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Power transformer malfunctions cause significant power outages and, as a result, material damage. This critical issue must be resolved. Power transformers must be protected from various fault conditions and power system failure. Fuzzy logic is used in this study to detect and protect power system transformer faults. In the simulation system that diagnoses transformer faults and monitors its operating conditions, the fuzzy logic (FL) toolbox in MATLAB/ SIMULINK software was used. The input variables, current and rate of change of current with time, have been identified as "Error" and "Error-Dot" in the programme. The study's findings show that when the output response is zero, the current in the transformer is normal. This is obtained by injecting [0] and [0] input values into the system, resulting in a response of "6e(-017)," which is close to zero. If the output response is greater than zero, the transformer current is rising above normal and the protection scheme should be notified. This condition is achieved by giving the system input values of [-1.5] and [5] and expecting a response of "+5". If the response is less than zero, the transformer current is less than normal, and the protection scheme should be notified. The study concluded that electrical faults and mechanical failures in power transformers can be quickly identified using Fuzzy logic. Thus, power transformer damage is avoided by controlling relays more effectively and quickly. This has resulted in significant savings in maintenance and repair costs. It is assumed that the study was a success.

Advances in medical imaging technology continue to create new possibilities for the collection of medical data that are important in timely and accurate diagnosis, in monitoring progress, and in the treatment of various diseases and in medical research. The capabilities of the new skills arise mainly from the technologies depicted in the vivo interior of the human body. Thus the study of the morphology and function of the various organs and the detection of any pathogens is achieved in a very direct way. The "source imaging data" provided by them is important information, but their large number is constantly growing, but their nature also creates the need for further processing with the help of computers. The primary purpose of processing images is to use denoising that includes the elimination of noise due to technical errors and feature preservation. Following noise reduction, the image segment, i.e. the location or areas of interest in an image, is the central objective of the process. In addition, usually, the complexity of the data in large volumes and charts requires a lot of time to study and a lot of experience to do their interpretation correctly. Therefore, in many cases, its automation using machine learning seeks out the partitioning process, but also categorizes images, i.e. classifying an image or parts of an image into specific categories. In most applications, machine learning performance is better than conventional techniques.

Skin diseases consist of a wide range of ailments that affect the skin, including microbial infections, viral, fungal, allergies, epidermis malignancies, and parasitic diseases. In South-Asian countries like India, people don’t care much about skin conditions. In our country, people prefer home remedies to cure skin conditions instead of visiting a dermatologist which can lead to serious skin conditions. Early diagnosis of skin disease is very important as it can reduce the severity of the condition. Melanoma is the deadliest type of skin cancer and the most prominent form of cancer. Melanoma could be diagnosed early, which would reduce overall illness and death. The odds of dying from the ailment are proportional to the extent of the malignancy, which is proportional to the length of time it has been growing. The keys to early detection are patient self-examination of the skin, full-body skin screenings by a dermatologist, and patient engagement. This work aims to categorize skin cancer into two types: malignant and benign. Two different approaches were used. Starting with a simple Convolutional Neural Network and then moving on to transfer learning. In our experiment, we were able to attain a classification accuracy of 82 percent.

A new compact four-port multi-input-multi-output (MIMO) dielectric resonator (DR) antenna is proposed with pattern diversity. The antenna contains four DR elements, the epsilon-shaped and cylindrical. These four DR elements are placed together such that they resonate at the same frequency. The specific geometry of the DR elements allows to place them at separation which is much smaller than operating wavelength. Hence, the combination of the four DR elements acts as a single DR. This helps in maintaining the compactness of the antenna structure. The antenna operates with the hybrid of modes HEM 11δ and TM 01δ excited in epsilon-shaped and cylindrical DR elements, respectively. The excitation of these orthogonal modes helps in obtaining the pattern diversity allowing the antenna for multi-directional coverage. The antenna provides 15.73% overlapping 10 dB impedance bandwidth with the variation of gain within ranges 4-6 and 6-6.5 dBi at port 1/2 and port 3/4, respectively. In addition, the antenna provides the high radiation efficiency >93% at port 1/2 and 97% at port 3/4. Other parameters required for MIMO applications such as envelope correlation coefficient and diversity gain are within the acceptable limits.

Food waste is something that occurs at every stage of the process ranging from the moment it is grown to the time it reaches the consumer. One-third of food produced globally is wasted and that equates to 1.3 billion tonnes. The food that is present in the supermarkets goes through an extensive production process to get on the shelves. During this process, a wide number of resources are used, all of which can have their own impact on the environment. Food waste is also a major global problem because of the number of people who are suffering from starvation in the world. Around 821 million people do not have access to food in the same way as people in developed countries. This means that 1 in 9 people in the world are starving or are malnourished. Considering the magnitude of social, environmental, and economic problems caused by food wastage there arises an urgent need to find ways to tackle this colossal issue. Digital technology comes as an efficient tool as they are advancing at a rapid rate they also re-establish and recast the relationships between the customer, workers, and employers as the silicon chips permeates almost everything that is done on the industrial scale. This digital global world will contribute for new systems regarding this move. Many digital applications are proving to be a saviour for reducing significant amount of food waste and helping to provide that food to the needy. They collect food waste and save tonnes of food to reduce the quantity of food waste and lower the malnourishment, globally. This chapter aims to highlight the various recent technological advancements which are providing sustainable food waste management processes.

Osmotic dehydration of apple slices by using dry sugar method was done along with the use of various additives for enhancing the flavor. The degree Brix was checked and maintained for 3 consecutive days. After 3 days, the osmotically pre-treated apple slices were further dehydrated in a cabinet dryer at 60 °C and were analyzed with sensory score texture, flavor, color, and overall acceptability.

Ready to use instant custard powder (ICP) was prepared by mixing potato starch and flour with powdered milk, sugar and dry fruits (cashews, almonds and resins). A total of four flavours, viz. butterscotch, cardamom, vanilla and orange, were tried, out of which orange was inferred to be the best. Grade D potatoes were procured from the market. The potato starch was prepared by crushing the potato slices in excess of water to make slurry which was sieved so that the settled starch can be obtained which was washed and dried in oven at 45° centigrade and ground into fine powder. The potato flour was prepared by drying the slices of potatoes in oven to bring down its moisture level to 3–4%. This was followed by the grinding of the dried slices. The custard was prepared by mixing the potato starch and potato flour in a given amount of milk and hot water, and dry fruits were added. The sensory evaluation was done with a panel of 20 members, and orange flavour was judged the best of all the four samples. The cost analysis was done, and it came out to be Rs 7/per packet.

With continuous growth of web applications around the globe, it is a challenge to find the suitable information needed for the user in a limited time.Number of handheld mobile devices is increasing and most of the business revolves around the correct search of the data. Without a proper recommender system it is very difficult to get required information from the web applications. Web applications use recommender systems to provide suitable data to users based on their choices and interests. For different kinds of needs different types of recommender systems have been proposed. Two most basic types of recommender systems are collaborative filtering recommender system and content based recommender system. Sometimes these two recommender systems are combined to increase the efficiency of a recommender system The generated new recommender system is known as hybrid recommender system.The purpose of this paper is to help readers understand the basics of recommender systems. This paper identifies key areas of research openly available for new researchers. After reading this paper new researchers can understand basic problems of recommender systems which need improvement and hence they can make those problems their area of research.

Recommendation System is the base of E-commerce business in India and World. After The advent of 4G technology in developed and developing countries people are using internet more than ever. Lot of options are available for almost everything on Internet. People are confused with all the options. Recommendation system makes this easier by giving users options on the basis of history of the user in the system. Now you can get choices on the basis of your likes and dislikes. But this recommendation system fails when we have no information about the user and item. In simple words because we do not have user history therefore we cannot use recommendation algorithm. In this paper we are suggesting a MBA (market basket Analysis) technique to help us solve this problem to some level. We are using data available by Amazon to develop and test our method.

This paper suggests use of sentiment analysis classification as an effective method for examining textual data coming from variety of resources on internet. Sentiment analysis is a method of data mining that evaluates textual data consuming machine learning techniques. Due to tremendous expanse of opinions of users, their reviews, feedbacks and suggestions available over the web resources, it is so much indispensable to discover, analyze and consolidate their views for enhanced decision making. Sentiment analysis presents an effective and efficient opinion of consumers in real time which can greatly affect the decision making process for business domain. We have seen an increment in level of activity during last ten year period and emphases on exploratory research approaches. We noticed that several procedures are inattentive from the pond of Business Intelligence research. We also recognized potential zones that requisite additional exploration.

Recommendation is a very important part of our digital lives. Without recommendation one can get lost in web of data. Movies are also very important form of entertainment. We watch most movies that are recommended by someone or others. Each person likes specific type of movies. So movie recommendation system can increase sales of a movie rent/sales shop. Websites like Netflix are using it. But there is one problem that can cause recommendation system to fail. This problem is sparsity problem. In this paper, we have used a new approach that can solve sparsity problem to a great extent.