Gas Sensor Array Drift in an E-Nose System: A Dataset for Machine Learning Applications

Gas sensor arrays are widely used in various applications such as environmental monitoring, industrial process control, and medical diagnosis. However, one of the main challenges in using gas sensor arrays is their tendency to drift over time, which can significantly affect their accuracy and reliability. In this research paper, we present a gas sensor array drift dataset that can be used to evaluate and develop drift compensation techniques. The dataset consists of measurements from an array of eight metal oxide gas sensors exposed to six different target gases at varying concentrations over several months. The paper also describes the experimental setup, data acquisition process, and preliminary dataset analysis. Our results show that the sensor array exhibits significant drift over time and that the drift patterns vary depending on the target gas and concentration. This dataset can provide a valuable resource for researchers and engineers working on gas sensor array applications and can help advance the development of more robust and accurate gas sensing systems

GRASE: Granulometry Analysis with Semi Eager Classifier to Detect Malware

Technological advancement in communication leading to 5G, motivates everyone to get connected to the internet including ‘Devices’, a technology named Web of Things (WoT). The community benefits from this large-scale network which allows monitoring and controlling of physical devices. But many times, it costs the security as MALicious softWARE (MalWare) developers try to invade the network, as for them, these devices are like a ‘backdoor’ providing them easy ‘entry’. To stop invaders from entering the network, identifying malware and its variants is of great significance for cyberspace. Traditional methods of malware detection like static and dynamic ones, detect the malware but lack against new techniques used by malware developers like obfuscation, polymorphism and encryption. A machine learning approach to detect malware, where the classifier is trained with handcrafted features, is not potent against these techniques and asks for efforts to put in for the feature engineering. The paper proposes a malware classification using a visualization methodology wherein the disassembled malware code is transformed into grey images. It presents the efficacy of Granulometry texture analysis technique for improving malware classification. Furthermore, a Semi Eager (SemiE) classifier, which is a combination of eager learning and lazy learning technique, is used to get robust classification of malware families. The outcome of the experiment is promising since the proposed technique requires less training time to learn the semantics of higher-level malicious behaviours. Identifying the malware (testing phase) is also done faster. A benchmark database like malimg and Microsoft Malware Classification challenge (BIG-2015) has been utilized to analyse the performance of the system. An overall average classification accuracy of 99.03 and 99.11% is achieved, respectively

Educational Aspirations as The Predictors of Teacher Engagement in Classroom in Context of Emotional Intelligence of Teachers

In the intricate network of factors that influence teacher engagement within the classroom, educational aspirations stand as potential critical predictors. These aspirations, which could be defined as the goals and objectives set by educational institutions or by the teachers themselves, could serve as a guiding force, steering the level and quality of engagement teachers display in the class. This essay explores the hypothesis that educational aspirations could indeed be potent predictors of teacher engagement, delineating the pathways through which aspirations mold teachers' dedication, enthusiasm, and proactive involvement in the educational process. Initially, it is fundamental to understand that educational aspirations are a two-pronged entity - encompassing the macro-level aspirations set forth by educational boards and policies, and the micro-level aspirations nurtured by individual teachers for their personal growth and the academic progression of their students. These aspirations, when clearly articulated and aligned, can act as a catalyst, propelling teachers towards higher levels of engagement in class. From one perspective, aspirations can foster engagement by instilling a sense of purpose and direction. When teachers are driven by well-defined goals - be it enhancing student literacy levels, fostering critical thinking skills, or nurturing holistic development - their engagement in class is naturally heightened. They are more likely to invest time and effort in devising innovative teaching strategies, engaging in continuous learning, and fostering an environment that is conducive to achieving these aspirations. Furthermore, educational aspirations often encapsulate the ideals of inclusive and equitable education. Teachers working towards these aspirations are likely to be more engaged, as they strive to create classrooms where every student is given an opportunity to thrive. This inclusive approach not only fosters positive student outcomes but also enriches the teacher's experience, as they find fulfillment and satisfaction in witnessing the growth and development of their students. Moreover, educational aspirations can foster a collaborative spirit among teachers. As they work towards common goals, there is an enhanced sense of community and collaboration

Transparency in Algorithmic Decision-making: Interpretable Models for Ethical Accountability

Concerns regarding their opacity and potential ethical ramifications have been raised by the spread of algorithmic decisionmaking systems across a variety of fields. By promoting the use of interpretable machine learning models, this research addresses the critical requirement for openness and moral responsibility in these systems. Interpretable models provide a transparent and intelligible depiction of how decisions are made, as opposed to complicated black-box algorithms. Users and stakeholders need this openness in order to understand, verify, and hold accountable the decisions made by these algorithms. Furthermore, interpretability promotes fairness in algorithmic results by making it easier to detect and reduce biases. In this article, we give an overview of the difficulties brought on by algorithmic opacity, highlighting how crucial it is to solve these difficulties in a variety of settings, including those involving healthcare, banking, criminal justice, and more. From linear models to rule-based systems to surrogate models, we give a thorough analysis of interpretable machine learning techniques, highlighting their benefits and drawbacks. We suggest that incorporating interpretable models into the design and use of algorithms can result in a more responsible and moral application of AI in society, ultimately benefiting people and communities while lowering the risks connected to opaque decision-making processes