The splendid technological inventions supersede many traditional agricultural monitoring systems. In the last decade, a variety of new techniques and tools are proposed to monitor storage areas, which provide more safe and secure storage for different crops. The term storage area monitoring is supposed to check and avoid fire hazards, whereas numerous other hazards also need attention. One such hazard to cotton storage is spontaneous combustion, a process by which an element having comparatively low ignition temperature (hay, straw, peat, etc.) starts to relieve heat. In the presence of spontaneous combustion and lack of oxygen, if cotton catches any sparks from bales or physicochemical heat to ignite, the combustion can convert in to smoldering, and it can last up to several days without being discovered. Consequently, the actual fire occurs, cotton silently smoldering which not only affects cotton quality but also became the reason of big fire event. Many researchers propose valuable tools and techniques based on laboratory methods and modern techniques as well for detection and prevention of security hazards in storages. However, there is no standalone efficient tool/technique to monitor the storage area for spontaneous combustion. In current research, we propose an efficient wireless sensor network (WSN) and machine learning- (ML-) based storage area monitoring system for early prediction of spontaneous combustion in the cotton storage area. The WSN is used to collect real-time values from storage field by different combinations of sensors and send this over the network, where data is processed to identify spontaneous combustion and distribute the prediction results to the end user. The real-time data collection and ML-based analysis make the system efficient and reliable. The efficiency of the current system is verified by presenting two groups of cotton stored with different conditions. The results showed that the proposed system is able to detect spontaneous combustion well in time with a 95% accuracy rate.

The combustion of agricultural storage represents a big hazard to the safety and quality preservation of crops during lengthy storage times. Cotton storage is considered more prone to combustion for many reasons, i.e., heat by microbial growth, exothermic and endothermic reactions in storage areas, and extreme weather conditions in storage areas. Combustion not only increases the chances of a big fire outbreak in the long run, but it may also affect cotton’s quality factors like its color, staple length, seed quality, etc. The cotton’s quality attributes may divert from their normal range in the presence of combustion. It is difficult to detect, monitor, and control combustion. The Internet of Things (IoT) offers efficient and reliable solutions for numerous research problems in agriculture, healthcare, business analytics, and industrial manufacturing. In the agricultural domain, the IoT provides various applications for crop monitoring, warehouse protection, the prevention of crop diseases, and crop yield maximization. We also used the IoT for the smart and real-time sensing of spontaneous combustion inside storage areas in order to maintain cotton quality during lengthy storage. In the current research, we investigate spontaneous combustion inside storage and identify the primary reasons for it. Then, we proposed an efficient IoT and machine learning (ML)-based solution for the early sensing of combustion in storage in order to maintain cotton quality during long storage times. The proposed system provides real-time sensing of combustion-causing factors with the help of the IoT-based circuit and prediction of combustion using an efficient artificial neural network (ANN) model. The proposed smart sensing of combustion is verified by a different set of experiments. The proposed ANN model showed a 99.8% accuracy rate with 95–98% correctness and 97–99% completeness. The proposed solution is very efficient in detecting combustion and enables storage owners to become aware of combustion hazards in a timely manner; hence, they can improve the storage conditions for the preservation of cotton quality in the long run. The whole article consists of five sections.

The patients with surgical and nonsurgical skin wounds shall maintain regular check on wounds in order to assure in‐time wound healing. There are numerous measures need to verify during wound checking, that is, wound appearance (wound color and size), wound environment. The wound appearance could verify by a number of clinical techniques presented by researchers of wound care domain, however measurement of wound environment shall fulfill by verification of different environmental factors as wound environment is composition of different factors which collectively formed wound internal environment and external environment. In current research, we presented a framework based on clinical decision support system for measurement of wound environment both internal and external. This framework contains different working module, which shall collaborate in order to check wound environment. We elaborated structural and implementation sketch of all these modules. The presented framework based on Internet of Things and machine learning algorithms to collect and analyze data effectively.

Clinical research of wound assessment focused on physical appearance of wound i.e. wound width, shape, color etc. Although, wound appearance is most crucial factors to influence healing process. however, apart from wound appearance other factors also contribute in healing process. Wound internal and external environment is one such factor that may show positive or negative impact on healing. Internet of things extensively popular during last decade, due to its heavy applications in almost all domains i.e. agriculture, health, marketing, banking, home etc. Therefore, in current research we proposed IoT based intelligent wound assessment system, for assessment of wound status and apply entropy and information gain statistics of decision tree to reflect status of wound assessment by categorization of assessment results in one of three class i.e. good, satisfactory or alarming. We implemented decision tree in MATLAB, in which we select ID3 algorithm for decision tree which based on entropy and information gain for the selection of best feature to split the tree. The efficient feature split of decision tree improved training accuracy rate and performance of decision tree.

Skin wounds either minor or chronic may heal up with different time durations. But, this time duration of healing could not be easily predicted as healing is affected by different factors, e.g., age, nutrition, medication, and surroundings. Despite these factors, wound characteristic also plays a role in the healing process. Wound characteristics include wound size, wound type, internal and external wound environment, body temperature, body oxygenation, wound hydration, and infection. Therefore, monitoring of wound healing also required careful consideration of wound characteristics. Although the healthcare domain contains many applications for detection and monitoring of diseases, the wound care domain requires efficient techniques and sensing systems for the identification of wound biomarkers such as temperature, blood pressure, oxygen, and infection status of wound using biosensors. In the current research, we provide a wound care solution based on a biosensor-based sensing system to measure basic biomarkers, considered as major wound characteristics, i.e., body temperature and body oxygenation, and design a fuzzy inference system to predict their effect on wound hydration, which ultimately recommends necessary actions to boost healing.

There are many factors that may have a significant effect on the skin wound healing process. The environment is one of them. Although different previous research woks have highlighted the role of environmental elements such as humidity, temperature, dust, etc., in the process of skin wound healing, there is no predefined method available to identify the favourable or adverse environment conditions that seriously affect (positively or negatively) the skin wound healing process. In the current research work, an IoT-based approach is used to design an AQSS (Air Quality Sensing System) using sensors for the acquisition of real-time environment data, and the SVM (Support Vector Machine) classifier is applied to classify environments into one of the two categories, i.e., “favourable”, and “unfavourable”. The proposed system is also supported with an Android application to provide an easy-to-use interface. The proposed system provides an easy and simple means for patients to evaluate the environmental parameters and monitor their effects in the process of open skin wound healing.

Process model play a vital role in structure analysis of system as it can identify system’s classes, their attributes, operations (or methods), and the relationships among objects. These class models can be go together with state diagrams or UML state machine to identify the behavior of systems. Currently web is huge source of information which contain huge amount of data. Semantic Web idea gain popularity by its efficient manner of data representation over the web. Key concept of semantic web is based on common medium for data representation making data more flexible and support data interoperability. Semantic web stores data in ontology, which provide description of data for domain. Ontology can be written by using different standard languages i.e. XML, RDF, RDFs. RDF give semantic representation of data by showing data as Resources and their associated properties, relations Schema is vocabulary which provide description about classes and properties of RDF resources. RDF file can be manipulated by user for extraction of information in different ways according to requirement of user. In our research we proposed an APACHE Jena based methodology which take RDF file of web information system as input and convert it in to object oriented process model to illustrate structural representation of we system. Therefore “Consuming RDF Schemas for extraction of information in different required manners” is root cause of current research.

Software development life cycle is systematic way of developing software which describes phases of the software development and the sequence in which these phases are executed. Each phase generates deliverables required by the next phase in the life cycle. Requirements are translated into design. Code is produced according to the design which is called development phase. After coding and development the testing verifies the deliverable of the implementation phase against requirements. Almost all software development models contain software testing as one phase but testing require at each phase of SDLC. Different testing technique can apply on different phases based on software quality attributes. Software testing majorly categorized in to functional and structural testing each of them focus on different aspect of software. Both play vital role in software development life cycle by assuring software quality, each of them can use to measure different quality attributes. Since, both structural and functional testing faces a lot of challenges during manual conduction .Focus of this research paper is to find out significance of structural testing in SDLC along with challenges of structural testing methodology “DD path testing” in manual environment and suggest suitable solution to face these challenges. Suggested solution describes which steps involve in DD path testing and how they can automate. Subject Categories and Descriptors D.2.5 [SoftwareTesting and Debugging] D.2.9 [Management]: Software Life cycle General Terms: Software Testing, SLDC Automated DD-path Testing and its Significance in SDLC Phases

Testing process ensures proper working of software. However, major hurdles during this process occur due to manual handling of a lot of overhead of software testing. Since software testing process is majorly categorized into functional and structural testing, each of them focuses on different aspect of software. Both structural and functional testing faces a lot of challenges during manual conduction. Focus of this research paper is to find out challenges of structural testing methodology “DD path testing” in manual environment and suggest suitable solution to face such challenges. Suggested solution describes a number of steps involve in DD path testing and the way the particular steps can be automated.