adopted a twofold data analysis strategy, using the qualitative coding tool MAXQDA and sentiment analysis tools Valence Aware Dictionary and sEntiment Reasoner (VADER) and TextBlob, to analyze the teachers' responses. The research questions explored the impact of the ROSE program on educators and how their experiences and outlooks influenced their engagement and professional development within the program. The findings indicate that the ROSE program has positively influenced the teachers' personal and professional growth. The teachers experienced increased confidence and knowledge in research and teaching CS concepts, despite facing various challenges, which acted as motivation for learning. Their reflections also indicated that the mentorship and resources provided by the ROSE program have promoted the development of innovative teaching methods and a shift towards a student-centered approach, showcasing the program's success in fostering the personal and professional development of teachers for the advancement of future CS and STEM professionals.

Cloud computing has become one of the most influential technologies in the computer science field. In the modern-day world, people are dependent on cloud services in every aspect of life. Cloud services process a vast number of user requests through various data centers. So often, data center selection plays a vital role in user satisfaction and the success of cloud services. Researchers are working relentlessly to use the behaviors of nature to solve real-world problems. In this paper, we used swarm intelligence to find optimal data centers for userbases. The swarm intelligence algorithms use their experience and knowledge of their neighbors to direct the algorithm toward an optimal solution. We have designed a particle swarm optimization-based data center selection policy inspired by swarm intelligence. To explore the accuracy and performance of the algorithm, we simulated it with different real-world scenarios using CloudAnalyst. The simulation results of response time and data processing time of the cloud environment exhibit that the proposed data center selection policy outperforms the traditional data center policy, such as optimized response time and closest data center policy.

Cloud computing has become an emerging trend for the software industry with the requirement of large infrastructure and resources. The future success of cloud computing depends on the effectiveness of instantiation of the infrastructure and utilization of available resources. Load Balancing ensures the fulfillment of these conditions to improve the cloud environment for the users. Load Balancing dynamically distributes the workload among the nodes in such a way that no single resource is either overwhelmed with tasks or underutilized. In this paper we propose a threshold based load balancing algorithm to ensure the equal distribution of the workload among the nodes. The main objective of the algorithms is to stop the VMs in the cloud being overloaded with tasks or being idle for lack allocation of tasks, when there are active tasks. We have simulated our proposed algorithm in the Cloudanalyst simulator with real world data scenarios. Simulation results shows that our proposed threshold based algorithm can provide a better response time for the task/requests and data processing time for the datacenters compared to the existing algorithms such as First Come First Serve (FCFS), Round Robin(RR) and Equally Spread Current Execution Load Balancing algorithm(ESCELB).

We present a novel approach for audio steganography that preserves first-order statistical properties of cover audio after embedding a secret message. This approach can avoid detection by histogram-based or similar statistical attacks. This approach partitions the audio samples in the cover audio, which is followed by reordering of the samples in each partition through an encoding process for embedding the secret message. Partitioning of samples is governed by a specified error limit on individual samples, and the error limit is determined from signal-to-noise ratio that needs to be maintained in the stego audio to avoid detection by an automated system or human auditory system. Experimental results on effectiveness as well as on capacity are presented using 8-bit and 16-bit audio as covers. It is shown that the proposed approach can achieve high capacity while maintaining its effectiveness against attacks.

Prediction of protein secondary structure (alpha-helix, beta-sheet, coil) from primary sequence of amino acids is a very challenging task, and the problem has been approached from several angles. Previously research was performed in this field using several techniques such as neural networks, simulated annealing (SA) and genetic algorithms (GA) for improving the protein secondary structure prediction accuracy. Decision fusion methods such as the committee method and correlation methods were also used in combination with the profile-based neural networks and AI algorithms for achieving better prediction accuracy. In this research we investigate the Bayesian inference method for predicting the protein secondary structure. The Bayesian inference method proposed in this research uses the results from the committee and correlation methods to achieve better prediction accuracy. Simulations are performed using the RS126 data set. The results show that the protein secondary structure prediction accuracy can be improved by more than 2% using the Bayesian inference method.

Optical multistage interconnection networks (MINs) suffer from optical-loss during switching and crosstalk problem in the switches. The crosstalk problem is solved by routing messages using time division multiplexing (TDM) approach. This paper focuses on minimizing the number of groups (time slots) required to realize a permutation. Many researchers concentrated on this NP-hard problem and concluded that AI algorithms perform better than the heuristic algorithms. They also showed that majority of the times the performance of genetic algorithm (GA) was better than simulated annealing algorithm (SAA). In this research, we implement a new approach to minimize the number of passes required for scheduling a given permutation. A combinational method is developed which comprises the use of Bayesian inference method on GA and SAA to always guarantee the best solution, instead of only using either GA or SAA. Simulations are performed in Java using multiple threads to run SA and GAA in parallel and to evaluate the performance of the new method. The results are then compared to those obtained from GA and SAA.

Summary form only given. Ant colony optimization (ACO) technique can be successfully implemented to solve many combinatorial optimization problems. In this paper we use the ACO technique to route messages through an N/spl times/N optical multistage interconnection network (OMIN) allowing up to 'C' limited crosstalk's (conflicts between messages within a switch) where 'C' is a technology driven parameter and is always less than log/sub 2/N. Messages with switch conflicts satisfying the crosstalk constraint are allowed to pass in the same group, but if there is any link conflict, then messages are routed in a different group. The focus here is to minimize the number of passes required for routing allowing up to 'C' limited crosstalks in an N/spl times/N OMIN. In this paper we show how the ACO technique can be applied to the routing problem, and its performance is compared to that of the degree-descending algorithm using simulation techniques. Finally the lower bound estimate on the minimum number of passes required is calculated and compared to the results obtained using the two algorithms discussed. The results obtained show that the ACO technique performs better than the degree-descending algorithm and is quite close to optimal algorithms to the problem.

Summary form only given. In this paper, a new class of optical multistage interconnection network (MIN) architecture is presented, which is constructed utilizing a modularization approach rather than the traditional recursive or fixed exchange pattern methods. We show that it has the best application flexibility and provides multicast function without imposing significant negative impacts to the whole network. A new multicast pattern is also proposed here, which makes it practical and economical to apply amplification in space-division networks. Compared with existing multicast architectures, this new architecture with Dilated Benes PTP modules has better performance in terms of system SNR, the number of switch elements, and system attenuation in point-to-point connections.