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Depression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.

(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.

1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.

A novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.

One week since the first meeting, Mr. Deepak Kumar, Chairman and Group CEO of MakeMyTrip (MMT), called a second meeting of the homepage website redesign team for redesigning the Home Page (see Exhibit 1 for the current Home Page)¹. This team comprised of the CBO, Analytics Head, User Experience (UX) Head, Product Head and the Tech Head (CTO). The second meeting was called to discuss and finalise the options of the Home Page design to be tested, the criteria to be used for choosing the best option, and the testing methodology.

The transformative impact of digital innovations on education has reshaped academic landscapes, affecting both instructional methods and evaluation systems. This study delves into the realm of distance education, exploring the intricate dynamics of digital access, equity and inclusivity, with a particular focus on their influence on learning outcomes.

A comprehensive survey involving 360 participants was conducted to gather data on various facets of the digital learning environment. Descriptive statistics illuminated participants' perceptions, while digital access disparities were analyzed through a chi-square test. Structural equation modelling (SEM) assessed direct relationships, mediation and moderation analyses and elucidated the nuanced influence of demographic parameters.

Descriptive analysis revealed generally positive perceptions of digital learning, with some variability in aspects like accessibility of learning materials and Internet connectivity. Digital access disparities were evident across demographic parameters, highlighting significant associations with gender, education, income, geographic location, religious affiliation and field of study. SEM indicated robust positive associations between digital access and learning outcomes, strategies for enhancing equity and inclusivity and their combined impact on overall academic success.

This study contributes original insights by comprehensively analyzing the interplay of digital access, equity and inclusivity in distance education and their impact on learning outcomes. The research unveils nuanced disparities across demographic dimensions, emphasizing the need for targeted interventions. The study’s integration of SEM, mediation and moderation analyses adds a sophisticated layer to the understanding of these relationships.

In early 2013, Mr. Deepak Kumar, Chairman and Group CEO of MakeMyTrip1 (MMT), summoned a meeting to discuss about redesigning the MMT Homepage2. It was a three-hour meeting at the Hindustan Conference Room at the company's head office in Gurgaon, India. Theis meeting was attended by key members of the homepage website redesign team comprising the CBO, Analytics head, User Experience (UX) head, Product head and the Tech head (CTO). As a pre-read for the meeting, Ajay Singh, Product Head, shared his findings3 that could help in defining the issues faced by visitors to the homepage of MMT.

In mid-2013 Deepak Kumar, Chairman and Group CEO of MakeMyTrip (MMT), was headed to the Hindustan conference room of the company's head office in Gurgaon, India, for deciding the new homepage design. A day earlier, Ajay, Product Head, had shared the consolidated results of the trials of different options (see Exhibit 1 for the results of testing the options) with the homepage website redesign team, responsible for the entire project. The team consisted of the head of business (CBO), the Analytics Head, the user experience (UX)head, the Product Head and the Tech Head (CTO).

Looking at the results, Deepak Kumar reflected that there were a lot of surprises in how the different options had performed. While the tracking tools in online businesses had a huge advantage over traditional businesses in that almost every customer interaction could be measured, there was still a lot of reading between the numbers that had to be done. Deepak was looking forward to a fruitful discussion to evaluate the options and finalise the new homepage design to be rolled out. As he opened the door to the conference room, he heard an excited chatter.