AISSMS Institute of Information Technology

Background ML-powered Internet of Medical Things (MLIoMT) is a burgeoning framework poised to transform healthcare, particularly in the timely identification of heart disease. Objectives This article proposes an innovative MLIoMT structure aimed at leveraging machine learning (ML) algorithms for heart disease detection. Materials and Methods Through the integration of wearable sensors, mobile applications, cloud computing, and advanced ML techniques, MLIoMT enables continuous monitoring of vital signs and cardiac health indicators in real time. By analyzing this data stream, abnormalities indicative of heart disease can be detected early, facilitating timely intervention and personalized healthcare recommendations. The MLIoMT framework employs diverse ML methods, such as deep learning and ensemble techniques to enhance the accuracy and reliability of heart disease prediction models. Results The proposed structure holds promise for revolutionizing preventive healthcare, enabling proactive management of cardiac health, and ultimately reducing the burden of heart disease. Results in terms of accuracy, precision, recall and F1 score show that the proposed system has better performance and efficiency. Conclusion Overall, MLIoMT represents a significant advancement in healthcare technology, with the potential to improve patient outcomes and enhance overall quality of life.

Alzheimer's disease (AD) is one of the most common and fastest growing neurodegenerative diseases in the western countries. Development of different biomarkers tools are key issues for the diagnosis of AD and its progression. Prediction of cognitive performance of subjects from electroencephalography (EEG) and identification of relevant biomarkers are some of the research problems. Although EEG is a powerful and relatively cheap tool for the diagnosis of AD and dementia, it does not achieve the standards of clinical performance in terms of sensitivity and specificity to accept as a reliable technique for the screening of AD. Hence, there is an immense need to develop an efficient system and algorithms for diagnosis. Accordingly, the objective of this research paper is to analyze different features for the diagnosis of AD to serve as a possible biomarker to distinguish between AD subject and normal subject. The research is carried out on an experimental database. Three different features such as spectral-, wavelet-, and complexity-based features are computed and compared on the basis of classification accuracy obtained. The classification is carried out using support vector machine classifier giving 96% accuracy on complexity-based features and increased performance in terms of sensitivity and specificity. The results show the improved performance in the diagnosis of AD. It is observed that the severity of AD is depicted in EEG complexity. These features used in research work can be considered as the benchmark for AD diagnosis.

In the telecom industry, large-scale of data is generated on daily basis by an enormous amount of customer base. Here, getting a new customer base is costlier than holding the current customers where churn is the process of customers switching from one firm to another in a given stipulated time. Telecom management and analysts are finding the explanations behind customers leaving subscriptions and behavior activities of the holding churn customers’ data. This system uses classification techniques to find out the leave subscriptions and collects the reasons behind the leave subscription of customers in the telecom industry. The major goal of this system is to analyze the diversified machine learning algorithms which are required to develop customer churn prediction models and identify churn reasons in order to give them with retention strategies and plans. In this system, leave subscriptions collects customers' data by applying classification algorithms such as Random Forest (RF), machine learning techniques such as KNN and decision tree Classifier. It offers an efficient business model that analyzes customer churn data and gives accurate predictions of churn customers so that business management may take action within the churn period to stop churn as well as loss in profit. System achieves an accuracy of 99 % using the random forest classifier for churn predicts, the classifier matrix has achieved a precision of 99 % with a recall factor of 99 % alongwith received overall accuracy of 99.09 %. Likewise, our research work improves churn prediction, scope other business fields, and provide prediction models to hold their existing customers customer service, and avoid churn effectively.

Smart sensing devices enabled hydroponics, a concept of vertical farming that involves soilless technology that increases green area. Although the cultivation medium is water, hydroponic cultivation uses 13 ± 10 times less water and gives 10 ± 5 times better quality products compared with those obtained through the substrate cultivation medium. The use of smart sensing devices helps in continuous real-time monitoring of the nutrient requirements and the environmental conditions required by the crop selected for cultivation. This, in turn, helps in enhanced year-round agricultural production. In this study, lettuce, a leafy crop, is cultivated with the Nutrient Film Technique (NFT) setup of hydroponics, and the growth results are compared with cultivation in a substrate medium. The leaf growth was analyzed in terms of cultivation cycle, leaf length, leaf perimeter, and leaf count in both cultivation methods, where hydroponics outperformed substrate cultivation. The results of the 'AquaCrop simulator also showed similar results, not only qualitatively and quantitatively, but also in terms of sustainable growth and year-round production. The energy consumption of both the cultivation methods is compared, and it is found that hydroponics consumes 70 ± 11 times more energy compared to substrate cultivation. Finally, it is concluded that smart sensing devices form the backbone of precision agriculture, thereby multiplying crop yield by real-time monitoring of the agronomical variables.

Increasingly digital technology in the present helped many people lives. Unlike the electoral system, there are many conventional uses of paper in its implementation. The aspect of security and transparency is a threat from still widespread election with the conventional system (offline). General elections still use a centralized system, there is one organization that manages it. Some of the problems that can occur in traditional electoral systems is with an organization that has full control over the database and system, it is possible to tamper with the database of considerable opportunities. Blockchain technology is one of solutions, because it embraces a decentralized system and the entire database are owned by many users. Blockchain itself has been used in the Bitcoin system known as the decentralized Bank system. By adopting blockchain in the distribution of databases on e-voting systems can reduce one of the cheating sources of database manipulation. This research discusses the recording of voting result using blockchain algorithm from every place of election. Unlike Bitcoin with its Proof of Work, this thesis proposed a method based on a predetermined turn on the system for each node in the built of blockchain

Background Machine Learning-powered Internet of Medical Things (MLIoMT) is a burgeoning framework poised to transform healthcare, particularly in the timely identification of heart disease. Purpose This article proposes an innovative MLIoMT structure aimed at leveraging machine learning (ML) algorithms for heart disease detection. Methods Through the integration of wearable sensors, mobile applications, cloud computing, and advanced ML techniques, MLIoMT enables continuous monitoring of vital signs and cardiac health indicators in real time. By analyzing this data stream, abnormalities indicative of heart disease can be detected early, facilitating timely intervention and personalized healthcare recommendations. The MLIoMT framework employs diverse ML methods such as deep learning and ensemble techniques to enhance the accuracy and reliability of heart disease prediction models. Results The proposed structure holds promise for revolutionizing preventive healthcare, enabling proactive management of cardiac health and ultimately reducing the burden of heart disease. Results in terms of accuracy, precision, recall and F1 score show that the proposed system has better performance and efficiency. Conclusion Overall, MLIoMT represents a significant advancement in healthcare technology, with the potential to improve patient outcomes and enhance overall quality of life.

In recent years, Agriculture sector has been researched a lot with the advancements in technologies like machine learning and smart computing. With the dynamic economics of Agri-produce, it is becoming challenging for farmers to utilize the land efficiently to get maximum profit in the specific landscape. Crop Yield Prediction (CYP) is crucial and is greatly dependent on environmental factors like soil contents, humidity, rainfall as well as area under cultivation and other required metrics. Due to insufficient incorporation of the multiple environmental circumstances, a number of existing tools and techniques used for CYP, such as historical averages, tend to produce inaccurate findings. In such situation, with multiple options of crop, it is essential for farmers to plan the crop strategy in advance. If the farmer can get estimate of the crop yield in advance, cultivation can be done accordingly. To solve this problem, machine learning approach is implemented as a base for accurate predictions. Crop prediction is done by classification model and yield prediction uses regression models to learn from the data. Multiple ML models are analyzed based on performance metrics. Best performer model is incorporated in backend. Among the used models for yield prediction, Random Forest Regression gives best results with MAE of 0.64 and R2 score of 0.96. For crop prediction, Naïve Bayes classifier gives most accurate results with accuracy of 99.39. The study emphasizes how machine learning could revolutionize crop management techniques by giving farmers insights about optimizing resource allocation and boost overall crop yield.

Objective: This study proposes a model for building the network intrusion detection system using a machine learning algorithm called decision tree. This system detects primarily an anomaly based intrusion. Methods: In this model, the categorical features from the dataset Change Control IDentifiers (CCIDS) 2017 are encoded using label encoder. Using Recursive-Feature-Elimination (RFE) some best features is selected. This data is then divided into training and testing data. Training data is then used to form a Decision-Tree-Model wherein each leaf signifies the possible outcome. Findings: Classification models are developed making use of the training data to classify the test data as malicious or benign. Measuring the accuracy of the classifier on future data rather than the past data is of a paramount aspect. The observed accuracy of the classifier on test data is 99%. The precision of the proposed system indicates that the True-Positive-Rate (TPR) is 99.9% and the False-Positive-Rate (FPR) is 0.1%. The proposed model uses the latest data set for training data and test data compared to the traditional systems which have been modeled using KDD-CUP-99 data set. Moreover, unlike other systems, it does not use any data-mining tool like Weka. This work provides as basis for any new algorithm using dataset CCIDS 2017. Improvements: The work can be extended to exploit the big data available for attacks and intrusions using big data analytics. Keywords: Accuracy, Detection, Decision Tree, Intrusion, Machine Learning

Potato is one of the most important crops worldwide, and its productivity can be affected by various diseases, including leaf diseases. Early detection and accurate diagnosis of leaf diseases can help prevent their spread and minimize crop losses. In recent years, Convolutional Neural Networks (CNNs) have shown great potential in image classification tasks, including disease detection in plants. In this study, we propose a CNN-based approach for the prediction of potato leaf diseases. The proposed method uses a pre-trained CNN model, which is fine-tuned on a dataset of potato leaf images. The dataset includes images of healthy leaves and leaves infected with different diseases such as early blight and late blight. The trained model is then used to classify new images of potato leaves into healthy or diseased categories. The proposed approach achieves 97.4% accuracy in the classification of potato leaf diseases such as early blight potato leaf disease and late blight potato leaf disease, and can be used as an effective tool for early detection and management of these diseases in potato crops.

In this paper, we have proposed an automated medical system for detecting type 2 diabetes from exhaled breath. Human breath can be used as a diagnostic sample for detecting many diseases as it contains many gases that are dissolved in the blood. Breath-based analysis stands out among the different non-invasive ways of detection as it provides more accurate predictions and offers many advantages. In this work, the concentration of acetone in the exhaled breath is analysed to detect type 2 diabetes. A new sensing module consisting of an array of sensors is implemented for monitoring the acetone concentration to detect the disease. Deep learning algorithms like Convolutional Neural Networks (CNN) are normally used to automatically analyse medical data to make predictions. Even though the CNN performs well, a few modifications to the network layout can further improve the classification accuracy of the learning model. To analyse the sensor signals to generate predictions, a new deep hybrid Correlational Neural Network (CORNN) is designed and implemented in this research. The proposed detection approach and deep learning algorithm offer improved accuracy when compared to other non-invasive techniques.

In many parts of the world, heart disease is the leading cause of mortality diagnosis is critical Towards Efficient Medical Care and prevention of heart attacks and other cardiac events. Deep learning algorithms have shown promise in accurately predicting heart disease based on medical data, including electrocardiograms (ECGs) and other health metrics. With this abstract, Specifically, we advocate for deep learning algorithm in accordance with CNNs for Deep Learning effective heart disease prediction. The proposed method uses a combination of ECG signals, demographic data, and clinical measurements Identifying risk factors for cardiovascular disease in patients. The proposed CNN-based model includes several layers, such as convolutional ones, pooling ones, and fully connected ones. The model takes input in the form of ECG signals, along with demographic data and clinical measurements, and uses convolutional layers to get features out of raw data. To lessen the effect of this, pooling layers are dimensionality of the extracted features, while layers that are already completely linked to estimate the risk of cardiovascular disease based on the extracted features. Training and evaluating the suggested model, We consulted a broad pool of ECG signals together with patient clinical data, both with and without heart disease. Training and test sets were created from the dataset testing arrays, and the prototype was trained using backpropagation and stochastic gradient descent. The model was evaluated using standard quantitative indicators such the F1 score, recall rate, and accuracy rate. The outcomes of experiments demonstrate the suggested CNN-based model achieves high accuracy in predicting heart disease, with an overall accuracy of over 90%. The model also outperforms several alternatives to classical techniques for heart disease prediction, including the more conventional forms of AI algorithms different forms of deep learning models. In conclusion, the proposed deep learning algorithm based on CNNs shows great potential for effective heart disease prediction. The model can be integrated into healthcare systems to provide accurate and timely diagnosis and treatment for patients with heart disease. Further research can be done to optimize the model's performance and test its effectiveness on different patient populations.

In this digital era, a tremendous amount of information gets generated every day. The generated information is used for multiple purposes such as scientific/medical research, news generation, blogs, etc. Reading and gathering the important information and summarizing it manually can become a tedious task which is also prone to manual errors. Therefore, more time is required to read that information, and also unwanted information gets mixed up with important information. It is difficult for a person to manually summarize a large document. Also, there is an issue with finding necessary documents and absorbing relevant information from them. In this proposed system, we are implementing a combination of TFIDF and Textrank algorithm with some NLP methods which will efficiently summarize the given data and will perform better than the other systems.

Campus placement is an activity of participating, identifying and hiring young talent for internships and entry level positions. Reputation and yearly admissions of the institute invariably depend upon the placements provided by the institute to the students. Therefore, most of the institutions, assiduously, try to boost their placement department in order to improve their organization on a full scale. Any assistance during this specific space can have a good impact on the institute's capability to position it's students. In this study, the target is to analyze student's placement data of last year and use it to determine the probability of campus placement of the present students. For this we have experimented with four different machine learning algorithms i.e. Logistic Regression, Decision Tree, K Nearest Neighbours and Random Forest.

Abstract The facial and physiological sensor-based emotion recognition methods are two popular methods of emotion recognition. The proposed research is the first of its kind in real-time emotion recognition that combines skin conductance signals with the visual-based facial emotion recognition (FER) method on a Raspberry Pi. This research includes stepwise documentation of method for automatic real-time face detection and FER on portable hardware. Further, the proposed work comprises experimentation related to video induction and habituation methods with FER and the galvanic skin response (GSR) method. The GSR data are recorded as skin conductance and represent the subject's behavioral changes in the form of emotional arousal and face emotion recognition on the portable device. The article provides a stepwise implementation of the following methods: (a) the skin conductance representation from the GSR sensor for arousal; (b) gathering visual inputs for identifying the human face; (c) FER from the camera module; and (d) experimentation on the proposed framework. The key feature of this article is the comprehensive documentation of stepwise implementation and experimentation, including video induction and habituation experimentation. An illuminating aspect of the proposed method is the survey of GSR trademarks and the conduct of psychological experiments. This study is useful for emotional computing systems and potential applications like lie detectors and human–machine interfaces, devices for gathering user experience input, identifying intruders, and providing portable and scalable devices for experimentation. We termed our approaches "sensovisual" (sensors + visual) and "Emosense" (emotion sensing).

Airway obstruction is a common component in Chronic Obstructive Pulmonary Disease (COPD). Detection of obstruction and its grading is very essential. Obstruction in the airways, forces the accessory muscles like sternomastoid muscle (SMM) of respiration to work. Normally, only essential muscles of respiration work. In the said paper electromyographic (EMG) analysis of SMM is done for COPD and Normal subjects. We have developed improved slope based onset detection algorithm to detect the onset and offset timing of EMG. Time domain features are extracted for COPD and normal subject. The onset detection algorithm reduces the number of computations by 32.96% and increases accuracy of feature calculation by 40.19%. Dominant time domain features are selected and applied to Support Vector Machine Classifier. The SVM classification algorithm is compared with Threshold and Naïve Bayes classification algorithm. SVM gives the highest accuracy of 87.80%, sensitivity of 89.65% and specificity of 83.33%. Results are also compared with previously used FEV1/FEV6 and Forced Oscillation Technique. The activity of SMM has a significant role in the classification of Normal and COPD subject. Further analysis of SMM can be done to find different grades of COPD.

Malaria is a vector borne disease widely occurring at equatorial region. Even after decades of campaigning of malaria control, still today it is high mortality causing disease due to improper and late diagnosis. To prevent number of people getting affected by malaria, the diagnosis should be in early stage and accurate. This paper presents an automatic method for diagnosis of malaria parasite in the blood images. Image processing techniques are used for diagnosis of malaria parasite and to detect their stages. The diagnosis of parasite stages is done using features like statistical features and textural features of malaria parasite in blood images. This paper gives a comparison of the textural based features individually used and used in group together. The comparison is made by considering the accuracy, sensitivity, and specificity of the features for the same images in database.

The term “Liver disease” refers to a broad category of disorders affecting the liver. There are a variety of common liver ailments, such as hepatitis, cirrhosis, and liver cancer. Accurate and early diagnosis is an emergent demand for the prediction and diagnosis of liver disease. Conventional diagnostic techniques, such as radiological, CT scan, and liver function tests, are often time-consuming and prone to inaccuracies in several cases. An application of machine learning (ML) and deep learning (DL) techniques is an efficient approach to diagnosing diseases in a wide range of medical fields. This type of machine-related learning can handle various tasks, such as image recognition, analysis, and classification, because it helps train large datasets and learns to identify patterns that might not be perceived by humans. This paper is presented here with an evaluation of the performance of various DL models on the estimation and subtyping of liver ailment and prognosis. In this manuscript, we propose a novel approach, termed CNN+LSTM, which is an integration of convolutional neural network (CNN) and long short-term memory (LSTM) networks. The results of the study prove that ML and DL can be used to improve the diagnosis and prognosis of liver disease. The CNN+LSTM model achieves a better accuracy of 98.73% compared to other models such as CNN, Recurrent Neural Network (RNN), and LSTM. The incorporation of the proposed CNN+LSTM model has better results in terms of accuracy (98.73%), precision (99%), recall (98%), F1 score (98%), and AUC (Area Under the Curve)-ROC (Receiver Operating Characteristic) (99%), respectively. The use of the CNN+LSTM model shows robustness in predicting the liver ailment with an accurate diagnosis and prognosis.

Nowadays, digital information is very easy to process, but it allows unauthorized users to access to this information. To protect this information from unauthorized access, cryptography is one of the most powerful and commonly used techniques. There are various cryptographic algorithms out of which advanced encryption standard (AES) is one of the most frequently used symmetric key cryptographic algorithms. The main objective of this chapter is to implement fast, secure, and areaefficient AES algorithm on a reconfigurable platform. In this chapter, AES algorithm is designed using Xilinx system generator, implemented on Nexys-4 DDR FPGA development board and simulated using MATLAB Simulink. Synthesis results show that the implementation consumes 121 slice registers, and its maximum operating frequency is 1102.536 MHz. Throughput achieved by this implementation is 14.1125 Gbps.

For century's agriculture has been based on traditional methods, and when modern technology was introduced, it was only in modernizing the traditional equipment. But the scope for monitoring, automation and data analysis based on real time data acquisition from farms has not been much explored. When we explore this field it will also open up the scope for Internet of Things based automation in agriculture field. The main aim of this research paper will be designing and implementing smart automation system for farms alongside constantly analyzing and reporting real time data from the field. In this paper explain about the prototype design of microcontroller based intelligent irrigation system controller which will allow irrigation to take place from remote places where manual inspection is not needed. According to pH value of soil, a list of best suited crop is selected from all crops. Values of monitoring parameters are adjusted according to optimal condition required for particular crop.

The power electronics (PE) industry is expected to play a significant role in the development of energy conservation and global industrialization trends of the 21st century. Due to the technological advancements that have occurred in the field, such as transportation and communication, the need for efficient and quality products is becoming more prevalent. The importance of power electronics is acknowledged in the automated industries that are constantly striving to improve their efficiency and effectiveness. Due to the increasing global energy consumption, the need for more energy-efficient technologies is also becoming more prevalent. Around 87% of our energy is derived from fossil fuels, while 6% is generated from nuclear power plants and 7% from renewable sources. Due to the increasing concerns about the environment and safety issues associated with nuclear plants and fossil fuels, the need for energy conservation is becoming more prevalent. This is also expected to be achieved through the development of power electronics. In the coming decades, the development of artificial intelligence (AI) tools, such as neural network, expert system, and fuzzy logic, is expected to bring a new era to the field of motion control and power electronics. Despite the technological advancements that have occurred in the field, these tools have not yet reached the power electronics sectors. In this paper, the AI tools and their applications in the field of power electronics and motion control are discussed.