Clinical applications of Artificial Intelligence (AI) for mental health care have experienced a meteoric rise in the past few years. AI-enabled chatbot software and applications have been administering significant medical treatments that were previously only available from experienced and competent healthcare professionals. Such initiatives, which range from “virtual psychiatrists” to “social robots” in mental health, strive to improve nursing performance and cost management, as well as meeting the mental health needs of vulnerable and underserved populations. Nevertheless, there is still a substantial gap between recent progress in AI mental health and the widespread use of these solutions by healthcare practitioners in clinical settings. Furthermore, treatments are frequently developed without clear ethical concerns. While AI-enabled solutions show promise in the realm of mental health, further research is needed to address the ethical and social aspects of these technologies, as well as to establish efficient research and medical practices in this innovative sector. Moreover, the current relevant literature still lacks a formal and objective review that specifically focuses on research questions from both developers and psychiatrists in AI-enabled chatbot-psychologists development. Taking into account all the problems outlined in this study, we conducted a systematic review of AI-enabled chatbots in mental healthcare that could cover some issues concerning psychotherapy and artificial intelligence. In this systematic review, we put five research questions related to technologies in chatbot development, psychological disorders that can be treated by using chatbots, types of therapies that are enabled in chatbots, machine learning models and techniques in chatbot psychologists, as well as ethical challenges.

Because there is more demand for freshwater around the world and the world’s population is growing at the same time, there is a severe lack of freshwater resources in the central part of the planet. The world’s current population of 7.2 billion people is expected to grow to over 9 billion by the year 2050. The vast majority of freshwater is used for things like cooking, cleaning, and farming. Most industrialised countries are in desperate need of smart irrigation systems, which are now a must-have because of how quickly technology is improving. In article presents IoT based Sensor integrated intelligent irrigation system for agriculture industry. IoT based humidity and soil sensors are used to collect soil related data. This data is stored in a centralized cloud. Features are selected by CFS algorithm. This will help in discarding irrelevant data. Clustering of data is performed by K means algorithm. This will help in keeping similar data together. Then classification model is build using the SVM, Random Forest and Naïve Bayes algorithm. Model is trained, validated and tested using the acquired data. Historical soil and humidity related data is also used in training the model. K-means SVM hybrid classifier is achieving better results for classification, prediction of water demand and saving fresh water by intelligent irrigation. K-means SVM hybrid classifier has achieved accuracy rate of 98.5 percent. Specificity, recall and precision of K-means SVM hybrid classifier is also higher than random forest and naïve bayes classifier.

Major depressive disorder (MDD) is a mood state that is not usually associated with vision problems. Recent research has found that the inhibitory neurotransmitter GABA levels in the occipital brain have dropped. Aim. The aim of the research is to evaluate mental workload by single channel electroencephalogram (EEG) approach through visual-motor activity and comparison of parameter among depressive disorder patient and in control group. Method. Two tests of a visual-motor task similar to reflect drawings were performed in this study to compare the visual information processing of patients with depression to that of a placebo group. The current study looks into the accuracy of monitoring cognitive burden with single-channel portable EEG equipment. Results. The alteration of frontal brain movement in reaction to fluctuations in cognitive burden stages generated through various vasomotor function was examined. By applying a computerised oculomotor activity analogous to reflector image diagram, we found that the complexity of the path to be drawn was more important than the real time required accomplishing the job in determining perceived difficulty in depressive disorder patients. The overall perceived difficulty of the exercise is positively linked with EEG activity measured from the motor cortex region at the start of every experiment test. The average rating for task completion for depression patients and in control group observed and no statistical significance association reported between rating scale and time spent on each trial ( ) for control group while the normalised perceived difficulty rating had 0.512, 0.623, and 0.821 correlations with the length of the pathway, the integer of inclination in the pathway, and the time spent to complete every experiment test, respectively ( ) among depression patients. The findings imply that alterations in comparative cognitive burden levels during an oculomotor activity considerably modify frontal EEG spectrum. Conclusion. Patients with depression perceived the optical illusion in the arrays as weaker, resulting in a little bigger disparity than individuals who were not diagnosed with depression. This discovery provided light on the prospect of adopting a user-friendly mobile EEG technology to assess mental workload in everyday life.

In this paper, we propose a skeleton-based method to identify violence and aggressive behavior. The approach does not necessitate high-processing equipment and it can be quickly implemented. Our approach consists of two phases: feature extraction from image sequences to assess a human posture, followed by activity classification applying a neural network to identify whether the frames include aggressive situations and violence. A video violence dataset of 400 min comprising a single person's activities and 20 h of video data including physical violence and aggressive acts, and 13 classifications for distinguishing aggressor and victim behavior were generated. Finally, the proposed method was trained and tested using the collected dataset. The results indicate the accuracy of 97% was achieved in identifying aggressive conduct in video sequences. Furthermore, the obtained results show that the proposed method can detect aggressive behavior and violence in a short period of time and is accessible for real-world applications.

Diseases of the cardiovascular system are one of the major causes of death worldwide. These diseases could be quickly detected by changes in the sound created by the action of the heart. This dynamic auscultations need extensive professional knowledge and emphasis on listening skills. There is also an unmet requirement for a compact cardiac condition early warning device. In this paper, we propose a prototype of a digital stethoscopic system for the diagnosis of cardiac abnormalities in real time using machine learning methods. This system consists of three subsystems that interact with each other (1) a portable digital subsystem of an electronic stethoscope, (2) a decision-making subsystem, and (3) a subsystem for displaying and visualizing the results in an understandable form. The electronic stethoscope captures the patient’s phonocardiographic sounds, filters and digitizes them, and then sends the resulting phonocardiographic sounds to the decision-making system. The decision-making system classifies sounds into normal and abnormal using machine learning techniques, and as a result identifies abnormal heart sounds. The display and visualization subsystem demonstrates the results obtained in an understandable way not only for medical staff, but also for patients and recommends further actions to patients. As a result of the study, we obtained an electronic stethoscope that can diagnose cardiac abnormalities with an accuracy of more than 90%. More accurately, the proposed stethoscope can identify normal heart sounds with 93.5% accuracy, abnormal heart sounds with 93.25% accuracy. Moreover, speed is the key benefit of the proposed stethoscope as 15 s is adequate for examination.

According to the benefits in safeguarding and transferring medical information, illness assessment, evaluation of “Magnetic Resonance Mapping” images, and certain other disciplines, blockchain and machine learning (ML) technology has significantly piqued attention in the healthcare domains. Formerly, those chores have been performed out along with individuals; eventually, individuals acquired attraction because to its precision and efficiency. The proposed study will examine the activities and possible capabilities of learning algorithms and blockchain in the healthcare professions focusing on these fascinating facts. Primary and secondary data analysis has been executed, with primary analysis method consisting of a survey of 150 randomly picked medicine professionals with expertise in machine learning and blockchain. They gave their answers that were being subsequently transferred to figures and employed as response variable in SPSS examining. The length of time where learning and blockchain have been used in medicine is really the independent factor. To better understand the primary and small hurdles of integrating machine learning and blockchain, a correlation investigation was done. Thereafter, secondary methodology is employed to validate the primary study results.